+ Coarse-to-fine Image Alignment incorporates OpenCV (Coarse) and SimpleITK (Fine) tools to align images and spatial omics datasets across same or adjacent tissue sections. Users may interact with built-in mini shiny apps to change alignment parameters and validate alignment accuracy. +
+Spatially Aware Analysis allows detecting spatial patterns across cells, spots, molecules and other entities.
@@ -44,22 +48,22 @@Support for Big Data for VoltRon objects enables storing large feature data matrices and large microscopic images of tissues on disk without overloading memory, thus allowing analysis on large datasets with ease. VoltRon stores large images as pyramid structures to speed up visualization and data retrieval.
-Interoperability across R/Python frameworks allows users to convert VoltRon objects to a large number of objects used by other spatial omic platforms such as Seurat, Squidpy (AnnData), SpatialExperiment (BioConductor) and Giotto.
+Support for Larger-than-memory data enables storing large feature data matrices and large microscopic images of tissues on disk without overloading memory, thus allowing analysis on large datasets with ease. VoltRon stores large images as pyramid structures to speed up visualization and data retrieval.
diff --git a/docs/conversion.html b/docs/conversion.html
index 147243a0..d93a5be1 100644
--- a/docs/conversion.html
+++ b/docs/conversion.html
@@ -13,7 +13,7 @@
library(ComplexHeatmap)
+# install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
+ BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
+library(ComplexHeatmap)
+library(viridisLite)
+
marker_features <- unique(topmarkers$gene)
vrHeatmapPlot(Xen_data, features = marker_features, group.by = "Clusters",
- show_row_names = TRUE, font.size = 10)
+ show_row_names = TRUE, font.size = 10, col = viridis(100))
-With these files, we can use the **importQuPathIF** function to import the data as a VoltRon object. You can select the resolution of the channels using **series** and **resolution** parameters which will pick the desired level from the ome.tiff file.
+With these files, we can use the **importQuPathIF** function to import the data as a VoltRon object. You can select the resolution of the channels using **series** and **resolution** parameters which will pick the desired level from the ome.tiff file. The image file can also be any of pyramid image formats supported by BioFormats. See [here](https://bio-formats.readthedocs.io/en/v8.3.0/supported-formats.html) for supported pyramid image formats.
```{r eval = FALSE, class.source="watch-out"}
+if(!requireNamespace("sf"))
+ install.packages("sf")
+library(sf)
+
+# get segments
+segments <- read_sf("measurements.geojson", type = 3)
+segments <- st_cast(segments, "POLYGON")
+segments <- generateSegments(segments)
+
+# import data
adj_vr <- importQuPathIF(measurements = "measurements.txt",
- segments = "measurements.geojson",
+ segments = segments,
image = "if_exp.ome.tif",
channels = c("DAPI", "CD20", "CD21"),
series = 1,
@@ -716,7 +725,7 @@ More information on the Xenium and the study can also be found on the [bioRxiv p
```{r eval = FALSE, class.source="watch-out"}
Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif",
sample_name = "XeniumR1image",
- image_name = "H&E")
+ spatial = "H&E")
Xen_R1_image
```
@@ -782,7 +791,7 @@ globalMetadata:List of 1525
-Now, we can specify the `series` and `resolution` to select the image layer we want to import to VoltRon.
+Now, we can specify the `series` and `resolution` to select the image layer we want to import to VoltRon. The image file can also be any of pyramid image formats supported by BioFormats. See [here](https://bio-formats.readthedocs.io/en/v8.3.0/supported-formats.html) for supported pyramid image formats.
```{r eval = FALSE, class.source="watch-out"}
vrimagedata <- importImageData(ome.tiff, series = 1, resolution = 5)
diff --git a/docs/importingdata.html b/docs/importingdata.html
index 7ee40a2b..b8f31c0d 100644
--- a/docs/importingdata.html
+++ b/docs/importingdata.html
@@ -13,7 +13,7 @@
Vis_R1 <- importVisium(dir.path = "Visium/", sample_name = "VisiumR1",
- assay_name = "Visium_assay", image_name = "H&E_stain")
+ assay_name = "Visium_assay", spatial = "H&E_stain")
SampleMetadata(Vis_R1) Assay Layer Sample
Assay1 Visium_assay Section1 VisiumR1vr_dbit <- importDBITSeq(path.rna = "GSM4189615_0719cL.tsv",
path.prot = "GSM4202309_0719aL.tsv",
- size = 10, sample_name = "E10_Eye_2", image_name = "main")VoltRon Object
E10_Eye_2:
Layers: Section1
@@ -1145,7 +1146,7 @@ Custom VoltRon objects
main.assay = "MELC",
assay.type = "cell",
sample_name = "control_case_3",
- image_name = "DAPI")
+ spatial = "DAPI")
vr_objectVoltRon Object
control_case_3:
@@ -1164,15 +1165,15 @@ Custom VoltRon objects
main.assay = "MELC",
assay.type = "cell",
sample_name = "control_case_3",
- image_name = "MELC")These channels then can be interrogated and used as background images for spatial plots and spatial feature plots as well.
vrImageChannelNames(vr_object) Assay Layer Sample Spatial Channels
Assay1 MELC Section1 control_case_3 MELC DAPI,CD45You can extract each of these channels individually.
-vrImages(vr_object, name = "MELC", channel = "DAPI")
-vrImages(vr_object, name = "MELC", channel = "CD45")vrImages(vr_object, spatial = "MELC", channel = "DAPI")
+vrImages(vr_object, spatial = "MELC", channel = "CD45")
@@ -1210,9 +1211,22 @@ Multiplex IF (QuPath)function to import the data as a VoltRon object. You can select the resolution of the channels using series and resolution parameters which will pick the desired level -from the ome.tiff file. - |
In this use case, we will align immunofluorescence (IF) and H&E images of the Xenium In Situ and Visium CytAssist platforms @@ -882,8 +895,103 @@
In the next use case, we will align H&E images +associated with Visium data generated from tissue block sections of +adult humans with postmortem dorsolateral prefrontal cortex +(DLPFC). Two pairs of adjacent sections were obtained from the +tissue block of the third donor. Each pair are composed of two 10 \(\mu\)m serial tissue sections, and pairs +are located 300 \(\mu\)m apart from +each other. Hence, we align each pair individually. The datasets can be +downloaded from here.
+
@@ -817,9 +832,9 @@ We can also transfer the CD20, and CD21 channels to the registered coordinate
space of the Xenium data.
```{r eval = FALSE, class.source="watch-out"}
-vrImages(xen_tonsil[["Assay1"]], name = "main_reg", channel = "CD20") <-
+vrImages(xen_tonsil[["Assay1"]], spatial = "main_reg", channel = "CD20") <-
vrImages(if_tonsil, channel = "CD20")
-vrImages(xen_tonsil[["Assay1"]], name = "main_reg", channel = "CD21") <-
+vrImages(xen_tonsil[["Assay1"]], spatial = "main_reg", channel = "CD21") <-
vrImages(if_tonsil, channel = "CD21")
```
diff --git a/docs/multiomic.html b/docs/multiomic.html
index 3a41bd3d..1f7084b6 100644
--- a/docs/multiomic.html
+++ b/docs/multiomic.html
@@ -13,7 +13,7 @@
@@ -362,7 +365,8 @@ SRBlock <- getClusters(SRBlock, nclus = 7, method = "kmeans", label = "niche_clu
# visualize
vrSpatialPlot(SRBlock, group.by = "niche_clusters", alpha = 1, nrow = 2)
-vrHeatmapPlot(SRBlock, features = vrFeatures(SRBlock), group.by = "niche_clusters")
+vrHeatmapPlot(SRBlock, features = vrFeatures(SRBlock), group.by = "niche_clusters",
+ col = viridis(100))
```
@@ -475,10 +479,15 @@ We use **vrHeatmapPlot** to investigate the abundance of each cell type across t
# install packages if necessary
if(!requireNamespace("ComplexHeatmap"))
BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
+library(ComplexHeatmap)
+library(viridisLite)
# heatmap of niches
library(ComplexHeatmap)
-vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters")
+vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters",
+ col = viridis(100))
```
@@ -630,11 +639,15 @@ We use **vrHeatmapPlot** to investigate the abundance of each cell type across t
# install packages if necessary
if(!requireNamespace("ComplexHeatmap"))
BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
+library(ComplexHeatmap)
+library(viridisLite)
# heatmap of niches
library(ComplexHeatmap)
vrHeatmapPlot(vr_openst, features = vrFeatures(vr_openst),
- group.by = "niche_clusters")
+ group.by = "niche_clusters", col = viridis(100))
```
diff --git a/docs/nicheclustering.html b/docs/nicheclustering.html
index be1e146f..6b377ac5 100644
--- a/docs/nicheclustering.html
+++ b/docs/nicheclustering.html
@@ -13,7 +13,7 @@
# dependencies
+if(!requireNamespace("rhdf5"))
+ BiocManager::install("rhdf5")
+
+# import Visium data
+library(VoltRon)
+DLPFC_1 <- importVisium("DLPFC/151673", sample_name = "DLPFC_1")
+DLPFC_2 <- importVisium("DLPFC/151674", sample_name = "DLPFC_2")
+DLPFC_3 <- importVisium("DLPFC/151675", sample_name = "DLPFC_3")
+DLPFC_4 <- importVisium("DLPFC/151676", sample_name = "DLPFC_4")We will again use the registerSpatialData function to +automatically register two Visium assays (two H&E +images). This time, we will use the +BRUTE-FORCE method for automated alignment which we +found to be more accurate compared to FLANN when aligning two H&E +images. The shiny app also provides two tuning parameters that used by +the BRUTE-FORCE workflow:
+We will use 1000 features for this alignment, set +Match % to 20% of the features to be matched across +images. The quality of the alignment will be determined by the fine +tuning of these parameters where users will immediately observe the +alignment quality looking at the slideshow.
+DLPFC_list <- list(DLPFC_1, DLPFC_2)
+reg1and2 <- registerSpatialData(object_list = DLPFC_list)We can now apply a similar alignment across the second pair of +VoltRon objects. We will use 800 features for this +alignment, set Match % to 50% of the features to be +matched across images.
+DLPFC_list <- list(DLPFC_3, DLPFC_4)
+reg3and4 <- registerSpatialData(object_list = DLPFC_list)We can now combine all sections into one VoltRon object. There are +two pairs of serial tissue sections, but both pairs (thus 4 sections) +are from the same tissue block. Hence, we can combine these two lists +into one list and merge VoltRon objects even though sections were +aligned separately.
+merge_list <- c(reg1and2$registered_spat, reg3and4$registered_spat)
+SRBlock <- merge(merge_list[[1]], merge_list[-1], samples = "DLPFC_Block")
+SRBlockVoltRon Object
+DLPFC_Block:
+ Layers: Section1 Section2 Section3 Section4
+Assays: Visium(Main)
+Features: RNA(Main)Now that we have combined registered pairs of adjacent tissue +sections, we should define the adjacency of these pairs in the tissue +block.
+# two pairs of adjacent sections
+connectivity <- data.frame(
+ c(1,1,3,3),
+ c(1,2,3,4)
+)
+# adjacent sections are 10 micron apart, and pairs 300 micron
+zlocation <- c(0, 10, 300, 310)*params$tissue_lowres_scalef
+
+# add connectivity information to the block
+SRBlock <- addBlockConnectivity(SRBlock,
+ connectivity = connectivity,
+ zlocation = zlocation,
+ sample = "DLPFC_Block")The aligned and connected Visium assays of the DLPFC then could be +used for 3D spatial analysis, specifically as shown in the Niche Clustering tutorial.
+In this use case, we will align immunofluorescence (IF) of the Xenium In Situ platform to an H&E images generated from the same sections as the @@ -926,7 +1034,7 @@
vrImages(Xen_R1_image, scale.perc = 5)
We can use the registerSpatialData function to warp/align images across multiple VoltRon objects and define these @@ -949,7 +1057,7 @@
Xen_R1_image_reg <- xen_reg$registered_spat[[2]]
-vrImages(Xen_R1[["Assay1"]], channel = "H&E") <- vrImages(Xen_R1_image_reg, name = "main_reg", channel = "H&E")We can now observe the new channels (H&E) available for the Xenium assay using vrImageChannelNames.
vrImageChannelNames(Xen_R1)In the next use case, we will align H&E images -associated with Visium data generated from tissue block sections of -adult humans with postmortem dorsolateral prefrontal cortex -(DLPFC). Two pairs of adjacent sections were obtained from the -tissue block of the third donor. Each pair are composed of two 10 \(\mu\)m serial tissue sections, and pairs -are located 300 \(\mu\)m apart from -each other. Hence, we align each pair individually. The datasets can be -downloaded from here.
-# dependencies
-if(!requireNamespace("rhdf5"))
- BiocManager::install("rhdf5")
-
-# import Visium data
-library(VoltRon)
-DLPFC_1 <- importVisium("DLPFC/151673", sample_name = "DLPFC_1")
-DLPFC_2 <- importVisium("DLPFC/151674", sample_name = "DLPFC_2")
-DLPFC_3 <- importVisium("DLPFC/151675", sample_name = "DLPFC_3")
-DLPFC_4 <- importVisium("DLPFC/151676", sample_name = "DLPFC_4")We will again use the registerSpatialData function to -automatically register two Visium assays (two H&E -images). This time, we will use the -BRUTE-FORCE method for automated alignment which we -found to be more accurate compared to FLANN when aligning two H&E -images. The shiny app also provides two tuning parameters that used by -the BRUTE-FORCE workflow:
+In this use case, we will align DBiTplus data (a +novel DBIT-Seq protocol that preserves the tissue for multiplex protein +imaging) with multiplex immunofluorescence (mIF) images +generated by a modified CODEX PhenoCycler-Fusion protocol.
+More information on the spatial datasets and the study can be also be +found on the DBITplus +manuscript. You can also download DBIT-Seq and CODEX datasets +from:
We will use 1000 features for this alignment, set -Match % to 20% of the features to be matched across -images. The quality of the alignment will be determined by the fine -tuning of these parameters where users will immediately observe the -alignment quality looking at the slideshow.
-DLPFC_list <- list(DLPFC_1, DLPFC_2)
-reg1and2 <- registerSpatialData(object_list = DLPFC_list)We can now apply a similar alignment across the second pair of -VoltRon objects. We will use 800 features for this -alignment, set Match % to 50% of the features to be -matched across images.
-DLPFC_list <- list(DLPFC_3, DLPFC_4)
-reg3and4 <- registerSpatialData(object_list = DLPFC_list)In cases where at least of the spatial omics datasets are missing +their images, we can use the spatial distribution of the cells, spots or +observations from the VoltRon object to align to another objects with +either observations or images.
+We first import the DBIT-Seq data, and we also subset based on +transcript counts by spot to
+# DBIT-seq
+vr_dbit <- importDBITSeq(path.rna = "GSM9239729_Mouse_embryo_FFPE_1_stdata_names.tsv")
+vr_dbit <- subset(vr_dbit, Count > 1000)We use importQuPathIF function to import the multiplex
+IF experiment which was quantified by QuPath. See tutorial on
+importQuPathIF here for
+more information.
We first prepare the measurements and segments exported from QuPath +as follows:
+# dependencies
+if(!requireNamespace("sf"))
+ install.packages("sf")
+
+# get segments
+library(sf)
+my_sf <- read_sf("../../data/DBIT_plus_GSE308167/Ms_Embryo_031523_Scan1.geojson")
+my_sf <- my_sf[my_sf$objectType=="cell",]
+cell_names <- paste0("cell", seq_along(my_sf$geometry))
+segments <- mapply(function(x,y){
+ tmp <- st_coordinates(x)[,c("X","Y")]/4 # scale to res 3
+ colnames(tmp) <- c("x", "y")
+ data.frame(id = y, tmp)
+}, my_sf$geometry, cell_names, SIMPLIFY = FALSE)
+names(segments) <- cell_names
+
+# get cellular measurements per channel
+measurements <- data.table::fread("Ms_Embryo_031523_Scan1.txt")
+col_pattern <- "^Cell: (.*): Median$"
+cols <- grep(col_pattern, names(measurements), value = TRUE)
+measurements <- measurements[, ..cols]
+names(measurements) <- sub(col_pattern, "\\1", cols)
+measurements <- t(as.matrix(measurements))
+colnames(measurements) <- names(segments)Now we can import the experiment processed with QuPath, and we also
+check the image metadata to select series and
+resolution
# dependencies
+if(!requireNamespace("RBioFormats"))
+ BiocManager::install("RBioFormats")
+
+# image metadata
+image_file <- "Ms_Embryo_031523_Scan1.qptiff"
+RBioFormats::read.metadata(image_file)```ImageMetadata list of length 7
+series res sizeX sizeY sizeC sizeZ sizeT total 1 1 11520 12960 22 1 1
+22
+1 2 5760 6480 22 1 1 22
+1 3 2880 3240 22 1 1 22
+1 4 1440 1620 22 1 1 22
+2 1 360 405 3 1 1 3
+3 1 1239 2717 3 1 1 3
+4 1 960 720 3 1 1 3
+
+```{.r .watch-out}
+# import CODEX PhenoCycler-Fusion
+vr_qupath <- importQuPathIF(measurements = measurements,
+ image = image_file,
+ series = 1, resolution = 3,
+ channels = getOmeTiffChannels(image_file),
+ segments = segments)Here, the DBIT-Seq data does not have any image, whereas the mIF +does. However, we can use the registerSpatialData +function to warp/align a collection of observations to another +collection or an image. Since at least one VoltRon object is missing its +image, the resulting alignment can only be performed manually. Thus we +select landmark points and perform an affine or homograph +transformation.
+dbit_req <- registerSpatialData(reference_spatdata = vr_qupath,
+ query_spatdata = vr_dbit)
We can now combine all sections into one VoltRon object. There are -two pairs of serial tissue sections, but both pairs (thus 4 sections) -are from the same tissue block. Hence, we can combine these two lists -into one list and merge VoltRon objects even though sections were -aligned separately.
-merge_list <- c(reg1and2$registered_spat, reg3and4$registered_spat)
-SRBlock <- merge(merge_list[[1]], merge_list[-1], samples = "DLPFC_Block")
-SRBlockNow that we have the registered DBIT-Seq spots, we can define these +spots as a new assay in a combined VoltRon object, and even transfer the +mIF images as background to the DBIT-Seq assay.
+vr_qupath_reg <- dbit_reg$registered_spat[[1]]
+dbit_reg <- dbit_reg$registered_spat[[2]]
+vr_all <- addAssay(vr_qupath_reg,
+ assay = dbit_reg[["Assay1"]],
+ assay_name = "DBIT")
+
+# transfer images
+vrImages(vr_all[["Assay2"]], spatial = "main_reg", channel = "DAPI") <-
+ vrImages(vr_all, assay = "Assay1", channel = "DAPI")
+
+# update radii
+vr_all[["Assay2"]]@params$vis.spot.radius <-
+ vr_all[["Assay2"]]@params$vis.spot.radius * 5
+vr_all[["Assay2"]]@params$spot.radius <-
+ vr_all[["Assay2"]]@params$spot.radius * 5
+
+vr_allVoltRon Object
-DLPFC_Block:
- Layers: Section1 Section2 Section3 Section4
-Assays: Visium(Main)
-Features: RNA(Main)Now that we have combined registered pairs of adjacent tissue -sections, we should define the adjacency of these pairs in the tissue -block.
-# two pairs of adjacent sections
-connectivity <- data.frame(
- c(1,1,3,3),
- c(1,2,3,4)
-)
-# adjacent sections are 10 micron apart, and pairs 300 micron
-zlocation <- c(0, 10, 300, 310)*params$tissue_lowres_scalef
-
-# add connectivity information to the block
-SRBlock <- addBlockConnectivity(SRBlock,
- connectivity = connectivity,
- zlocation = zlocation,
- sample = "DLPFC_Block")The aligned and connected Visium assays of the DLPFC then could be -used for 3D spatial analysis, specifically as shown in the Niche Clustering tutorial.
+Sample1: + Layers: Section1 +Assays: IF(Main) DBIT +Features: main(Main) +The combined VoltRon object can be used to transfer features from the +mIF experiment to the DBIT-Seq spots. If not features are defined, all +features (in this case the mIF markers) will be transfered to DBIT spots +as additional feature sets.
+vr_all <- transferData(vr_all, from = "Assay1", to = "Assay2", new_feature_name = "Protein")
+
+vrMainAssay(vr_qupath_new) <- "DBIT"
+vr_allVoltRon Object
+Sample1:
+ Layers: Section1
+Assays: DBIT(Main) IF
+Features: RNA(Main) Protein We can visualize the transfered protein expression (i.e. image +intensity of the marker channel) on both the original mIF assay and the +DBIT-Seq spots.
+library(patchwork)
+vrFeatureTypeNames(vr_all, assay = "all")
+g1 <- vrSpatialFeaturePlot(vr_all, assay = "Assay1", features = "AQP4",
+ channel = "DAPI", n.tile = 400, alpha = 1)
+vrMainFeatureType(vr_all[["Assay2"]]) <- "Protein"
+g2 <- vrSpatialFeaturePlot(vr_all, assay = "Assay2", features = "AQP4",
+ channel = "DAPI", alpha = 1, spatial = "main_reg")
+g1 | g2
# get DE for all conditions
+# install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
+ BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
library(ComplexHeatmap)
+library(viridisLite)
+
vrHeatmapPlot(GeoMxR1, features = unique(DEresults_sig$gene), group.by = "ROI.type",
- highlight.some = TRUE, n_highlight = 40)
+ highlight.some = TRUE, n_highlight = 40, col = viridis(100))
In order to get a deeper understanding of differences between @@ -952,7 +958,8 @@
We can also exchange images where the H&E image now is registered to the perspective of the GeoMx channels, and can be defined as a new channel in the original GeoMx object.
-vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "main_reg", channel = "H&E")vrImages(GeoMxR1_subset[["Assay1"]], spatial = "main", channel = "H&E") <-
+ vrImages(vrHEimage_reg, spatial = "main_reg", channel = "H&E")We can now observe the new channels (H&E) available for the GeoMx
assay using vrImageChannelNames. H&E is saved as a
separate channel along with the originally available antibody channels
diff --git a/docs/site_libs/header-attrs-2.31/header-attrs.js b/docs/site_libs/header-attrs-2.31/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/site_libs/header-attrs-2.31/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/spotanalysis.Rmd b/docs/spotanalysis.Rmd
index 174bbcbd..9eb740b0 100644
--- a/docs/spotanalysis.Rmd
+++ b/docs/spotanalysis.Rmd
@@ -96,8 +96,7 @@ vrImages(Xen_R2)
Once both VoltRon objects are created and images are well-tuned, we can merge these two into a single VoltRon object.
```{r eval = FALSE, class.source="watch-out"}
-Xen_list <- list(Xen_R1, Xen_R2)
-Xen_data <- merge(Xen_list[[1]], Xen_list[-1])
+Xen_data <- merge(Xen_R1, Xen_R2)
```
```
@@ -277,7 +276,88 @@ vrSpatialFeaturePlot(Xen_data, features = "PGR", alpha = 1,
```
+
+
+## Single Cell Integration
+
+VoltRon supports the integration of VoltRon objects with Seurat or SingleCellExperiment objects
+to facilitate the integration of spatial and single cell omic data modalities. This integration
+enables both the transfer of features from the scRNA data that are originally not available in the spatial assay, or
+the transfer of annotations from the scRNA data that serves as a single cell omics reference.
+
+As the scRNA reference, we will use the 10x single cell FFPE dataset (GSE243275) generated from the same tissue block as the Xenium experiment. You can find the single cell data [here](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE243275).
+
+We first import the scRNA data as a Seurat object and we also normalize the profile of each cell.
+
+```{r eval = FALSE, class.source="watch-out"}
+if (!requireNamespace("Seurat", quietly = TRUE))
+ install.packages("Seurat")
+library(Seurat)
+seu <- Seurat::Read10X_h5("filtered_feature_bc_matrix.h5")
+seu <- Seurat::CreateSeuratObject(seu)
+seu <- Seurat::NormalizeData(seu, scale.factor = 10000)
+```
+
+To transfer the features of scRNA to the VoltRon object, we use the `transferData`
+function. If no features are provided, all features from the scRNA object
+will be transferred. After the transfer is complete, a new feature set for the
+target VoltRon assay will appear.
+
+```{r eval = FALSE, class.source="watch-out"}
+Xen_data <- transferData(Xen_data, seu, to = "Assay1",
+ features = VariableFeatures(seu))
+Xen_data
+```
+
+```
+VoltRon Object
+XeniumR1:
+ Layers: Section1
+XeniumR2:
+ Layers: Section1
+Assays: Xenium(Main)
+Features: RNA(Main) RNA_import
+```
+Now we can visualize features both existing in the Xenium panel such as EPCAM,
+and compare it with another tumor marker, e.g. CCNB2, that are spatially
+correlated with EPCAM in this tissue section.
+
+```{r eval = FALSE, class.source="watch-out", fig.align='center'}
+if (!requireNamespace("patchwork", quietly = TRUE))
+ install.packages("patchwork")
+library(patchwork)
+
+vrMainFeatureType(Xen_data) <- "RNA"
+g1 <- vrSpatialFeaturePlot(Xen_data, assay = "Assay1",
+ features = "EPCAM", n.tile = 200)
+vrMainFeatureType(Xen_data[["Assay1"]]) <- "Import"
+g2 <- vrSpatialFeaturePlot(Xen_data, assay = "Assay1",
+ features = "CCNB2", n.tile = 200)
+g1 | g2
+```
+
+
+
+
+VoltRon also allows transfering cell type annotations (or any metadata feature)
+from a single cell assay to a VoltRon assay. We use the cell metadata available
+in [GSE243275](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE243275).
+
+```{r eval = FALSE, class.source="watch-out", fig.align='center'}
+Xen_data <- transferData(Xen_data, seu, to = "Assay1",
+ features = "CellType",
+ new_feature_name = "CellType_scRNA")
+```
+
+Now we can visualize the transfered annotations on spatially-resolved cells.
+
+```{r eval = FALSE, class.source="watch-out", fig.align='center'}
+vrSpatialPlot(Xen_data, assay = "Assay1", group.by = "CellType_scRNA",
+ n.tile = 200)
+```
+
+
# Visium Data Analysis
@@ -607,14 +687,18 @@ g1 | g2
VoltRon provides both violin plots (**vrViolinPlot**) and heatmaps (**vrHeatmapPlot**) to further investigate the enrichment of markers across newly clustered datasets. **Note:** the vrHeatmapPlot function would require you to have the **ComplexHeatmap** package in your namespace.
```{r eval = FALSE, class.source="watch-out", fig.align='center'}
-# install patchwork package
-if (!requireNamespace("ComplexHeatmap", quietly = TRUE))
+# install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
library(ComplexHeatmap)
+library(viridisLite)
# Visualize Markers
vrHeatmapPlot(vr_merged, features = vrFeatures(vr_merged),
- group.by = "MELC_Clusters", show_row_names = TRUE)
+ group.by = "MELC_Clusters", show_row_names = TRUE,
+ col = viridis(100))
```
diff --git a/docs/spotanalysis.html b/docs/spotanalysis.html
index 20347c10..865562ff 100644
--- a/docs/spotanalysis.html
+++ b/docs/spotanalysis.html
@@ -13,7 +13,7 @@
Once both VoltRon objects are created and images are well-tuned, we can merge these two into a single VoltRon object.
-Xen_list <- list(Xen_R1, Xen_R2)
-Xen_data <- merge(Xen_list[[1]], Xen_list[-1])Xen_data <- merge(Xen_R1, Xen_R2)VoltRon Object
XeniumR1:
Layers: Section1
@@ -697,8 +696,73 @@ Hot Spot Analysis
vrSpatialFeaturePlot(Xen_data, features = "PGR", alpha = 1,
n.tile = 250, background.color = "black")
-
-
+
+
+VoltRon supports the integration of VoltRon objects with Seurat or +SingleCellExperiment objects to facilitate the integration of spatial +and single cell omic data modalities. This integration enables both the +transfer of features from the scRNA data that are originally not +available in the spatial assay, or the transfer of annotations from the +scRNA data that serves as a single cell omics reference.
+As the scRNA reference, we will use the 10x single cell FFPE dataset +(GSE243275) generated from the same tissue block as the Xenium +experiment. You can find the single cell data here.
+We first import the scRNA data as a Seurat object and we also +normalize the profile of each cell.
+if (!requireNamespace("Seurat", quietly = TRUE))
+ install.packages("Seurat")
+library(Seurat)
+seu <- Seurat::Read10X_h5("filtered_feature_bc_matrix.h5")
+seu <- Seurat::CreateSeuratObject(seu)
+seu <- Seurat::NormalizeData(seu, scale.factor = 10000)To transfer the features of scRNA to the VoltRon object, we use the
+transferData function. If no features are provided, all
+features from the scRNA object will be transferred. After the transfer
+is complete, a new feature set for the target VoltRon assay will
+appear.
Xen_data <- transferData(Xen_data, seu, to = "Assay1",
+ features = VariableFeatures(seu))
+Xen_dataVoltRon Object
+XeniumR1:
+ Layers: Section1
+XeniumR2:
+ Layers: Section1
+Assays: Xenium(Main)
+Features: RNA(Main) RNA_import Now we can visualize features both existing in the Xenium panel such +as EPCAM, and compare it with another tumor marker, e.g. CCNB2, that are +spatially correlated with EPCAM in this tissue section.
+if (!requireNamespace("patchwork", quietly = TRUE))
+ install.packages("patchwork")
+library(patchwork)
+
+vrMainFeatureType(Xen_data) <- "RNA"
+g1 <- vrSpatialFeaturePlot(Xen_data, assay = "Assay1",
+ features = "EPCAM", n.tile = 200)
+vrMainFeatureType(Xen_data[["Assay1"]]) <- "Import"
+g2 <- vrSpatialFeaturePlot(Xen_data, assay = "Assay1",
+ features = "CCNB2", n.tile = 200)
+g1 | g2
+
VoltRon also allows transfering cell type annotations (or any +metadata feature) from a single cell assay to a VoltRon assay. We use +the cell metadata available in GSE243275.
+Xen_data <- transferData(Xen_data, seu, to = "Assay1",
+ features = "CellType",
+ new_feature_name = "CellType_scRNA")Now we can visualize the transfered annotations on spatially-resolved +cells.
+vrSpatialPlot(Xen_data, assay = "Assay1", group.by = "CellType_scRNA",
+ n.tile = 200)
+
# install patchwork package
-if (!requireNamespace("ComplexHeatmap", quietly = TRUE))
+# install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
BiocManager::install("ComplexHeatmap")
+if (!requireNamespace('viridisLite'))
+ install.packages('viridisLite')
library(ComplexHeatmap)
+library(viridisLite)
# Visualize Markers
vrHeatmapPlot(vr_merged, features = vrFeatures(vr_merged),
- group.by = "MELC_Clusters", show_row_names = TRUE)
+ group.by = "MELC_Clusters", show_row_names = TRUE,
+ col = viridis(100))
vrViolinPlot(vr_merged, features = c("CD3", "SMA", "Pancytokeratin", "CCR2"),
diff --git a/docs/tutorials.html b/docs/tutorials.html
index 1dd1f8da..caeeb366 100644
--- a/docs/tutorials.html
+++ b/docs/tutorials.html
@@ -13,7 +13,7 @@
Tutorials
-
+
diff --git a/docs/voltronobjects.Rmd b/docs/voltronobjects.Rmd
index 2305c61c..1b1b808a 100644
--- a/docs/voltronobjects.Rmd
+++ b/docs/voltronobjects.Rmd
@@ -192,17 +192,17 @@ vrCoordinates(visium_data, assay = "Assay1")
Each assay a VoltRon object may incorporate indefinite number of coordinate systems. One can look for these coordinate systems using the **vrMainSpatial** function, and select one of systems to call coordinates (see [vrMainSpatial](#image))
```{r eval = FALSE, class.source="watch-out"}
-vrCoordinates(visium_data, spatial_name = "main")
+vrCoordinates(visium_data, spatial = "main")
```
The **reg** option in the **vrCoordinates** function looks for a registered version of the main coordinate system and returns its coordinates (if there is any).
```{r eval = FALSE, class.source="watch-out"}
-vrCoordinates(visium_data, spatial_name = "main", reg = TRUE)
+vrCoordinates(visium_data, spatial = "main", reg = TRUE)
```
```{r echo = FALSE, class.source="watch-out"}
-head(vrCoordinates(visium_data, spatial_name = "main", reg = TRUE),6)
+head(vrCoordinates(visium_data, spatial = "main", reg = TRUE),6)
```
@@ -213,8 +213,8 @@ The arguments of the **vrSegments** functions are identical to vrCoordinates and
vrSegments(xenium_data)
vrSegments(xenium_data, assay = "Xenium")
vrSegments(xenium_data, assay = "Assay1")
-vrSegments(xenium_data, spatial_name = "main")
-vrSegments(xenium_data, spatial_name = "main", reg = TRUE)
+vrSegments(xenium_data, spatial = "main")
+vrSegments(xenium_data, spatial = "main", reg = TRUE)
```
```{r echo = FALSE, class.source="watch-out"}
@@ -306,11 +306,11 @@ magick::image_ggplot(vrImages(visium_data))
Once you know the name of a specific channel, you can the image of a specific channel by providing the name and the associated channel.
```{r eval = FALSE, class.source="watch-out"}
-vrImages(melc_data, name = "MELC", channel = "DAPI")
+vrImages(melc_data, spatial = "MELC", channel = "DAPI")
```
```{r echo = FALSE, class.source="watch-out", fig.align='center', fig.height=5, fig.width=6, out.width="60%"}
-magick::image_ggplot(vrImages(melc_data, name = "MELC", channel = "DAPI"))
+magick::image_ggplot(vrImages(melc_data, spatial = "MELC", channel = "DAPI"))
```
diff --git a/docs/voltronobjects.html b/docs/voltronobjects.html
index c5d8db92..dcb0daed 100644
--- a/docs/voltronobjects.html
+++ b/docs/voltronobjects.html
@@ -13,7 +13,7 @@
Importing Spatial Data
-
+
@@ -494,12 +494,12 @@ Metadata
the main assay type (see vrMainAssay).
Metadata(visium_data)
## id Count assay_id Assay Layer Sample
-## AAAGGCTCTCGCGCCG-1_Assay1 AAAGGCTCTCGCGCCG-1_Assay1 39690 Assay1 Visium Section1 Anterior1
-## AAATGGCCCGTGCCCT-1_Assay1 AAATGGCCCGTGCCCT-1_Assay1 33516 Assay1 Visium Section1 Anterior1
-## AAATTACACGACTCTG-1_Assay1 AAATTACACGACTCTG-1_Assay1 12250 Assay1 Visium Section1 Anterior1
-## AAGACATACGTGGTTT-1_Assay1 AAGACATACGTGGTTT-1_Assay1 31821 Assay1 Visium Section1 Anterior1
-## ACCTACTATAAATCTA-1_Assay1 ACCTACTATAAATCTA-1_Assay1 41387 Assay1 Visium Section1 Anterior1
-## ACGCGGGCCAAGGACA-1_Assay1 ACGCGGGCCAAGGACA-1_Assay1 48003 Assay1 Visium Section1 Anterior1
+## CCTTGACCACTTTATT-1_Assay1 CCTTGACCACTTTATT-1_Assay1 2350 Assay1 Visium Section1 Anterior1
+## ATTTGTCTTGGGAGCT-1_Assay1 ATTTGTCTTGGGAGCT-1_Assay1 16200 Assay1 Visium Section1 Anterior1
+## TCACGCATTGTAGATC-1_Assay1 TCACGCATTGTAGATC-1_Assay1 29377 Assay1 Visium Section1 Anterior1
+## CCGAGCTGTGCTTGTC-1_Assay1 CCGAGCTGTGCTTGTC-1_Assay1 32380 Assay1 Visium Section1 Anterior1
+## GCATGGGTACTGACGC-1_Assay1 GCATGGGTACTGACGC-1_Assay1 25433 Assay1 Visium Section1 Anterior1
+## AGTCGGCCCAAACGAC-1_Assay1 AGTCGGCCCAAACGAC-1_Assay1 18906 Assay1 Visium Section1 Anterior1You can also specify the assay ID or the assay type to call metadata of a subset of spatial points.
Metadata(visium_data, assay = "Visium")
@@ -523,12 +523,12 @@ Spatial Points
The vrSpatialPoints function return the IDs of these
entities for further downstream operations, such as subsetting etc.
vrSpatialPoints(visium_data)
-## [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1"
+## [1] "CCTTGACCACTTTATT-1_Assay1" "ATTTGTCTTGGGAGCT-1_Assay1" "TCACGCATTGTAGATC-1_Assay1"
You can also specify the assay ID or the assay type to call metadata
of a subset of spatial points.
vrSpatialPoints(visium_data, assay = "Visium")
vrSpatialPoints(visium_data, assay = "Assay1")
-## [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1"
+## [1] "CCTTGACCACTTTATT-1_Assay1" "ATTTGTCTTGGGAGCT-1_Assay1" "TCACGCATTGTAGATC-1_Assay1"
vrCoordinates(visium_data, spatial_name = "main")vrCoordinates(visium_data, spatial = "main")The reg option in the vrCoordinates function looks for a registered version of the main coordinate system and returns its coordinates (if there is any).
-vrCoordinates(visium_data, spatial_name = "main", reg = TRUE)vrCoordinates(visium_data, spatial = "main", reg = TRUE)## Warning in .local(object, ...): There are no registered spatial systems with name main!## x y z
## CCTTGACCACTTTATT-1_Assay1 463.78658 183.1497 0
@@ -604,8 +604,8 @@ Coordinates and Segments
vrSegments(xenium_data)
vrSegments(xenium_data, assay = "Xenium")
vrSegments(xenium_data, assay = "Assay1")
-vrSegments(xenium_data, spatial_name = "main")
-vrSegments(xenium_data, spatial_name = "main", reg = TRUE)
+vrSegments(xenium_data, spatial = "main")
+vrSegments(xenium_data, spatial = "main", reg = TRUE)## $`171_Assay1`
## id x y
## 1 171 105.25 145.50
@@ -696,19 +696,12 @@ Get and Set Images
vrImages(visium_data)
vrImages(visium_data, assay = "Visium")
vrImages(visium_data, assay = "Assay1")
-## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
-## ℹ Please use tidy evaluation idioms with `aes()`.
-## ℹ See also `vignette("ggplot2-in-packages")` for more information.
-## ℹ The deprecated feature was likely used in the magick package.
-## Please report the issue at <https://github.com/ropensci/magick/issues>.
-## This warning is displayed once per session.
-## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Once you know the name of a specific channel, you can the image of a
specific channel by providing the name and the associated channel.
-vrImages(melc_data, name = "MELC", channel = "DAPI")
+vrImages(melc_data, spatial = "MELC", channel = "DAPI")
@@ -746,21 +739,21 @@ Combining Image Channels
Feature Matrix (Data)
vrData(visium_data)[3:8,3:5]
-## AAATTACACGACTCTG-1_Assay1 AAGACATACGTGGTTT-1_Assay1 ACCTACTATAAATCTA-1_Assay1
+## TCACGCATTGTAGATC-1_Assay1 CCGAGCTGTGCTTGTC-1_Assay1 GCATGGGTACTGACGC-1_Assay1
## Gm19938 0 0 0
## Gm37381 0 0 0
## Rp1 0 0 0
-## Sox17 0 1 1
+## Sox17 0 0 0
## Gm37587 0 0 0
## Gm37323 0 0 0
vrData(visium_data, norm = TRUE)[3:8,3:5]
-## AAATTACACGACTCTG-1_Assay1 AAGACATACGTGGTTT-1_Assay1 ACCTACTATAAATCTA-1_Assay1
-## Gm19938 0 0.0000000 0.0000000
-## Gm37381 0 0.0000000 0.0000000
-## Rp1 0 0.0000000 0.0000000
-## Sox17 0 0.2732722 0.2164184
-## Gm37587 0 0.0000000 0.0000000
-## Gm37323 0 0.0000000 0.0000000
+## TCACGCATTGTAGATC-1_Assay1 CCGAGCTGTGCTTGTC-1_Assay1 GCATGGGTACTGACGC-1_Assay1
+## Gm19938 0 0 0
+## Gm37381 0 0 0
+## Rp1 0 0 0
+## Sox17 0 0 0
+## Gm37587 0 0 0
+## Gm37323 0 0 0
@@ -823,7 +816,7 @@ spatial points
href="#spatialpoints">vrSpatialPoints)
selected_points <- vrSpatialPoints(visium_data)
selected_points[1:20]
-## [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1"
+## [1] "CCTTGACCACTTTATT-1_Assay1" "ATTTGTCTTGGGAGCT-1_Assay1" "TCACGCATTGTAGATC-1_Assay1"
visium_data_subset <- subset(visium_data, spatialpoints = selected_points[1:20])
visium_data_subset
## VoltRon Object
@@ -840,14 +833,16 @@ features
href="#assays">vrMainAssay)
selected_features <- vrFeatures(visium_data)
selected_features[1:20]
-## [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1" "Sox17" "Gm37587" "Gm37323"
-## [9] "Mrpl15" "Lypla1" "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1" "Npbwr1"
-## [17] "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"
+## [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1"
+## [6] "Sox17" "Gm37587" "Gm37323" "Mrpl15" "Lypla1"
+## [11] "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1"
+## [16] "Npbwr1" "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"
visium_data_subset <- subset(visium_data, features = selected_features[1:20])
vrFeatures(visium_data_subset)
-## [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1" "Sox17" "Gm37587" "Gm37323"
-## [9] "Mrpl15" "Lypla1" "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1" "Npbwr1"
-## [17] "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"
+## [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1"
+## [6] "Sox17" "Gm37587" "Gm37323" "Mrpl15" "Lypla1"
+## [11] "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1"
+## [16] "Npbwr1" "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"
diff --git a/inst/extdata/DAPI.geojson b/inst/extdata/DAPI.geojson
new file mode 100644
index 00000000..5f677367
--- /dev/null
+++ b/inst/extdata/DAPI.geojson
@@ -0,0 +1,15188 @@
+{
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "id": "c2fff1fc-15d9-4d71-921d-cda1bb0e7428",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [337.18, 0.82],
+ [337.47, 2.8],
+ [339.95, 5.9],
+ [341.32, 9.64],
+ [344.06, 12.53],
+ [347.99, 12.8],
+ [351.59, 11.08],
+ [354, 7.93],
+ [353.97, 3.93],
+ [350.91, 0.61],
+ [349, 0],
+ [339, 0],
+ [337.18, 0.82]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 165.0,
+ "Cell: Perimeter": 48.580309275983865,
+ "Cell: Circularity": 0.8785637045152714,
+ "Cell: Max caliper": 18.262774285804696,
+ "Cell: Min caliper": 13.0,
+ "Cell: Eccentricity": 0.6479720506630946,
+ "Cell: Channel 1 mean": 5613.090361445783,
+ "Cell: Channel 1 std dev": 14149.191877907584,
+ "Cell: Channel 1 max": 65535.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 280.2076923076923,
+ "Cytoplasm: Channel 1 std dev": 1274.996395776355,
+ "Cytoplasm: Channel 1 max": 9556.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "0eaf047a-725d-489b-a996-3f1d40828dc3",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [373.17, 1.16],
+ [373.23, 5.14],
+ [377.24, 9.57],
+ [383.19, 9.81],
+ [384.9, 6.21],
+ [383.72, 2.44],
+ [380.82, 0],
+ [374.83, 0.03],
+ [373.17, 1.16]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 98.0,
+ "Cell: Perimeter": 35.80983451462504,
+ "Cell: Circularity": 0.9603539160109945,
+ "Cell: Max caliper": 13.240437842209861,
+ "Cell: Min caliper": 9.66379050888936,
+ "Cell: Eccentricity": 0.6328279795622074,
+ "Cell: Channel 1 mean": 7195.04,
+ "Cell: Channel 1 std dev": 14083.861639665893,
+ "Cell: Channel 1 max": 53077.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 316.7432432432432,
+ "Cytoplasm: Channel 1 std dev": 1297.835173915529,
+ "Cytoplasm: Channel 1 max": 8604.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "290e818f-f13c-446e-95e8-813aa8b6331b",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [383.79, 1.13],
+ [385.72, 4.61],
+ [385.1, 8.52],
+ [385.75, 10.41],
+ [387.31, 11.66],
+ [391.08, 12.98],
+ [394.81, 11.59],
+ [396.34, 10.31],
+ [397, 8.42],
+ [396.72, 2.44],
+ [393.45, 0],
+ [385.45, 0],
+ [383.79, 1.13]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 135.0,
+ "Cell: Perimeter": 44.143136273380925,
+ "Cell: Circularity": 0.8705971905229476,
+ "Cell: Max caliper": 15.554043484590913,
+ "Cell: Min caliper": 12.503898630102414,
+ "Cell: Eccentricity": 0.4324020969167601,
+ "Cell: Channel 1 mean": 7439.072463768116,
+ "Cell: Channel 1 std dev": 16408.10790705623,
+ "Cell: Channel 1 max": 65535.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 484.21153846153845,
+ "Cytoplasm: Channel 1 std dev": 1397.2310041968267,
+ "Cytoplasm: Channel 1 max": 9180.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "335cc84b-9ce3-4f51-bdcc-5f612251eb5c",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [140, 2.26],
+ [140.79, 6.12],
+ [143.71, 8.85],
+ [148.96, 11.67],
+ [153.81, 7.85],
+ [153.97, 1.86],
+ [152.89, 0.18],
+ [142.9, 0],
+ [140.97, 0.52],
+ [140, 2.26]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 129.0,
+ "Cell: Perimeter": 42.20671769981471,
+ "Cell: Circularity": 0.9099895711479441,
+ "Cell: Max caliper": 14.900713141859754,
+ "Cell: Min caliper": 11.666666984558105,
+ "Cell: Eccentricity": 0.6332292524728266,
+ "Cell: Channel 1 mean": 3815.0227272727275,
+ "Cell: Channel 1 std dev": 9789.932033125666,
+ "Cell: Channel 1 max": 47091.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 306.8137254901961,
+ "Cytoplasm: Channel 1 std dev": 1807.8063657503171,
+ "Cytoplasm: Channel 1 max": 16012.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "ceedeeb6-ea15-483f-b8a1-f38475d9615a",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
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+ "Cell: Circularity": 0.9035004595819376,
+ "Cell: Max caliper": 15.567584013838555,
+ "Cell: Min caliper": 11.313719288577978,
+ "Cell: Eccentricity": 0.7030326471021898,
+ "Cell: Channel 1 mean": 4527.669064748202,
+ "Cell: Channel 1 std dev": 9383.193209282847,
+ "Cell: Channel 1 max": 36290.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 238.02777777777777,
+ "Cytoplasm: Channel 1 std dev": 773.3029469873464,
+ "Cytoplasm: Channel 1 max": 4495.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "8402e6fd-31f3-4985-886e-9fded39ebe1a",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [229, 388.19],
+ [229.09, 390.19],
+ [232.81, 394.76],
+ [238.79, 394.84],
+ [241.93, 392.41],
+ [242.67, 390.55],
+ [240.73, 387.07],
+ [235.99, 382.66],
+ [234.04, 382.19],
+ [232.37, 383.29],
+ [229, 388.19]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 123.0,
+ "Cell: Perimeter": 40.485151100301266,
+ "Cell: Circularity": 0.9430255216221585,
+ "Cell: Max caliper": 13.868559317877331,
+ "Cell: Min caliper": 11.31370849898476,
+ "Cell: Eccentricity": 0.4892646805846189,
+ "Cell: Channel 1 mean": 2606.6299212598424,
+ "Cell: Channel 1 std dev": 5892.420036433611,
+ "Cell: Channel 1 max": 25639.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 213.46153846153845,
+ "Cytoplasm: Channel 1 std dev": 716.4874801320397,
+ "Cytoplasm: Channel 1 max": 4294.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "6fafebbc-950b-4d09-b33b-168cbd85f137",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [114.32, 393.63],
+ [117.05, 396.52],
+ [122.98, 396.8],
+ [127.47, 392.86],
+ [127.97, 386.93],
+ [124.21, 383.14],
+ [122.23, 383.39],
+ [118.84, 385.49],
+ [114.62, 389.75],
+ [114.32, 393.63]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 144.0,
+ "Cell: Perimeter": 43.41469686467573,
+ "Cell: Circularity": 0.96006096062255,
+ "Cell: Max caliper": 15.205690161296436,
+ "Cell: Min caliper": 11.31369770939154,
+ "Cell: Eccentricity": 0.6499531381366718,
+ "Cell: Channel 1 mean": 4231.703448275862,
+ "Cell: Channel 1 std dev": 9592.260361096205,
+ "Cell: Channel 1 max": 40811.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 353.30434782608694,
+ "Cytoplasm: Channel 1 std dev": 1056.2341594278864,
+ "Cytoplasm: Channel 1 max": 6893.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "710f38dc-0bc9-4d89-9a3d-de08cef62695",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [86, 392.06],
+ [86.63, 395.96],
+ [91.67, 399],
+ [95.65, 398.68],
+ [98.91, 396.43],
+ [99.67, 394.58],
+ [98.85, 388.7],
+ [97.33, 385],
+ [95.02, 384.49],
+ [88.42, 388.91],
+ [86, 392.06]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 145.0,
+ "Cell: Perimeter": 44.4578186649365,
+ "Cell: Circularity": 0.921895226079001,
+ "Cell: Max caliper": 15.546230388298607,
+ "Cell: Min caliper": 12.4700919936813,
+ "Cell: Eccentricity": 0.5356564786578657,
+ "Cell: Channel 1 mean": 3900.1283783783783,
+ "Cell: Channel 1 std dev": 7734.1188226583845,
+ "Cell: Channel 1 max": 31215.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 256.64864864864865,
+ "Cytoplasm: Channel 1 std dev": 812.3714180622809,
+ "Cytoplasm: Channel 1 max": 5430.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "9889b3b7-1dbf-4f5e-a8e1-c983030a01fa",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [213.33, 395.25],
+ [215.16, 398.78],
+ [216.77, 399.95],
+ [226.77, 400],
+ [228, 398.42],
+ [227, 390.54],
+ [225.83, 388.92],
+ [220.56, 386.09],
+ [218.74, 386.93],
+ [213.33, 395.25]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 147.0,
+ "Cell: Perimeter": 45.996606844719686,
+ "Cell: Circularity": 0.8731233617332002,
+ "Cell: Max caliper": 15.344799790900773,
+ "Cell: Min caliper": 13.911376953125,
+ "Cell: Eccentricity": 0.15839196919339757,
+ "Cell: Channel 1 mean": 6693.3677419354835,
+ "Cell: Channel 1 std dev": 10015.45752079757,
+ "Cell: Channel 1 max": 49161.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 1621.5094339622642,
+ "Cytoplasm: Channel 1 std dev": 2749.8309359123577,
+ "Cytoplasm: Channel 1 max": 13702.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "ed3e652e-9b7d-4ddf-a165-b4fd29ad5fad",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [33.07, 392.66],
+ [33.33, 396.63],
+ [38.21, 400],
+ [46.2, 399.9],
+ [47.71, 398.58],
+ [46.86, 392.72],
+ [42.9, 388.24],
+ [40.21, 386.14],
+ [33.85, 390.82],
+ [33.07, 392.66]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 152.0,
+ "Cell: Perimeter": 45.41097222933186,
+ "Cell: Circularity": 0.926257743826765,
+ "Cell: Max caliper": 15.887186294404184,
+ "Cell: Min caliper": 12.727935548349379,
+ "Cell: Eccentricity": 0.5492400809106724,
+ "Cell: Channel 1 mean": 5226.16339869281,
+ "Cell: Channel 1 std dev": 9967.011125825822,
+ "Cell: Channel 1 max": 43941.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 870.542372881356,
+ "Cytoplasm: Channel 1 std dev": 2042.763996660515,
+ "Cytoplasm: Channel 1 max": 9763.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "9bd0b441-1bc7-4c9a-aad8-fe01c4600c8e",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [48.11, 394.56],
+ [50.01, 398],
+ [53.84, 399],
+ [57.83, 398.83],
+ [59.54, 397.79],
+ [60, 395.85],
+ [59.92, 389.85],
+ [59.03, 388.06],
+ [55.7, 386.06],
+ [53.85, 386.82],
+ [48.29, 392.57],
+ [48.11, 394.56]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 119.0,
+ "Cell: Perimeter": 39.886814022178584,
+ "Cell: Circularity": 0.9399356653255149,
+ "Cell: Max caliper": 13.427868315020572,
+ "Cell: Min caliper": 11.785089642323818,
+ "Cell: Eccentricity": 0.42787714776262675,
+ "Cell: Channel 1 mean": 3932.7258064516127,
+ "Cell: Channel 1 std dev": 6943.576589853819,
+ "Cell: Channel 1 max": 24738.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 497.70526315789476,
+ "Cytoplasm: Channel 1 std dev": 1117.336487858364,
+ "Cytoplasm: Channel 1 max": 4688.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "d3d8360b-5912-4fc7-a61d-ae844bda9230",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [381.17, 395.17],
+ [381.54, 399.09],
+ [383.32, 400],
+ [395.3, 399.7],
+ [396, 397.83],
+ [395.77, 393.84],
+ [393.16, 390.82],
+ [390.13, 389.09],
+ [386.17, 389.41],
+ [382.8, 391.53],
+ [381.17, 395.17]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 139.0,
+ "Cell: Perimeter": 43.51243854312117,
+ "Cell: Circularity": 0.9225667929734708,
+ "Cell: Max caliper": 15.161455222739077,
+ "Cell: Min caliper": 11.0,
+ "Cell: Eccentricity": 0.6848416913438913,
+ "Cell: Channel 1 mean": 6574.390070921986,
+ "Cell: Channel 1 std dev": 16834.406620614413,
+ "Cell: Channel 1 max": 65535.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 289.24778761061947,
+ "Cytoplasm: Channel 1 std dev": 1210.55894359186,
+ "Cytoplasm: Channel 1 max": 10277.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ },
+ {
+ "type": "Feature",
+ "id": "463d2093-e273-4e05-bc75-f4dc68f26c52",
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [17.09, 393.82],
+ [17.61, 399.72],
+ [29.6, 400],
+ [31.52, 399.48],
+ [32, 395.53],
+ [31.32, 393.65],
+ [27.44, 390.29],
+ [21.46, 390],
+ [19.6, 390.73],
+ [17.09, 393.82]
+ ]
+ ]
+ },
+ "properties": {
+ "objectType": "cell",
+ "measurements": {
+ "Cell: Area": 132.0,
+ "Cell: Perimeter": 43.141386537098974,
+ "Cell: Circularity": 0.8912419031276683,
+ "Cell: Max caliper": 15.50331924757213,
+ "Cell: Min caliper": 10.0,
+ "Cell: Eccentricity": 0.7362079002944322,
+ "Cell: Channel 1 mean": 3180.7611940298507,
+ "Cell: Channel 1 std dev": 7911.98494070492,
+ "Cell: Channel 1 max": 35612.0,
+ "Cell: Channel 1 min": 0.0,
+ "Cytoplasm: Channel 1 mean": 186.7663551401869,
+ "Cytoplasm: Channel 1 std dev": 712.5983623522831,
+ "Cytoplasm: Channel 1 max": 5892.0,
+ "Cytoplasm: Channel 1 min": 0.0
+ }
+ }
+ }
+ ]
+}
\ No newline at end of file
diff --git a/inst/extdata/bspline_map.txt b/inst/extdata/bspline_map.txt
index 12340a04..4b706fa8 100644
--- a/inst/extdata/bspline_map.txt
+++ b/inst/extdata/bspline_map.txt
@@ -34,4 +34,5 @@
(WriteResultImage "true")
(WriteTransformParametersEachResolution "true")
(ResultImageFormat "tif")
-(ResultImagePixelType "unsigned char")
\ No newline at end of file
+(ResultImagePixelType "unsigned char")
+(RandomSeed 121212)
\ No newline at end of file
diff --git a/inst/legacy/import.R b/inst/legacy/import.R
new file mode 100644
index 00000000..c1a6cf55
--- /dev/null
+++ b/inst/legacy/import.R
@@ -0,0 +1,96 @@
+#' generateSegments_old
+#'
+#' The function to import segments from a geojson file
+#'
+#' @param geojson.file the GeoJSON file, typically generated by QuPath software
+#'
+#' @importFrom rjson fromJSON
+#' @importFrom dplyr tibble
+#'
+#' @noRd
+generateSegments_old <- function(geojson.file) {
+ # get segments
+ if (inherits(geojson.file, "character")) {
+ if (file.exists(geojson.file)) {
+ segments <- rjson::fromJSON(file = geojson.file)
+ } else {
+ stop("geojson.file doesn't exist!")
+ }
+ } else {
+ stop("geojson.file should be the path to the GeoJSON file!")
+ }
+
+ # parse polygons as segments/ROIs
+ segments <- lapply(segments, function(x) {
+ type <- x$geometry$type
+ poly <- x$geometry$coordinates
+ if (grepl("Polygon", type)) {
+ poly <- as.data.frame(matrix(unlist(poly[[1]]), ncol = 2, byrow = TRUE))
+ }
+ colnames(poly) <- c("x", "y")
+ dplyr::tibble(poly)
+ })
+
+ # attach names to segments
+ segments <- mapply(
+ function(x, sgt) {
+ dplyr::tibble(data.frame(id = x, sgt))
+ },
+ seq_len(length(segments)),
+ segments,
+ SIMPLIFY = FALSE
+ )
+
+ # generate ROI names
+ names(segments) <- paste0("ROI", seq_len(length(segments)))
+
+ # return
+ return(segments)
+}
+
+#' generateGeoJSON_old
+#'
+#' Old version: generating geojson files from segments
+#'
+#' @param segments the segments, typically from \link{vrSegments}.
+#' @param file the GeoJSON file, typically to be used by QuPath software.
+#'
+#' @noRd
+generateGeoJSON_old <- function(segments, file) {
+ if (!requireNamespace('geojsonR')) {
+ stop("Please install geojsonR package for using geojsonR functions: ",
+ "install.packages('geojsonR')")
+ }
+
+ # get segments
+ if (!inherits(file, "character")) {
+ stop("file should be the path to the GeoJSON file!")
+ }
+
+ # reshape segments
+ segments <- mapply(
+ function(id, sgt) {
+ poly <- na.omit(as.matrix(sgt[, c("x", "y")]))
+ poly <- rbind(poly, poly[1, , drop = FALSE])
+ poly <- as.list(data.frame(t(poly)))
+ names(poly) <- NULL
+ init <- geojsonR::TO_GeoJson$new()
+ geometry <- init$Polygon(list(poly), stringify = TRUE)
+ feature <- list(
+ type = "Feature",
+ id = id,
+ geometry = geometry[!names(geometry) %in% "json_dump"],
+ properties = list(objectType = "annotation")
+ )
+ feature
+ },
+ names(segments),
+ segments,
+ SIMPLIFY = FALSE,
+ USE.NAMES = FALSE
+ )
+
+ # save as json
+ segments <- rjson::toJSON(segments)
+ write(segments, file = file)
+}
\ No newline at end of file
diff --git a/man/VoltRon-methods.Rd b/man/VoltRon-methods.Rd
index 96bdf224..b7f7bf9e 100644
--- a/man/VoltRon-methods.Rd
+++ b/man/VoltRon-methods.Rd
@@ -69,13 +69,13 @@ packages
\item \code{.DollarNames(VoltRon)}: Autocompletion for \code{$} access for \code{VoltRon} objects
-\item \code{x[[i}: Accessing vrAssay or vrSample objects from \code{VoltRon} objects
+\item \code{x = VoltRon[[i = character, j = missing]]}: Accessing vrAssay or vrSample objects from \code{VoltRon} objects
-\item \code{`[[`(x = VoltRon, i = character, j = missing) <- value}: Overwriting vrAssay or vrSample objects from \code{VoltRon} objects
+\item \code{x = VoltRon[[i = character, j = missing]] <- value}: Overwriting vrAssay or vrSample objects from \code{VoltRon} objects
-\item \code{x[[i}: Accessing vrLayer objects from \code{VoltRon} objects
+\item \code{x = VoltRon[[i = character, j = character]]}: Accessing vrLayer objects from \code{VoltRon} objects
-\item \code{`[[`(x = VoltRon, i = character, j = character) <- value}: Overwriting vrLayer objects from \code{VoltRon} objects
+\item \code{x = VoltRon[[i = character, j = character]] <- value}: Overwriting vrLayer objects from \code{VoltRon} objects
}}
\concept{voltron}
diff --git a/man/addSpatialLayer.Rd b/man/addSpatialLayer.Rd
index 7c08dd64..b466f58a 100644
--- a/man/addSpatialLayer.Rd
+++ b/man/addSpatialLayer.Rd
@@ -54,7 +54,7 @@ Only applicable to spots, cells and molecules. If \code{n.tile = 0} will turn of
\item{graph.edge.color}{the color of graph edges, if \code{graph} is not NULL.}
-\item{spatial}{the name of the main spatial system}
+\item{spatial}{the name of the spatial coordinate system}
\item{combine.groups}{if TRUE, tile colors will reflect relative abundance of either of two groups, strictly for visualizing two groups when assay is a molecule typed and tiled (see \code{n.tile}).}
}
diff --git a/man/annotateSpatialData.Rd b/man/annotateSpatialData.Rd
index d1088e56..f646ca85 100644
--- a/man/annotateSpatialData.Rd
+++ b/man/annotateSpatialData.Rd
@@ -11,7 +11,7 @@ annotateSpatialData(
annotation_assay = "ROIAnnotation",
use.image.only = FALSE,
shiny.options = list(launch.browser = getOption("shiny.launch.browser", interactive())),
- image_name = NULL,
+ spatial = NULL,
channel = NULL,
...
)
@@ -30,7 +30,7 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.}
\item{shiny.options}{a list of shiny options (launch.browser, host, port etc.) passed \code{options} arguement of \link{shinyApp}. For more information, see \link{runApp}}
-\item{image_name}{the name/key of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{channel}{the name of the main channel}
diff --git a/man/as.AnnData.Rd b/man/as.AnnData.Rd
index bf6a808b..acb7a4ac 100644
--- a/man/as.AnnData.Rd
+++ b/man/as.AnnData.Rd
@@ -9,7 +9,7 @@ as.AnnData(
file,
assay = NULL,
flip_coordinates = FALSE,
- method = "anndata",
+ method = "anndataR",
create.ometiff = FALSE,
python.path = NULL,
...
diff --git a/man/combineChannels.Rd b/man/combineChannels.Rd
index 8b54f274..e660b8ee 100644
--- a/man/combineChannels.Rd
+++ b/man/combineChannels.Rd
@@ -14,7 +14,7 @@ combineChannels(object, ...)
\S4method{combineChannels}{VoltRon}(
object,
assay = NULL,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channels = NULL,
colors = NULL,
@@ -23,7 +23,7 @@ combineChannels(object, ...)
\S4method{combineChannels}{vrAssay}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channels = NULL,
colors = NULL,
@@ -32,7 +32,7 @@ combineChannels(object, ...)
\S4method{combineChannels}{vrAssayV2}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channels = NULL,
colors = NULL,
@@ -62,7 +62,7 @@ combineChannels(object, ...)
see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the name of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image
(\link{vrMainSpatial}) is requested}
diff --git a/man/flipCoordinates.Rd b/man/flipCoordinates.Rd
index b41c25f7..3b825318 100644
--- a/man/flipCoordinates.Rd
+++ b/man/flipCoordinates.Rd
@@ -9,17 +9,11 @@
\usage{
flipCoordinates(object, ...)
-\S4method{flipCoordinates}{VoltRon}(
- object,
- assay = NULL,
- image_name = NULL,
- spatial_name = NULL,
- ...
-)
+\S4method{flipCoordinates}{VoltRon}(object, assay = NULL, image_name = NULL, spatial = NULL, ...)
-\S4method{flipCoordinates}{vrAssay}(object, image_name = NULL, spatial_name = NULL, ...)
+\S4method{flipCoordinates}{vrAssay}(object, image_name = NULL, spatial = NULL, ...)
-\S4method{flipCoordinates}{vrAssayV2}(object, image_name = NULL, spatial_name = NULL, ...)
+\S4method{flipCoordinates}{vrAssayV2}(object, image_name = NULL, spatial = NULL, ...)
}
\arguments{
\item{object}{a VoltRon, vrAssay or vrSpatial object.}
@@ -29,9 +23,9 @@ flipCoordinates(object, ...)
\item{assay}{assay name (exp: Assay1) or assay class (exp: Visium, Xenium), see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{image_name}{(deprecated, use \code{spatial_name}) the name/key of the image}
+\item{image_name}{(deprecated, use \code{spatial}) the name/key of the image}
-\item{spatial_name}{the name/key of the spatial system associated with the coordinates}
+\item{spatial}{the name of the spatial coordinate system}
}
\description{
Flip the coordinates of spatial points in the y axis direction.
diff --git a/man/formAssay.Rd b/man/formAssay.Rd
index b1030811..0b0cc7bc 100644
--- a/man/formAssay.Rd
+++ b/man/formAssay.Rd
@@ -6,13 +6,14 @@
\usage{
formAssay(
data = NULL,
- coords,
+ coords = NULL,
segments = list(),
image = NULL,
params = list(),
type = "ROI",
name = "Assay1",
- main_image = "image_1",
+ main_spatial = "image_1",
+ main_image = NULL,
main_featureset = NULL,
assay_version = "v2",
...
@@ -33,13 +34,15 @@ formAssay(
\item{name}{the name of the assay}
-\item{main_image}{the name of the main_image}
+\item{main_spatial}{the name of the main spatial coordinate system}
+
+\item{main_image}{(deprecated) the name of the main spatial coordinate system}
\item{main_featureset}{the name of the main_featureset}
\item{assay_version}{the assay version}
-\item{...}{additional arguements passed to \link{formImage}}
+\item{...}{additional arguements passed to \link{formSpatial}}
}
\description{
Create a vrAssay (VoltRon assay) object
diff --git a/man/formImage.Rd b/man/formSpatial.Rd
similarity index 74%
rename from man/formImage.Rd
rename to man/formSpatial.Rd
index 7d6b30d2..405ee3b9 100644
--- a/man/formImage.Rd
+++ b/man/formSpatial.Rd
@@ -1,10 +1,15 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/image.R
-\name{formImage}
-\alias{formImage}
-\title{formImage}
+\name{formSpatial}
+\alias{formSpatial}
+\title{formSpatial}
\usage{
-formImage(coords, segments = list(), image = NULL, main_channel = NULL)
+formSpatial(
+ coords = NULL,
+ segments = list(),
+ image = NULL,
+ main_channel = NULL
+)
}
\arguments{
\item{coords}{the coordinates of the spatial points}
diff --git a/man/formVoltRon.Rd b/man/formVoltRon.Rd
index 348a948d..2a8002ac 100644
--- a/man/formVoltRon.Rd
+++ b/man/formVoltRon.Rd
@@ -16,6 +16,7 @@ formVoltRon(
params = list(),
sample_name = NULL,
layer_name = NULL,
+ spatial = NULL,
image_name = NULL,
feature_name = NULL,
project = NULL,
@@ -46,7 +47,9 @@ formVoltRon(
\item{layer_name}{the name of the layer}
-\item{image_name}{the name/key of the image}
+\item{spatial}{the name of the spatial coordinate system}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system}
\item{feature_name}{the name/key of the feature set}
diff --git a/man/generateSegments.Rd b/man/generateSegments.Rd
index 1829bbaf..dcb83e5c 100644
--- a/man/generateSegments.Rd
+++ b/man/generateSegments.Rd
@@ -4,10 +4,12 @@
\alias{generateSegments}
\title{generateSegments}
\usage{
-generateSegments(geojson.file)
+generateSegments(object, type = c("ROI", "cell"))
}
\arguments{
-\item{geojson.file}{the GeoJSON file, typically generated by QuPath software}
+\item{object}{an object of sf class with each element having a geometry}
+
+\item{type}{spatial entity type: ROI or cell}
}
\description{
The function to import segments from a geojson file
diff --git a/man/generateTileData.Rd b/man/generateTileData.Rd
index 0d3820e3..146d3e67 100644
--- a/man/generateTileData.Rd
+++ b/man/generateTileData.Rd
@@ -11,9 +11,9 @@ generateTileData(object, ...)
\S4method{generateTileData}{VoltRon}(object, assay = NULL, ...)
-\S4method{generateTileData}{vrAssay}(object, name = NULL, reg = FALSE, channel = NULL)
+\S4method{generateTileData}{vrAssay}(object, spatial = NULL, reg = FALSE, channel = NULL)
-\S4method{generateTileData}{vrAssayV2}(object, name = NULL, reg = FALSE, channel = NULL)
+\S4method{generateTileData}{vrAssayV2}(object, spatial = NULL, reg = FALSE, channel = NULL)
}
\arguments{
\item{object}{a VoltRon or vrAssay object.}
@@ -23,7 +23,7 @@ generateTileData(object, ...)
\item{assay}{assay name (exp: Assay1) or assay class (exp: Visium, Xenium), see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the name of the main spatial system}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main
image (\link{vrMainSpatial}) is requested}
diff --git a/man/getPCA.Rd b/man/getPCA.Rd
index 6853a9d0..678c10ed 100644
--- a/man/getPCA.Rd
+++ b/man/getPCA.Rd
@@ -1,10 +1,16 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/processing.R
+% Please edit documentation in R/allgenerics.R, R/processing.R
\name{getPCA}
\alias{getPCA}
+\alias{getPCA,data_matrix-method}
+\alias{getPCA,VoltRon-method}
\title{getPCA}
\usage{
-getPCA(
+getPCA(object, ...)
+
+\S4method{getPCA}{data_matrix}(object, dims = 30, n.workers = 1, data.type = "norm", seed = 1)
+
+\S4method{getPCA}{VoltRon}(
object,
assay = NULL,
features = NULL,
@@ -20,6 +26,17 @@ getPCA(
\arguments{
\item{object}{a VoltRon object}
+\item{...}{arguments passed to other methods.}
+
+\item{dims}{the number of dimensions extracted from PCA}
+
+\item{n.workers}{the number of cores/workers use for parallelization.}
+
+\item{data.type}{the type of data used to calculate PCA from:
+"norm" (default), "raw" or an existing embeddings \link{vrEmbeddingNames}.}
+
+\item{seed}{seed}
+
\item{assay}{assay name (exp: Assay1) or assay class
(exp: Visium, Xenium), see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
@@ -28,19 +45,10 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.}
\item{feat_type}{the feature set type}
-\item{data.type}{the type of data used to calculate PCA from:
-"norm" (default), "raw" or an existing embeddings \link{vrEmbeddingNames}.}
-
-\item{dims}{the number of dimensions extracted from PCA}
-
\item{pca.key}{the key name for the embedding, default: pca}
-\item{n.workers}{the number of cores/workers use for parallelization.}
-
\item{overwrite}{Whether the existing embedding with name 'type'
should be overwritten in \link{vrEmbeddings}}
-
-\item{seed}{seed}
}
\description{
calculate PCA of the VoltRon objects
diff --git a/man/getRcppAutomatedRegistration.Rd b/man/getRcppAutomatedRegistration.Rd
index 4b86c88b..96a65a56 100644
--- a/man/getRcppAutomatedRegistration.Rd
+++ b/man/getRcppAutomatedRegistration.Rd
@@ -17,7 +17,8 @@ getRcppAutomatedRegistration(
rotate_ref = "0",
matcher = "FLANN",
method = "Homography",
- nonrigid = "TPS (OpenCV)"
+ nonrigid = "TPS (OpenCV)",
+ compute_matte_map = TRUE
)
}
\arguments{
@@ -46,6 +47,8 @@ getRcppAutomatedRegistration(
\item{method}{the automated registration method, Homography or Homography+TPS}
\item{nonrigid}{the non-rigid registration method, "TPS (OpenCV)" or "BSpline (SimpleITK)"}
+
+\item{compute_matte_map}{Should matte map be computed ?}
}
\description{
Automated registration workflos with Rcpp
diff --git a/man/getRcppManualRegistration.Rd b/man/getRcppManualRegistration.Rd
index 9cb3fc8a..090e249f 100644
--- a/man/getRcppManualRegistration.Rd
+++ b/man/getRcppManualRegistration.Rd
@@ -9,8 +9,11 @@ getRcppManualRegistration(
ref_image,
query_landmark,
reference_landmark,
+ invert_query = FALSE,
+ invert_ref = FALSE,
method = "Homography",
- nonrigid = "TPS (OpenCV)"
+ nonrigid = "TPS (OpenCV)",
+ compute_matte_map = TRUE
)
}
\arguments{
@@ -22,9 +25,15 @@ getRcppManualRegistration(
\item{reference_landmark}{refernece landmark points}
+\item{invert_query}{invert query image}
+
+\item{invert_ref}{invert reference image}
+
\item{method}{the automated registration method, either TPS or Homography+TPS}
\item{nonrigid}{the non-rigid registration method, "TPS (OpenCV)" or "BSpline (SimpleITK)"}
+
+\item{compute_matte_map}{Should matte map be computed ?}
}
\description{
Manual registration workflow with Rcpp
diff --git a/man/importCosMx.Rd b/man/importCosMx.Rd
index 14289528..2ce4172a 100644
--- a/man/importCosMx.Rd
+++ b/man/importCosMx.Rd
@@ -8,7 +8,8 @@ importCosMx(
path,
assay_name = "CosMx",
image = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
import_molecules = FALSE,
verbose = TRUE,
method = "CSV",
@@ -23,7 +24,9 @@ importCosMx(
\item{image}{the reference morphology image of the CosMx assay}
-\item{image_name}{the image name of the CosMx assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{import_molecules}{if TRUE, molecule assay will be created along
with cell assay.}
diff --git a/man/importDBITSeq.Rd b/man/importDBITSeq.Rd
index 832fa225..0a39112f 100644
--- a/man/importDBITSeq.Rd
+++ b/man/importDBITSeq.Rd
@@ -10,7 +10,8 @@ importDBITSeq(
size = 10,
assay_name = "DBIT-Seq",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "H&E",
...
)
@@ -26,7 +27,9 @@ importDBITSeq(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Visium assay, Default: H&E}
diff --git a/man/importGenePS.Rd b/man/importGenePS.Rd
index cc3e34fe..ee6073a2 100644
--- a/man/importGenePS.Rd
+++ b/man/importGenePS.Rd
@@ -10,7 +10,8 @@ importGenePS(
sample_name = NULL,
use_image = TRUE,
resolution_level = 7,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "DAPI",
import_molecules = FALSE,
verbose = TRUE,
@@ -28,7 +29,9 @@ importGenePS(
\item{resolution_level}{the level of resolution within TIFF image. Default: 7 (971x638)}
-\item{image_name}{the image name of the Xenium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Xenium assay, Default: DAPI}
diff --git a/man/importGeoMx.Rd b/man/importGeoMx.Rd
index c2758af6..2c12dcef 100644
--- a/man/importGeoMx.Rd
+++ b/man/importGeoMx.Rd
@@ -14,7 +14,8 @@ importGeoMx(
segment_polygons = FALSE,
ome.tiff = NULL,
resolution_level = 3,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
verbose = TRUE,
...
)
@@ -43,7 +44,9 @@ OME.TIFF file}
\item{resolution_level}{the level of resolution within GeoMx OME-TIFF
image, Default: 3}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{verbose}{verbose}
diff --git a/man/importImageData.Rd b/man/importImageData.Rd
index c6785459..3fa9d03d 100644
--- a/man/importImageData.Rd
+++ b/man/importImageData.Rd
@@ -8,7 +8,8 @@ importImageData(
image,
tile.size = 10,
segments = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channels = NULL,
series = 1,
resolution = NULL,
@@ -21,10 +22,12 @@ importImageData(
\item{tile.size}{the size of tiles}
-\item{segments}{Either a list of segments or a GeoJSON file. This will
-result in a second assay in the VoltRon object to be created}
+\item{segments}{Either a list of segments. This will result in a second
+assay in the VoltRon object to be created}
-\item{image_name}{the image name of the Image assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channels}{the channel names of the images if multiple
images are provided}
@@ -46,12 +49,12 @@ import an image as VoltRon object
\examples{
# single image
imgfile <- system.file("extdata", "DAPI.tif", package = "VoltRon")
-vrdata <- importImageData(imgfile, image_name = "main")
+vrdata <- importImageData(imgfile, spatial = "main")
# multiple images
imgfile <- c(system.file("extdata", "DAPI.tif", package = "VoltRon"),
system.file("extdata", "DAPI.tif", package = "VoltRon"))
-vrdata <- importImageData(imgfile, image_name = "main",
+vrdata <- importImageData(imgfile, spatial = "main",
channels = c("DAPI", "DAPI2"))
}
diff --git a/man/importOpenST.Rd b/man/importOpenST.Rd
index fb46f3d7..94b94d60 100644
--- a/man/importOpenST.Rd
+++ b/man/importOpenST.Rd
@@ -8,7 +8,8 @@ importOpenST(
h5ad.path,
assay_name = "OpenST",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "H&E",
verbose = TRUE,
...
@@ -21,7 +22,9 @@ importOpenST(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Visium assay, Default: H&E}
diff --git a/man/importPhenoCycler.Rd b/man/importPhenoCycler.Rd
index b8d0e814..1a201bd2 100644
--- a/man/importPhenoCycler.Rd
+++ b/man/importPhenoCycler.Rd
@@ -8,7 +8,8 @@ importPhenoCycler(
dir.path,
assay_name = "PhenoCycler",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
type = c("inform", "processor", "qupath"),
filter = "DAPI|Blank|Empty",
inform.quant = c("mean", "total", "min", "max", "std"),
@@ -23,7 +24,9 @@ importPhenoCycler(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Xenium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{type}{Specify which type matrix is being provided.
\itemize{
diff --git a/man/importQuPathIF.Rd b/man/importQuPathIF.Rd
index 8171654d..358e96e2 100644
--- a/man/importQuPathIF.Rd
+++ b/man/importQuPathIF.Rd
@@ -8,7 +8,8 @@ importQuPathIF(
measurements,
image,
segments,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channels = NULL,
series = 1,
resolution = NULL,
@@ -20,10 +21,12 @@ importQuPathIF(
\item{image}{a single or a list of image paths or magick-image objects}
-\item{segments}{Either a list of segments or a GeoJSON file. This will
-result in a second assay in the VoltRon object to be created}
+\item{segments}{Either a list of segments. This will result in a second
+assay in the VoltRon object to be created}
-\item{image_name}{the image name of the Image assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channels}{the channel names of the images if multiple
images are provided}
diff --git a/man/importSTOmics.Rd b/man/importSTOmics.Rd
index 42f91354..c73d217d 100644
--- a/man/importSTOmics.Rd
+++ b/man/importSTOmics.Rd
@@ -8,7 +8,8 @@ importSTOmics(
h5ad.path,
assay_name = "STOmics",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "H&E",
...
)
@@ -20,7 +21,9 @@ importSTOmics(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Visium assay, Default: H&E}
diff --git a/man/importVisium.Rd b/man/importVisium.Rd
index e053abf4..65634fec 100644
--- a/man/importVisium.Rd
+++ b/man/importVisium.Rd
@@ -9,7 +9,8 @@ importVisium(
selected_assay = "Gene Expression",
assay_name = "Visium",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "H&E",
inTissue = TRUE,
resolution_level = "lowres",
@@ -25,7 +26,9 @@ importVisium(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Visium assay,
Default: H&E}
diff --git a/man/importVisiumHD.Rd b/man/importVisiumHD.Rd
index 6211d319..bfa54dbb 100644
--- a/man/importVisiumHD.Rd
+++ b/man/importVisiumHD.Rd
@@ -10,7 +10,8 @@ importVisiumHD(
selected_assay = "Gene Expression",
assay_name = "VisiumHD",
sample_name = NULL,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "H&E",
inTissue = TRUE,
resolution_level = "lowres",
@@ -28,7 +29,9 @@ importVisiumHD(
\item{sample_name}{the name of the sample}
-\item{image_name}{the image name of the Visium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Visium assay,
Default: H&E}
diff --git a/man/importXenium.Rd b/man/importXenium.Rd
index 8e31d24c..8c26642e 100644
--- a/man/importXenium.Rd
+++ b/man/importXenium.Rd
@@ -13,7 +13,8 @@ importXenium(
morphology_image = "morphology_lowres.tif",
resolution_level = 7,
overwrite_resolution = TRUE,
- image_name = "main",
+ spatial = "main",
+ image_name = NULL,
channel_name = "DAPI",
import_molecules = FALSE,
verbose = TRUE,
@@ -40,7 +41,9 @@ image, see \link{generateXeniumImage}. Default: 7 (553x402)}
\item{overwrite_resolution}{if TRUE, the image "file.name" will be generated
again although it exists at "dir.path"}
-\item{image_name}{the image name of the Xenium assay, Default: main}
+\item{spatial}{the name of the spatial coordinate system, Default: main}
+
+\item{image_name}{(deprecated) the name of the spatial coordinate system, Default: main}
\item{channel_name}{the channel name of the image of the Xenium assay,
Default: DAPI}
diff --git a/man/modulateImage.Rd b/man/modulateImage.Rd
index 91e7ff4c..abcbb83e 100644
--- a/man/modulateImage.Rd
+++ b/man/modulateImage.Rd
@@ -14,7 +14,7 @@ modulateImage(object, ...)
\S4method{modulateImage}{VoltRon}(
object,
assay = NULL,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
brightness = 100,
@@ -25,7 +25,7 @@ modulateImage(object, ...)
\S4method{modulateImage}{vrAssay}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
brightness = 100,
@@ -36,7 +36,7 @@ modulateImage(object, ...)
\S4method{modulateImage}{vrAssayV2}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
brightness = 100,
@@ -72,7 +72,7 @@ modulateImage(object, ...)
see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the name of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image
(\link{vrMainSpatial}) is requested}
diff --git a/man/normalizeData.Rd b/man/normalizeData.Rd
index e3bfaa6d..8ab35a91 100644
--- a/man/normalizeData.Rd
+++ b/man/normalizeData.Rd
@@ -2,21 +2,20 @@
% Please edit documentation in R/allgenerics.R, R/processing.R
\name{normalizeData}
\alias{normalizeData}
-\alias{normalizeData,VoltRon-method}
+\alias{normalizeData,data_matrix-method}
\alias{normalizeData,vrAssay-method}
\alias{normalizeData,vrAssayV2-method}
+\alias{normalizeData,VoltRon-method}
\title{Normalize Data}
\usage{
normalizeData(object, ...)
-\S4method{normalizeData}{VoltRon}(
+\S4method{normalizeData}{data_matrix}(
object,
- assay = NULL,
method = "LogNorm",
desiredQuantile = 0.9,
scale = 0.2,
- sizefactor = 10000,
- feat_type = NULL
+ sizefactor = 10000
)
\S4method{normalizeData}{vrAssay}(
@@ -36,15 +35,22 @@ normalizeData(object, ...)
sizefactor = 10000,
feat_type = NULL
)
+
+\S4method{normalizeData}{VoltRon}(
+ object,
+ assay = NULL,
+ method = "LogNorm",
+ desiredQuantile = 0.9,
+ scale = 0.2,
+ sizefactor = 10000,
+ feat_type = NULL
+)
}
\arguments{
\item{object}{a VoltRon or vrAssay object.}
\item{...}{arguments passed to other methods.}
-\item{assay}{assay name (exp: Assay1) or assay class
-if NULL, the default assay will be used, see \link{vrMainAssay}.}
-
\item{method}{the normalization method: "LogNorm",
"Q3Norm", "LogQ3Norm", "CLR" or "hyper.arcsine".}
@@ -56,6 +62,9 @@ or "LogQuanNorm" is selected as \code{method}.}
\item{sizefactor}{size factor if \code{method} is selected as \code{LogNorm}}
\item{feat_type}{the feature set type}
+
+\item{assay}{assay name (exp: Assay1) or assay class
+if NULL, the default assay will be used, see \link{vrMainAssay}.}
}
\description{
Given a VoltRon or vrAssay object, normalize the raw count data.
diff --git a/man/resizeImage.Rd b/man/resizeImage.Rd
index a36aeb9f..cce40644 100644
--- a/man/resizeImage.Rd
+++ b/man/resizeImage.Rd
@@ -11,11 +11,11 @@
\usage{
resizeImage(object, ...)
-\S4method{resizeImage}{VoltRon}(object, assay = NULL, name = NULL, reg = FALSE, size = NULL)
+\S4method{resizeImage}{VoltRon}(object, assay = NULL, spatial = NULL, reg = FALSE, size = NULL)
-\S4method{resizeImage}{vrAssay}(object, name = NULL, reg = FALSE, size = NULL)
+\S4method{resizeImage}{vrAssay}(object, spatial = NULL, reg = FALSE, size = NULL)
-\S4method{resizeImage}{vrAssayV2}(object, name = NULL, reg = FALSE, size = NULL)
+\S4method{resizeImage}{vrAssayV2}(object, spatial = NULL, reg = FALSE, size = NULL)
\S4method{resizeImage}{vrImage}(object, size = NULL)
@@ -30,7 +30,7 @@ resizeImage(object, ...)
see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the name of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image
(\link{vrMainSpatial}) is requested}
diff --git a/man/transferData.Rd b/man/transferData.Rd
index 2c609f92..034c3715 100644
--- a/man/transferData.Rd
+++ b/man/transferData.Rd
@@ -1,10 +1,15 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/integration.R
+% Please edit documentation in R/allgenerics.R, R/integration.R
\name{transferData}
\alias{transferData}
+\alias{transferData,VoltRon,character-method}
+\alias{transferData,VoltRon,Seurat-method}
+\alias{transferData,VoltRon,SingleCellExperiment-method}
\title{transferData}
\usage{
-transferData(
+transferData(object, from, ...)
+
+\S4method{transferData}{VoltRon,character}(
object,
from = NULL,
to = NULL,
@@ -12,16 +17,42 @@ transferData(
expand = NULL,
new_feature_name = NULL
)
+
+\S4method{transferData}{VoltRon,Seurat}(
+ object,
+ from,
+ to = NULL,
+ features = NULL,
+ new_feature_name = NULL,
+ sc.assay = NULL,
+ pc.dims = 30,
+ seed = 1
+)
+
+\S4method{transferData}{VoltRon,SingleCellExperiment}(
+ object,
+ from,
+ to = NULL,
+ features = NULL,
+ new_feature_name = NULL,
+ sc.assay = NULL,
+ pc.dims = 30,
+ seed = 1
+)
}
\arguments{
\item{object}{a VoltRon object}
-\item{from}{the name or class of assay whose data transfered to the second assay}
+\item{from}{if a character, the name of the assay whose data transfered to the second assay,
+otherwise a single cell object of class Seurat or SingleCellExperiment whose data is
+transfered to the VoltRon assay provided in \code{to}.}
+
+\item{...}{arguments passed to other methods.}
\item{to}{the name or class of target assay where data is transfered to}
-\item{features}{the set of features from \link{vrFeatures} or metadata columns from \link{Metadata} that are transferred.
-Only one metadata feature can be transfered at a time.}
+\item{features}{the set of features from \link{vrFeatures} or metadata columns from \link{Metadata}
+that are transferred. Only one metadata feature can be transferred at a time.}
\item{expand}{if TRUE, metadata features will be transformed into
dummy features where each category in the feature will be a new feature.
@@ -30,8 +61,23 @@ metadata columns, else the decision will be made automatically.}
\item{new_feature_name}{the name of the new feature set created
from the source assay defined in \code{from} argument.
-Only used when a new assay in created.}
+Only used when a new assay in created. If a \code{features} is a metadata
+column and \code{from} is a single cell object of Seurat or
+SingleCellExperiment, then \code{new_feature_name} is the name of the new
+metadata column in the VoltRon object.}
+
+\item{sc.assay}{When \code{from} is a single cell object (Seurat or
+SingleCellExperiment), sc.assay can be provided to choose a specific assay
+from the single cell object.}
+
+\item{pc.dims}{the number of dimensions extracted from PCA. Only used
+when \code{sc.object} is a Seurat or SingleCellExperiment object.}
+
+\item{seed}{seed}
}
\description{
-transfer data across assays
+Transfering features across spatial omics assays and single cell assays (Seurat or SingleCellExperiment).
+This functions facilitates the transfer of features between both (i) VoltRon assays in a common
+coordinate system and tissue block, and also (ii) features between a VoltRon assay and a single cell assay
+with Seurat or SingleCellExperiment object.
}
diff --git a/man/vrCoordinates.Rd b/man/vrCoordinates.Rd
index 240eccdb..faccf323 100644
--- a/man/vrCoordinates.Rd
+++ b/man/vrCoordinates.Rd
@@ -22,23 +22,29 @@ vrCoordinates(object, ...)
object,
assay = NULL,
image_name = NULL,
- spatial_name = NULL,
+ spatial = NULL,
reg = FALSE
)
-\S4method{vrCoordinates}{vrAssay}(object, image_name = NULL, spatial_name = NULL, reg = FALSE)
+\S4method{vrCoordinates}{vrAssay}(object, image_name = NULL, spatial = NULL, reg = FALSE)
-\S4method{vrCoordinates}{vrAssayV2}(object, image_name = NULL, spatial_name = NULL, reg = FALSE)
+\S4method{vrCoordinates}{vrAssayV2}(object, image_name = NULL, spatial = NULL, reg = FALSE)
\S4method{vrCoordinates}{vrImage}(object)
\S4method{vrCoordinates}{vrSpatial}(object)
-\S4method{vrCoordinates}{VoltRon}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrCoordinates}{VoltRon}(
+ object,
+ assay = NULL,
+ image_name = NULL,
+ spatial = NULL,
+ reg = FALSE
+) <- value
-\S4method{vrCoordinates}{vrAssay}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrCoordinates}{vrAssay}(object, image_name = NULL, spatial = NULL, reg = FALSE) <- value
-\S4method{vrCoordinates}{vrAssayV2}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrCoordinates}{vrAssayV2}(object, image_name = NULL, spatial = NULL, reg = FALSE) <- value
\S4method{vrCoordinates}{vrImage}(object) <- value
@@ -54,9 +60,9 @@ vrCoordinates(object, ...) <- value
\item{assay}{assay name (exp: Assay1) or assay class (exp: Visium, Xenium), see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{image_name}{(deprecated, use \code{spatial_name}) the name/key of the image associated with the coordinates}
+\item{image_name}{(deprecated, use \code{spatial}) the name/key of the image associated with the coordinates}
-\item{spatial_name}{the name/key of the spatial system associated with the coordinates}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image (\link{vrMainImage}) is requested}
diff --git a/man/vrHeatmapPlot.Rd b/man/vrHeatmapPlot.Rd
index 8b2a1295..a74a8305 100644
--- a/man/vrHeatmapPlot.Rd
+++ b/man/vrHeatmapPlot.Rd
@@ -18,6 +18,7 @@ vrHeatmapPlot(
highlight.some = FALSE,
n_highlight = 30,
font.size = 13.2,
+ col,
...
)
}
@@ -56,6 +57,8 @@ if \code{show_row_names} is TRUE}
\item{font.size}{font size}
+\item{col}{color palette to be used for the heatmap.}
+
\item{...}{additional parameters passed to \link{getVariableFeatures}}
}
\description{
diff --git a/man/vrImageChannelNames.Rd b/man/vrImageChannelNames.Rd
index aa77f35f..37420135 100644
--- a/man/vrImageChannelNames.Rd
+++ b/man/vrImageChannelNames.Rd
@@ -11,11 +11,11 @@
\usage{
vrImageChannelNames(object, ...)
-\S4method{vrImageChannelNames}{VoltRon}(object, assay = NULL)
+\S4method{vrImageChannelNames}{VoltRon}(object, assay = NULL, ...)
-\S4method{vrImageChannelNames}{vrAssay}(object, name = NULL)
+\S4method{vrImageChannelNames}{vrAssay}(object, spatial = NULL, ...)
-\S4method{vrImageChannelNames}{vrAssayV2}(object, name = NULL)
+\S4method{vrImageChannelNames}{vrAssayV2}(object, spatial = NULL, ...)
\S4method{vrImageChannelNames}{vrImage}(object, return.report = TRUE)
@@ -30,7 +30,7 @@ vrImageChannelNames(object, ...)
see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the key of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{return.report}{if TRUE and no image is present, return a character
stating that there is no image}
diff --git a/man/vrImages.Rd b/man/vrImages.Rd
index 9b78fc95..82b0299b 100644
--- a/man/vrImages.Rd
+++ b/man/vrImages.Rd
@@ -19,7 +19,7 @@ vrImages(object, ...)
\S4method{vrImages}{VoltRon}(
object,
assay = NULL,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
as.raster = FALSE,
@@ -28,7 +28,7 @@ vrImages(object, ...)
\S4method{vrImages}{vrAssay}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
as.raster = FALSE,
@@ -37,7 +37,7 @@ vrImages(object, ...)
\S4method{vrImages}{vrAssayV2}(
object,
- name = NULL,
+ spatial = NULL,
reg = FALSE,
channel = NULL,
as.raster = FALSE,
@@ -48,9 +48,9 @@ vrImages(object, ...)
\S4method{vrImages}{vrSpatial}(object, channel = NULL, as.raster = FALSE, scale.perc = 100)
-\S4method{vrImages}{vrAssay}(object, name = NULL, channel = NULL, reg = FALSE) <- value
+\S4method{vrImages}{vrAssay}(object, spatial = NULL, channel = NULL, reg = FALSE) <- value
-\S4method{vrImages}{vrAssayV2}(object, name = NULL, channel = NULL, reg = FALSE) <- value
+\S4method{vrImages}{vrAssayV2}(object, spatial = NULL, channel = NULL, reg = FALSE) <- value
\S4method{vrImages}{vrImage}(object, channel = NULL) <- value
@@ -67,7 +67,7 @@ vrImages(object, ...) <- value
see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{name}{the name of the main spatial system}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image
(\link{vrMainSpatial}) is requested}
diff --git a/man/vrLayer-methods.Rd b/man/vrLayer-methods.Rd
index 90baad9d..e32e56e4 100644
--- a/man/vrLayer-methods.Rd
+++ b/man/vrLayer-methods.Rd
@@ -23,9 +23,9 @@ packages
}
\section{Functions}{
\itemize{
-\item \code{x[[i}: Accessing vrAssay objects from \code{vrLayer} objects
+\item \code{x = vrLayer[[i = character, j = ANY]]}: Accessing vrAssay objects from \code{vrLayer} objects
-\item \code{`[[`(x = vrLayer, i = character, j = ANY) <- value}: Overwriting vrAssay objects from \code{vrLayer} objects
+\item \code{x = vrLayer[[i = character, j = ANY]] <- value}: Overwriting vrAssay objects from \code{vrLayer} objects
}}
\concept{vrlayer}
diff --git a/man/vrMainChannel.Rd b/man/vrMainChannel.Rd
index cba0f0ef..d7b5a3bc 100644
--- a/man/vrMainChannel.Rd
+++ b/man/vrMainChannel.Rd
@@ -15,17 +15,17 @@
\usage{
vrMainChannel(object, ...)
-\S4method{vrMainChannel}{vrAssay}(object, name = NULL)
+\S4method{vrMainChannel}{vrAssay}(object, spatial = NULL)
-\S4method{vrMainChannel}{vrAssayV2}(object, name = NULL)
+\S4method{vrMainChannel}{vrAssayV2}(object, spatial = NULL)
\S4method{vrMainChannel}{vrImage}(object)
\S4method{vrMainChannel}{vrSpatial}(object)
-\S4method{vrMainChannel}{vrAssay}(object, name = NULL) <- value
+\S4method{vrMainChannel}{vrAssay}(object, spatial = NULL) <- value
-\S4method{vrMainChannel}{vrAssayV2}(object, name = NULL) <- value
+\S4method{vrMainChannel}{vrAssayV2}(object, spatial = NULL) <- value
\S4method{vrMainChannel}{vrImage}(object) <- value
@@ -38,7 +38,7 @@ vrMainChannel(object, ...) <- value
\item{...}{arguments passed to other methods.}
-\item{name}{the name of the image}
+\item{spatial}{the name of the spatial coordinate system}
\item{value}{the name of main channel of the spatial system}
}
diff --git a/man/vrSample-methods.Rd b/man/vrSample-methods.Rd
index 7ffb9b88..dc2cb366 100644
--- a/man/vrSample-methods.Rd
+++ b/man/vrSample-methods.Rd
@@ -29,13 +29,13 @@ packages
}
\section{Functions}{
\itemize{
-\item \code{x[[i}: Accessing vrLayer objects from \code{vrSample} objects
+\item \code{x = vrSample[[i = character, j = ANY]]}: Accessing vrLayer objects from \code{vrSample} objects
-\item \code{`[[`(x = vrSample, i = character, j = ANY) <- value}: Accessing vrLayer objects from \code{vrSample} objects
+\item \code{x = vrSample[[i = character, j = ANY]] <- value}: Accessing vrLayer objects from \code{vrSample} objects
-\item \code{x[[i}: (deprecated) Accessing vrLayer objects from \code{vrBlock} objects
+\item \code{x = vrBlock[[i = character, j = ANY]]}: (deprecated) Accessing vrLayer objects from \code{vrBlock} objects
-\item \code{`[[`(x = vrBlock, i = character, j = ANY) <- value}: (deprecated) Overwriting vrLayer objects from \code{vrBlock} objects
+\item \code{x = vrBlock[[i = character, j = ANY]] <- value}: (deprecated) Overwriting vrLayer objects from \code{vrBlock} objects
}}
\concept{vrsample}
diff --git a/man/vrSegments.Rd b/man/vrSegments.Rd
index d9df7189..ce67f07b 100644
--- a/man/vrSegments.Rd
+++ b/man/vrSegments.Rd
@@ -22,24 +22,30 @@ vrSegments(object, ...)
object,
assay = NULL,
image_name = NULL,
- spatial_name = NULL,
+ spatial = NULL,
reg = FALSE,
as.data.frame = FALSE
)
-\S4method{vrSegments}{vrAssay}(object, image_name = NULL, spatial_name = NULL, reg = FALSE)
+\S4method{vrSegments}{vrAssay}(object, image_name = NULL, spatial = NULL, reg = FALSE)
-\S4method{vrSegments}{vrAssayV2}(object, image_name = NULL, spatial_name = NULL, reg = FALSE)
+\S4method{vrSegments}{vrAssayV2}(object, image_name = NULL, spatial = NULL, reg = FALSE)
\S4method{vrSegments}{vrImage}(object)
\S4method{vrSegments}{vrSpatial}(object)
-\S4method{vrSegments}{VoltRon}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrSegments}{VoltRon}(
+ object,
+ assay = NULL,
+ image_name = NULL,
+ spatial = NULL,
+ reg = FALSE
+) <- value
-\S4method{vrSegments}{vrAssay}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrSegments}{vrAssay}(object, image_name = NULL, spatial = NULL, reg = FALSE) <- value
-\S4method{vrSegments}{vrAssayV2}(object, image_name = NULL, spatial_name = NULL, reg = FALSE) <- value
+\S4method{vrSegments}{vrAssayV2}(object, image_name = NULL, spatial = NULL, reg = FALSE) <- value
\S4method{vrSegments}{vrImage}(object) <- value
@@ -55,9 +61,9 @@ vrSegments(object, ...) <- value
\item{assay}{assay name (exp: Assay1) or assay class (exp: Visium, Xenium), see \link{SampleMetadata}.
if NULL, the default assay will be used, see \link{vrMainAssay}.}
-\item{image_name}{(deprecated, use \code{spatial_name}) the name/key of the image associated with the coordinates}
+\item{image_name}{(deprecated, use \code{spatial}) the name/key of the image associated with the coordinates}
-\item{spatial_name}{the name/key of the spatial system associated with the coordinates}
+\item{spatial}{the name of the spatial coordinate system}
\item{reg}{TRUE if registered coordinates of the main image (\link{vrMainImage}) is requested}
diff --git a/man/vrSpatialFeaturePlot.Rd b/man/vrSpatialFeaturePlot.Rd
index 8a45c519..21410037 100644
--- a/man/vrSpatialFeaturePlot.Rd
+++ b/man/vrSpatialFeaturePlot.Rd
@@ -77,7 +77,7 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.}
\item{label}{if TRUE, labels of ROIs will be visualized too}
-\item{spatial}{the name of the main spatial system}
+\item{spatial}{the name of the spatial coordinate system}
\item{channel}{the name of the channel associated with the image}
diff --git a/man/vrSpatialPlot.Rd b/man/vrSpatialPlot.Rd
index 18a4351d..9d2615b3 100644
--- a/man/vrSpatialPlot.Rd
+++ b/man/vrSpatialPlot.Rd
@@ -76,7 +76,7 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.}
\item{label}{if TRUE, the labels of the ROI assays will be visualized}
-\item{spatial}{the name of the main spatial system}
+\item{spatial}{the name of the spatial coordinate system}
\item{channel}{the name of the channel associated with the image}
diff --git a/man/warpSimpleITKImage.Rd b/man/warpSimpleITKImage.Rd
index 03bbddd9..68fd3d83 100644
--- a/man/warpSimpleITKImage.Rd
+++ b/man/warpSimpleITKImage.Rd
@@ -4,11 +4,9 @@
\alias{warpSimpleITKImage}
\title{getRcppWarpImage}
\usage{
-warpSimpleITKImage(ref_image, query_image, mapping)
+warpSimpleITKImage(query_image, mapping)
}
\arguments{
-\item{ref_image}{reference image}
-
\item{query_image}{query image}
\item{mapping}{a list of the homography matrices and TPS keypoints}
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index 069b569b..20f8e9c6 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -11,9 +11,27 @@ Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get();
Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
#endif
+// accuracy_rawvector
+Rcpp::List accuracy_rawvector(Rcpp::RawVector& ref_image, Rcpp::RawVector& query_image, Rcpp::RawVector& mask, const int width, const int height, std::string type, bool overlay_images, const bool compute_matte_map);
+RcppExport SEXP _VoltRon_accuracy_rawvector(SEXP ref_imageSEXP, SEXP query_imageSEXP, SEXP maskSEXP, SEXP widthSEXP, SEXP heightSEXP, SEXP typeSEXP, SEXP overlay_imagesSEXP, SEXP compute_matte_mapSEXP) {
+BEGIN_RCPP
+ Rcpp::RObject rcpp_result_gen;
+ Rcpp::RNGScope rcpp_rngScope_gen;
+ Rcpp::traits::input_parameter< Rcpp::RawVector& >::type ref_image(ref_imageSEXP);
+ Rcpp::traits::input_parameter< Rcpp::RawVector& >::type query_image(query_imageSEXP);
+ Rcpp::traits::input_parameter< Rcpp::RawVector& >::type mask(maskSEXP);
+ Rcpp::traits::input_parameter< const int >::type width(widthSEXP);
+ Rcpp::traits::input_parameter< const int >::type height(heightSEXP);
+ Rcpp::traits::input_parameter< std::string >::type type(typeSEXP);
+ Rcpp::traits::input_parameter< bool >::type overlay_images(overlay_imagesSEXP);
+ Rcpp::traits::input_parameter< const bool >::type compute_matte_map(compute_matte_mapSEXP);
+ rcpp_result_gen = Rcpp::wrap(accuracy_rawvector(ref_image, query_image, mask, width, height, type, overlay_images, compute_matte_map));
+ return rcpp_result_gen;
+END_RCPP
+}
// automated_registeration_rawvector
-Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::RawVector& query_image, const int width1, const int height1, const int width2, const int height2, const float GOOD_MATCH_PERCENT, const int MAX_FEATURES, const bool invert_query, const bool invert_ref, Rcpp::String flipflop_query, Rcpp::String flipflop_ref, Rcpp::String rotate_query, Rcpp::String rotate_ref, Rcpp::String matcher, Rcpp::String method, Rcpp::String nonrigid);
-RcppExport SEXP _VoltRon_automated_registeration_rawvector(SEXP ref_imageSEXP, SEXP query_imageSEXP, SEXP width1SEXP, SEXP height1SEXP, SEXP width2SEXP, SEXP height2SEXP, SEXP GOOD_MATCH_PERCENTSEXP, SEXP MAX_FEATURESSEXP, SEXP invert_querySEXP, SEXP invert_refSEXP, SEXP flipflop_querySEXP, SEXP flipflop_refSEXP, SEXP rotate_querySEXP, SEXP rotate_refSEXP, SEXP matcherSEXP, SEXP methodSEXP, SEXP nonrigidSEXP) {
+Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::RawVector& query_image, const int width1, const int height1, const int width2, const int height2, const float GOOD_MATCH_PERCENT, const int MAX_FEATURES, const bool invert_query, const bool invert_ref, Rcpp::String flipflop_query, Rcpp::String flipflop_ref, Rcpp::String rotate_query, Rcpp::String rotate_ref, Rcpp::String matcher, Rcpp::String method, Rcpp::String nonrigid, const bool compute_matte_map);
+RcppExport SEXP _VoltRon_automated_registeration_rawvector(SEXP ref_imageSEXP, SEXP query_imageSEXP, SEXP width1SEXP, SEXP height1SEXP, SEXP width2SEXP, SEXP height2SEXP, SEXP GOOD_MATCH_PERCENTSEXP, SEXP MAX_FEATURESSEXP, SEXP invert_querySEXP, SEXP invert_refSEXP, SEXP flipflop_querySEXP, SEXP flipflop_refSEXP, SEXP rotate_querySEXP, SEXP rotate_refSEXP, SEXP matcherSEXP, SEXP methodSEXP, SEXP nonrigidSEXP, SEXP compute_matte_mapSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
@@ -34,7 +52,8 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< Rcpp::String >::type matcher(matcherSEXP);
Rcpp::traits::input_parameter< Rcpp::String >::type method(methodSEXP);
Rcpp::traits::input_parameter< Rcpp::String >::type nonrigid(nonrigidSEXP);
- rcpp_result_gen = Rcpp::wrap(automated_registeration_rawvector(ref_image, query_image, width1, height1, width2, height2, GOOD_MATCH_PERCENT, MAX_FEATURES, invert_query, invert_ref, flipflop_query, flipflop_ref, rotate_query, rotate_ref, matcher, method, nonrigid));
+ Rcpp::traits::input_parameter< const bool >::type compute_matte_map(compute_matte_mapSEXP);
+ rcpp_result_gen = Rcpp::wrap(automated_registeration_rawvector(ref_image, query_image, width1, height1, width2, height2, GOOD_MATCH_PERCENT, MAX_FEATURES, invert_query, invert_ref, flipflop_query, flipflop_ref, rotate_query, rotate_ref, matcher, method, nonrigid, compute_matte_map));
return rcpp_result_gen;
END_RCPP
}
@@ -101,8 +120,8 @@ BEGIN_RCPP
END_RCPP
}
// manual_registeration_rawvector
-Rcpp::List manual_registeration_rawvector(Rcpp::RawVector ref_image, Rcpp::RawVector query_image, Rcpp::NumericMatrix reference_landmark, Rcpp::NumericMatrix query_landmark, const int width1, const int height1, const int width2, const int height2, Rcpp::String method, Rcpp::String nonrigid);
-RcppExport SEXP _VoltRon_manual_registeration_rawvector(SEXP ref_imageSEXP, SEXP query_imageSEXP, SEXP reference_landmarkSEXP, SEXP query_landmarkSEXP, SEXP width1SEXP, SEXP height1SEXP, SEXP width2SEXP, SEXP height2SEXP, SEXP methodSEXP, SEXP nonrigidSEXP) {
+Rcpp::List manual_registeration_rawvector(Rcpp::RawVector ref_image, Rcpp::RawVector query_image, Rcpp::NumericMatrix reference_landmark, Rcpp::NumericMatrix query_landmark, const int width1, const int height1, const int width2, const int height2, const bool invert_query, const bool invert_ref, Rcpp::String method, Rcpp::String nonrigid, const bool compute_matte_map);
+RcppExport SEXP _VoltRon_manual_registeration_rawvector(SEXP ref_imageSEXP, SEXP query_imageSEXP, SEXP reference_landmarkSEXP, SEXP query_landmarkSEXP, SEXP width1SEXP, SEXP height1SEXP, SEXP width2SEXP, SEXP height2SEXP, SEXP invert_querySEXP, SEXP invert_refSEXP, SEXP methodSEXP, SEXP nonrigidSEXP, SEXP compute_matte_mapSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
@@ -114,9 +133,27 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< const int >::type height1(height1SEXP);
Rcpp::traits::input_parameter< const int >::type width2(width2SEXP);
Rcpp::traits::input_parameter< const int >::type height2(height2SEXP);
+ Rcpp::traits::input_parameter< const bool >::type invert_query(invert_querySEXP);
+ Rcpp::traits::input_parameter< const bool >::type invert_ref(invert_refSEXP);
+ Rcpp::traits::input_parameter< Rcpp::String >::type method(methodSEXP);
+ Rcpp::traits::input_parameter< Rcpp::String >::type nonrigid(nonrigidSEXP);
+ Rcpp::traits::input_parameter< const bool >::type compute_matte_map(compute_matte_mapSEXP);
+ rcpp_result_gen = Rcpp::wrap(manual_registeration_rawvector(ref_image, query_image, reference_landmark, query_landmark, width1, height1, width2, height2, invert_query, invert_ref, method, nonrigid, compute_matte_map));
+ return rcpp_result_gen;
+END_RCPP
+}
+// manual_registeration_matrix
+Rcpp::List manual_registeration_matrix(Rcpp::NumericMatrix query_data, Rcpp::NumericMatrix reference_landmark, Rcpp::NumericMatrix query_landmark, Rcpp::String method, Rcpp::String nonrigid);
+RcppExport SEXP _VoltRon_manual_registeration_matrix(SEXP query_dataSEXP, SEXP reference_landmarkSEXP, SEXP query_landmarkSEXP, SEXP methodSEXP, SEXP nonrigidSEXP) {
+BEGIN_RCPP
+ Rcpp::RObject rcpp_result_gen;
+ Rcpp::RNGScope rcpp_rngScope_gen;
+ Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type query_data(query_dataSEXP);
+ Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type reference_landmark(reference_landmarkSEXP);
+ Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type query_landmark(query_landmarkSEXP);
Rcpp::traits::input_parameter< Rcpp::String >::type method(methodSEXP);
Rcpp::traits::input_parameter< Rcpp::String >::type nonrigid(nonrigidSEXP);
- rcpp_result_gen = Rcpp::wrap(manual_registeration_rawvector(ref_image, query_image, reference_landmark, query_landmark, width1, height1, width2, height2, method, nonrigid));
+ rcpp_result_gen = Rcpp::wrap(manual_registeration_matrix(query_data, reference_landmark, query_landmark, method, nonrigid));
return rcpp_result_gen;
END_RCPP
}
@@ -132,6 +169,19 @@ BEGIN_RCPP
return rcpp_result_gen;
END_RCPP
}
+// generateOverlapMask
+Rcpp::IntegerVector generateOverlapMask(Rcpp::NumericVector& dsize, Rcpp::NumericMatrix& trans_mat, Rcpp::NumericVector& ssize);
+RcppExport SEXP _VoltRon_generateOverlapMask(SEXP dsizeSEXP, SEXP trans_matSEXP, SEXP ssizeSEXP) {
+BEGIN_RCPP
+ Rcpp::RObject rcpp_result_gen;
+ Rcpp::RNGScope rcpp_rngScope_gen;
+ Rcpp::traits::input_parameter< Rcpp::NumericVector& >::type dsize(dsizeSEXP);
+ Rcpp::traits::input_parameter< Rcpp::NumericMatrix& >::type trans_mat(trans_matSEXP);
+ Rcpp::traits::input_parameter< Rcpp::NumericVector& >::type ssize(ssizeSEXP);
+ rcpp_result_gen = Rcpp::wrap(generateOverlapMask(dsize, trans_mat, ssize));
+ return rcpp_result_gen;
+END_RCPP
+}
// build_snn_rank
Rcpp::List build_snn_rank(Rcpp::IntegerMatrix neighbors);
RcppExport SEXP _VoltRon_build_snn_rank(SEXP neighborsSEXP) {
@@ -167,13 +217,16 @@ END_RCPP
}
static const R_CallMethodDef CallEntries[] = {
- {"_VoltRon_automated_registeration_rawvector", (DL_FUNC) &_VoltRon_automated_registeration_rawvector, 17},
+ {"_VoltRon_accuracy_rawvector", (DL_FUNC) &_VoltRon_accuracy_rawvector, 8},
+ {"_VoltRon_automated_registeration_rawvector", (DL_FUNC) &_VoltRon_automated_registeration_rawvector, 18},
{"_VoltRon_replaceNaMatrix", (DL_FUNC) &_VoltRon_replaceNaMatrix, 2},
{"_VoltRon_warpRcppImage", (DL_FUNC) &_VoltRon_warpRcppImage, 7},
{"_VoltRon_warpImageAuto", (DL_FUNC) &_VoltRon_warpImageAuto, 7},
{"_VoltRon_warpImageManual", (DL_FUNC) &_VoltRon_warpImageManual, 7},
- {"_VoltRon_manual_registeration_rawvector", (DL_FUNC) &_VoltRon_manual_registeration_rawvector, 10},
+ {"_VoltRon_manual_registeration_rawvector", (DL_FUNC) &_VoltRon_manual_registeration_rawvector, 13},
+ {"_VoltRon_manual_registeration_matrix", (DL_FUNC) &_VoltRon_manual_registeration_matrix, 5},
{"_VoltRon_applyRcppMapping", (DL_FUNC) &_VoltRon_applyRcppMapping, 2},
+ {"_VoltRon_generateOverlapMask", (DL_FUNC) &_VoltRon_generateOverlapMask, 3},
{"_VoltRon_build_snn_rank", (DL_FUNC) &_VoltRon_build_snn_rank, 1},
{"_VoltRon_build_snn_number", (DL_FUNC) &_VoltRon_build_snn_number, 1},
{"_VoltRon_replacePatternInRcppVectorWrapper", (DL_FUNC) &_VoltRon_replacePatternInRcppVectorWrapper, 3},
diff --git a/src/accuracy.cpp b/src/accuracy.cpp
new file mode 100644
index 00000000..1374d4fa
--- /dev/null
+++ b/src/accuracy.cpp
@@ -0,0 +1,66 @@
+#include
+
+// OpenCV
+#include
+
+// Library
+#include "auxiliary.h"
+#include "image.h"
+#include "metrics.h"
+#include "matte_mi.h"
+
+// Namespaces
+using namespace Rcpp;
+using namespace std;
+using namespace cv;
+
+// [[Rcpp::export]]
+Rcpp::List accuracy_rawvector(Rcpp::RawVector& ref_image,
+ Rcpp::RawVector& query_image,
+ Rcpp::RawVector& mask,
+ const int width,
+ const int height,
+ std::string type,
+ bool overlay_images = true,
+ const bool compute_matte_map = true) {
+ // results
+ Rcpp::List out(3);
+
+ // Read images
+ cv::Mat imReference = imageToMat(ref_image, width, height);
+ cv::Mat imReg = imageToMat(query_image, width, height);
+ cv::Mat maskReg = imageToMat(mask, width, height);
+
+ // process
+ Mat im1Proc, im2Proc;
+ cvtColor(imReg, im1Proc, cv::COLOR_BGR2GRAY);
+ cvtColor(imReference, im2Proc, cv::COLOR_BGR2GRAY);
+ cvtColor(maskReg, maskReg, cv::COLOR_BGR2GRAY);
+
+ // get metrics
+ std::map accuracy;
+ accuracy = getAlignmentMetrics(im1Proc, im2Proc, maskReg, type);
+ out[0] = accuracy;
+
+ // get matte map
+ Mat1d accuracyMatte;
+ if(compute_matte_map){
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, maskReg, 50);
+ out[1] = matToNumericMatrix(accuracyMatte); // Matte MI metric
+ } else {
+ out[1] = R_NilValue;
+ }
+
+ // image overlay
+ if(overlay_images){
+ cv::addWeighted(im2Proc, 0.7, im1Proc, 0.3, 0, im1Proc);
+ cvtColor(im1Proc, im1Proc, cv::COLOR_GRAY2BGR);
+ im1Proc = resize_image(im1Proc, 500);
+ out[2] = matToImage(im1Proc);
+ } else {
+ out[2] = R_NilValue;
+ }
+
+ // return
+ return out;
+}
\ No newline at end of file
diff --git a/src/automated_registration.cpp b/src/automated_registration.cpp
index 7bbb1305..baade750 100644
--- a/src/automated_registration.cpp
+++ b/src/automated_registration.cpp
@@ -4,17 +4,22 @@
#include
#include "opencv2/features2d.hpp"
#include "opencv2/shape/shape_transformer.hpp"
-// #include
-// Internal functions
+// Library
#include "auxiliary.h"
#include "image.h"
+#include "metrics.h"
+#include "matte_mi.h"
// Namespaces
using namespace Rcpp;
using namespace std;
using namespace cv;
+////
+// Parameters
+////
+
// SIFT Parameters
struct SIFTParameters
{
@@ -27,95 +32,9 @@ struct SIFTParameters
const int ransac_maxIters=2000;
};
-// check if keypoints are degenerate
-bool check_degenerate(std::vector &points1, std::vector &points2) {
-
- // get sd
- double points1_sd = cppSD(points1);
- double points2_sd = cppSD(points2);
-
- // get warning message
- bool is_degenerate = FALSE;
- if(points1_sd < 1.0 | points2_sd < 1.0){
- is_degenerate = TRUE;
- Rcout << "WARNING: points may be in a degenerate configuration." << endl;
- }
-
- return is_degenerate;
-}
-
-// check distribution of registered points
-std::string check_transformation_by_point_distribution(Mat &im, Mat &h){
-
- // message
- std::string message;
-
- // get image shape
- int height = im.rows;
- int width = im.cols;
- int height_interval = height > 50 ? (double) height/50.0 : 1;
- int width_interval = width > 50 ? (double) width/50.0 : 1;
-
- // perspective transformation of grid points
- std::vector gridpoints;
- for (double i = 0.0; i <= height; i += height_interval) {
- for (double j = 0.0; j <= width; j += width_interval) {
- gridpoints.push_back(cv::Point2f(j,i));
- }
- }
-
- // register grid points
- std::vector gridpoints_reg;
- if (h.rows == 2){
- cv::transform(gridpoints, gridpoints_reg, h);
- } else if(h.rows == 3) {
- cv::perspectiveTransform(gridpoints, gridpoints_reg, h);
- } else {
- message = "no distribution";
- return message;
- }
-
- // Compute the standard deviation of the transformed points
- double gridpoints_reg_sd = cppSD(gridpoints_reg);
-
- // get warning message
- if(gridpoints_reg_sd < 1.0 | gridpoints_reg_sd > max(height, width)){
- message = "large distribution";
- Rcout << "WARNING: Transformation may be poor - transformed points grid seem to be concentrated!" << endl;
- } else {
- message = "small distribution";
- }
-
- return message;
-}
-
-bool check_matches(Mat &mask){
- int j=0;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- j++;
- }
- }
- return j > 6;
-}
-
-// do overall checks on keypoints and images
-bool check_transformation_metrics(std::vector &points1, std::vector &points2, Mat &im2, Mat &h, Mat &mask) {
-
- // check keypoint standard deviation
- bool is_degenerate = check_degenerate(points1, points2);
-
- // TODO: check transformation
- // make keypoints from points
- // std::string transformation;
- // transformation = check_transformation_by_pts_mean_sqrt(keypoints1, keypoints2, h, mask);
-
- // check distribution
- std::string distribution;
- distribution = check_transformation_by_point_distribution(im2, h);
-
- return is_degenerate;
-}
+////
+// Manage Keypoints and Matches
+////
// get good matching keypoints
void getGoodMatches(std::vector> &matches12,std::vector> &matches21,
@@ -171,17 +90,10 @@ void getGoodMatches(std::vector> &matches12,std::vector, std::vector>().swap(matches21_map);
}
-void getGoodMatches_temp(std::vector> matches, std::vector &good_matches, const float lowe_ratio = 0.8)
-{
- for (size_t i = 0; i < matches.size(); i++) {
- if (matches[i][0].distance < lowe_ratio * matches[i][1].distance) {
- good_matches.push_back(matches[i][0]);
- }
- }
-}
-
// remove duplicate keypoints for TPS
-void removeCloseMatches(std::vector& points1, std::vector& points2, float threshold = std::numeric_limits::epsilon()) {
+void removeCloseMatches(std::vector& points1,
+ std::vector& points2,
+ float threshold = std::numeric_limits::epsilon()) {
// Create a vector to store filtered points
std::vector filtered_points1;
@@ -293,6 +205,10 @@ void keepTopKeypoints(std::vector &keypoints, Mat &descriptors, SIFTPa
}
}
+////
+// Compute SIFT/ORB and transformations
+////
+
void computeSIFTTiles(Mat &im, std::vector &keypoints, Mat &descriptors, Ptr &sift,
SIFTParameters params){
// profiler
@@ -352,6 +268,7 @@ void computeSIFTTiles(Mat &im, std::vector &keypoints, Mat &descriptor
}
}
+
bool getSIFTTransformationMatrixSingle(
Mat &im1Proc, Mat &im2Proc, Mat &h, Mat &mask,
Mat &imMatches,
@@ -370,8 +287,6 @@ bool getSIFTTransformationMatrixSingle(
Ptr sift = cv::SIFT::create(params.sift_nfeatures);
computeSIFTTiles(im1Proc, keypoints1, descriptors1, sift, params);
computeSIFTTiles(im2Proc, keypoints2, descriptors2, sift, params);
- Rcout << "MESSAGE: Generated " << keypoints1.size() << " and " << keypoints2.size() << " keypoints" << endl;
- Rcout << "DONE: SIFT based key-points detection and descriptors computation" << endl;
// filter duplicates
filterDuplicateKeypoints(keypoints1, descriptors1);
@@ -380,7 +295,6 @@ bool getSIFTTransformationMatrixSingle(
// get top key points
keepTopKeypoints(keypoints1, descriptors1, params);
keepTopKeypoints(keypoints2, descriptors2, params);
- Rcout << "MESSAGE: Filtered other than " << keypoints1.size() << " and " << keypoints2.size() << " keypoints" << endl;
///////////////////////
/// Compute FLANN /////
@@ -389,7 +303,6 @@ bool getSIFTTransformationMatrixSingle(
// Match features using FLANN matching
std::vector> matches12, matches21;
getFLANNMatches(descriptors1, descriptors2, matches12, matches21);
- Rcout << "DONE: FLANN - Fast Library for Approximate Nearest Neighbors - descriptor matching" << endl;
// TODO: can I release there now ?
descriptors1.release();
@@ -398,12 +311,7 @@ bool getSIFTTransformationMatrixSingle(
// Find good matches
std::vector good_matches;
getGoodMatches(matches12, matches21, good_matches);
- Rcout << "DONE: get good mutual matches by distance thresholding" << endl;
- // TODO: can I release there now ?
- std::vector>().swap(matches12);
- std::vector>().swap(matches21);
-
///////////////////////
/// Find Homography ///
///////////////////////
@@ -414,10 +322,7 @@ bool getSIFTTransformationMatrixSingle(
points1.push_back(keypoints1[good_matches[i].queryIdx].pt);
points2.push_back(keypoints2[good_matches[i].trainIdx].pt);
}
-
- // check variable
- Rcout << "MESSAGE: Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
-
+
// Find transformation matrix
if(points1.size() > 0){
if(run_Affine){
@@ -444,35 +349,31 @@ bool getSIFTTransformationMatrixSingle(
return false;
}
- // Draw top matches and good ones only
- std::vector top_matches;
- std::vector keypoints1_best, keypoints2_best;
+ // filter keypoints by good matches
+ std::vector keypoints1_good, keypoints2_good;
for(size_t i = 0; i < good_matches.size(); i++ )
{
- keypoints1_best.push_back(keypoints1[good_matches[i].queryIdx]);
- keypoints2_best.push_back(keypoints2[good_matches[i].trainIdx]);
- }
- std::vector keypoints1_best2, keypoints2_best2;
- int j=0;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- keypoints1_best2.push_back(keypoints1_best[i]);
- keypoints2_best2.push_back(keypoints2_best[i]);
- top_matches.push_back(cv::DMatch(static_cast(j), static_cast(j), 0));
- j++;
- }
+ keypoints1_good.push_back(keypoints1[good_matches[i].queryIdx]);
+ keypoints2_good.push_back(keypoints2[good_matches[i].trainIdx]);
}
- scaledDrawMatches(im1Proc, keypoints1_best2, im2Proc, keypoints2_best2, top_matches, imMatches);
-
- // TODO: can I release there now ?
- // std::vector().swap(keypoints1_best);
- // std::vector().swap(keypoints2_best);
- // std::vector().swap(keypoints1_best2);
- // std::vector().swap(keypoints2_best2);
- // std::vector().swap(top_matches);
+
+ // filter keypoints using mask
+ std::vector keypoints1_masked, keypoints2_masked;
+ std::vector top_matches;
+ maskKeypoints(keypoints1_good, keypoints2_good,
+ keypoints1_masked, keypoints2_masked,
+ top_matches, mask);
+
+ // convert keypoints to points
+ points1 = KeyPointToPoint2f(keypoints1_masked);
+ points2 = KeyPointToPoint2f(keypoints2_masked);
+
+ // draw matches
+ scaledDrawMatches(im1Proc, keypoints1_masked, im2Proc, keypoints2_masked,
+ top_matches, imMatches);
// check number of matches
- return check_matches(mask);
+ return checkMaskAbundance(mask);
}
void getSIFTTransformationMatrix(
@@ -493,70 +394,76 @@ void getSIFTTransformationMatrix(
// check variable
bool check;
- Rcout << "MESSAGE: Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
+ Rcout << "Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
+
+ Rcout << "Round 1: No histogram equalization" << endl;
// find matches and points
check = getSIFTTransformationMatrixSingle(im1Proc, im2Proc, h, mask,
imMatches,
points1, points2,
run_Affine, params, is_faulty);
- Rcout << "DONE: calculated homography matrix with " << points1.size() << " points" << endl;
-
+
// equalize first image if fails
if(!check){
+
+ // clear points, mask is reset itself
+ points1.clear();
+ points2.clear();
Mat im1Proc_eq;
cv::equalizeHist(im1Proc, im1Proc_eq);
- Rcout << "MESSAGE: Calculating Transformation Matrix with histogram equalization (1)" << endl;
+ Rcout << "Round 2: Histogram equalization of Image 1" << endl;
check = getSIFTTransformationMatrixSingle(im1Proc_eq, im2Proc, h, mask,
imMatches,
points1, points2,
run_Affine, params, is_faulty);
- Rcout << "DONE: calculated homography matrix with " << points1.size() << " points" << endl;
} else {
return;
}
// equalize second image if fails
if(!check){
+
+ // clear points, mask is reset itself
+ points1.clear();
+ points2.clear();
cv::equalizeHist(im2Proc, im2Proc_eq);
- Rcout << "MESSAGE: Calculating Transformation Matrix with histogram equalization (2)" << endl;
+ Rcout << "Round 3: Histogram equalization of Image 2" << endl;
check = getSIFTTransformationMatrixSingle(im1Proc, im2Proc_eq, h, mask,
imMatches,
points1, points2,
run_Affine, params, is_faulty);
- Rcout << "DONE: calculated homography matrix with " << points1.size() << " points" << endl;
} else {
return;
}
// last try with both equalized images
if(!check){
+
+ // clear points, mask is reset itself
+ points1.clear();
+ points2.clear();
cv::equalizeHist(im1Proc, im1Proc_eq2);
cv::equalizeHist(im2Proc, im2Proc_eq2);
- Rcout << "MESSAGE: Calculating Transformation Matrix with histogram equalization (3)" << endl;
+ Rcout << "Round 4: Histogram equalization of Image 1 and 2" << endl;
check = getSIFTTransformationMatrixSingle(im1Proc_eq2, im2Proc_eq2, h, mask,
imMatches,
points1, points2,
run_Affine, params, is_faulty);
- Rcout << "DONE: calculated homography matrix with " << points1.size() << " points" << endl;
} else {
return;
}
- // TODO: release ?
- // im1Proc_eq.release();
- // im1Proc_eq2.release();
- // im2Proc_eq.release();
- // im2Proc_eq2.release();
+
}
-bool getORBTransformationMatrix(
+void getORBTransformationMatrix(
Mat &im1Proc, Mat &im2Proc, Mat &h, Mat &mask,
Mat &imMatches, std::vector &points1, std::vector &points2,
const bool &run_Affine, const float GOOD_MATCH_PERCENT, const int MAX_FEATURES, bool &is_faulty){
@@ -573,22 +480,19 @@ bool getORBTransformationMatrix(
Ptr orb = ORB::create(MAX_FEATURES);
orb->detectAndCompute(im1Proc, Mat(), keypoints1, descriptors1);
orb->detectAndCompute(im2Proc, Mat(), keypoints2, descriptors2);
- Rcout << "DONE: orb based key-points detection and descriptors computation" << endl;
-
+
// Match features.
std::vector matches;
Ptr matcher = DescriptorMatcher::create("BruteForce-Hamming");
matcher->match(descriptors1, descriptors2, matches, Mat());
- Rcout << "DONE: BruteForce-Hamming - descriptor matching" << endl;
-
+
// Sort matches by score
std::sort(matches.begin(), matches.end());
// Remove not so good matches
const int numGoodMatches = matches.size() * GOOD_MATCH_PERCENT;
matches.erase(matches.begin()+numGoodMatches, matches.end());
- Rcout << "DONE: get good matches by distance thresholding" << endl;
-
+
// Extract location of good matches
for( size_t i = 0; i < matches.size(); i++ )
{
@@ -597,8 +501,8 @@ bool getORBTransformationMatrix(
}
// check variable
- Rcout << "MESSAGE: Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
-
+ Rcout << "Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
+
// Find transformation matrix
if(points1.size() > 0){
if(run_Affine){
@@ -616,43 +520,60 @@ bool getORBTransformationMatrix(
mask);
}
} else {
- Rcout << "Found no matches!" << endl;
- return false;
+ Rcout << "WARNING: Found no matches!" << endl;
}
-
- // Draw top matches and good ones only
- std::vector top_matches;
- std::vector keypoints1_best, keypoints2_best;
+
+ // filter keypoints by good matches
+ std::vector keypoints1_good, keypoints2_good;
for(size_t i = 0; i < matches.size(); i++ )
{
- keypoints1_best.push_back(keypoints1[matches[i].queryIdx]);
- keypoints2_best.push_back(keypoints2[matches[i].trainIdx]);
- }
- std::vector keypoints1_best2, keypoints2_best2;
- int j=0;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- keypoints1_best2.push_back(keypoints1_best[i]);
- keypoints2_best2.push_back(keypoints2_best[i]);
- top_matches.push_back(cv::DMatch(static_cast(j), static_cast(j), 0));
- j++;
- }
+ keypoints1_good.push_back(keypoints1[matches[i].queryIdx]);
+ keypoints2_good.push_back(keypoints2[matches[i].trainIdx]);
}
- scaledDrawMatches(im1Proc, keypoints1_best2, im2Proc, keypoints2_best2, top_matches, imMatches);
-
- // check number of matches
- return check_matches(mask);
+
+ // filter keypoints using mask
+ std::vector keypoints1_masked, keypoints2_masked;
+ std::vector top_matches;
+ maskKeypoints(keypoints1_good, keypoints2_good,
+ keypoints1_masked, keypoints2_masked,
+ top_matches, mask);
+
+ // convert keypoints to points
+ points1 = KeyPointToPoint2f(keypoints1_masked);
+ points2 = KeyPointToPoint2f(keypoints2_masked);
+
+ // draw matches
+ scaledDrawMatches(im1Proc, keypoints1_masked, im2Proc, keypoints2_masked,
+ top_matches, imMatches);
}
+////
+// Align Images
+////
+
// align images with FLANN algorithm
-void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
- Mat &imMatches, Mat &h, Rcpp::List &keypoints,
- const float GOOD_MATCH_PERCENT, const int MAX_FEATURES,
+void alignImages(Mat &im1,
+ Mat &im2,
+ Mat &im1Reg,
+ Mat &im1Overlay,
+ Mat &imMatches,
+ Mat &h,
+ Rcpp::List &keypoints,
+ const float GOOD_MATCH_PERCENT,
+ const int MAX_FEATURES,
Rcpp::String matcher,
- const bool invert_query, const bool invert_ref,
- const char* flipflop_query, const char* flipflop_ref,
- const char* rotate_query, const char* rotate_ref,
- const bool run_Affine, const bool run_TPS)
+ const bool invert_query,
+ const bool invert_ref,
+ const char* flipflop_query,
+ const char* flipflop_ref,
+ const char* rotate_query,
+ const char* rotate_ref,
+ const bool run_Affine,
+ const bool run_TPS,
+ const bool compute_matte_map,
+ Mat1d &accuracyMatte,
+ std::map &accuracy_coarse,
+ std::map &accuracy_fine)
{
// parameters
@@ -683,43 +604,63 @@ void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
if(strcmp(matcher.get_cstring(), "BRUTE-FORCE") == 0){
// message
- Rcout << "MESSAGE: Running BRUTE-FORCE Alignment" << endl;
+ Rcout << "Running Coarse Alignment (BRUTE-FORCE)" << endl;
// run ORB
- bool check;
- check = getORBTransformationMatrix(im1Proc, im2Proc, h, mask, imMatches,
- points1, points2, run_Affine,
- GOOD_MATCH_PERCENT, MAX_FEATURES, is_faulty);
+ getORBTransformationMatrix(im1Proc, im2Proc, h, mask, imMatches,
+ points1, points2, run_Affine,
+ GOOD_MATCH_PERCENT, MAX_FEATURES, is_faulty);
} else {
// message
- Rcout << "MESSAGE: Running SIFT+FLANN Alignment" << ((run_TPS) ? " with TPS" : "") << endl;
+ // Rcout << "Running SIFT+FLANN Alignment" << ((run_TPS) ? " with TPS" : "") << endl;
+ Rcout << "Running Coarse Alignment (SIFT+FLANN)" << endl;
// run SIFT
getSIFTTransformationMatrix(im1Proc, im2Proc, h, mask, imMatches,
points1, points2, run_Affine, is_faulty);
}
-
- // check result
- is_faulty = check_transformation_metrics(points1, points2, im2, h, mask);
- Rcout << "MESSAGE: Registration is " << (is_faulty ? "degenerate!" : "not degenerate!") << endl;
- // Use homography to warp image
- if(h.rows == 2){
- warpAffine(im1Proc, im1Proc, h, im2Proc.size());
- warpAffine(im1NormalProc, im1NormalProc, h, im2Proc.size());
- } else if(h.rows == 3){
- warpPerspective(im1Proc, im1Proc, h, im2Proc.size());
- warpPerspective(im1NormalProc, im1NormalProc, h, im2Proc.size());
+ // warp mask and image
+ cv::Mat alignmentMask;
+ std::map keypoint_metrics;
+ if(!h.empty()){
+ alignmentMask = generateOverlapMask(im2Proc.size(),
+ h,
+ im1Proc.size());
+
+ // get keypoint metrics before warping
+ keypoint_metrics = getKeypointMetrics(points1, points2,
+ im1Proc, im2Proc, h, mask);
+ is_faulty = (bool) keypoint_metrics["Degenerate"];
+
+ if(h.rows == 2){
+ warpAffine(im1Proc, im1Proc, h, im2Proc.size());
+ warpAffine(im1NormalProc, im1NormalProc, h, im2Proc.size());
+ } else if(h.rows == 3){
+ warpPerspective(im1Proc, im1Proc, h, im2Proc.size());
+ warpPerspective(im1NormalProc, im1NormalProc, h, im2Proc.size());
+ }
} else {
Rcout << "WARNING: No transformation was found" << endl;
return;
}
-
- Rcout << "DONE: warped query image" << endl;
+ // get alignment metrics
+ std::map image_metrics;
+ image_metrics = getAlignmentMetrics(im1Proc, im2Proc,
+ alignmentMask, "Coarse");
+
+ // combine metrics
+ std::vector> temp_map;
+ temp_map.reserve(keypoint_metrics.size() + image_metrics.size());
+ std::copy(keypoint_metrics.begin(), keypoint_metrics.end(), std::back_inserter(temp_map));
+ std::copy(image_metrics.begin(), image_metrics.end(), std::back_inserter(temp_map));
+ std::map final_map(temp_map.begin(), temp_map.end());
+ accuracy_coarse = final_map;
+
///////////////////////
/// Find Homography ///
///////////////////////
@@ -727,6 +668,10 @@ void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
// continue with TPS or do FLANN only
if(is_faulty || !run_TPS){
+ // compute matte map if finishing alignment
+ if(compute_matte_map)
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, alignmentMask, 50);
+
// change color map
cv::addWeighted(im2Proc, 0.7, im1Proc, 0.3, 0, im1Proc);
@@ -736,55 +681,47 @@ void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
// TPS is requested (only if FLANN succeeded)
} else {
- Rcout << "MESSAGE: Running Thin-Plate-Spline Alignment" << endl;
-
- // Filtered points (inliers) based on the mask
- std::vector filtered_points1;
- std::vector filtered_points2;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- filtered_points1.push_back(points1[i]);
- filtered_points2.push_back(points2[i]);
- }
- }
- removeCloseMatches(filtered_points1, filtered_points2);
+ Rcout << "Running Fine Alignment (Thin-Plate-Spline)" << endl;
+ // remove close looking matches
+ removeCloseMatches(points1, points2);
+
// transform query
- std::vector filtered_points1_reg;
+ std::vector points1_reg;
if (h.rows == 2){
- cv::transform(filtered_points1, filtered_points1_reg, h);
+ cv::transform(points1, points1_reg, h);
} else {
- cv::perspectiveTransform(filtered_points1, filtered_points1_reg, h);
+ cv::perspectiveTransform(points1, points1_reg, h);
}
// get TPS matches
std::vector matches;
- for (unsigned int i = 0; i < filtered_points2.size(); i++)
+ for (unsigned int i = 0; i < points2.size(); i++)
matches.push_back(cv::DMatch(i, i, 0));
// calculate TPS transformation
Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
- tps->estimateTransformation(filtered_points2, filtered_points1_reg, matches);
+ tps->estimateTransformation(points2, points1_reg, matches);
// save keypoints
- keypoints[0] = point2fToNumericMatrix(filtered_points2);
- keypoints[1] = point2fToNumericMatrix(filtered_points1_reg);
+ keypoints[0] = point2fToNumericMatrix(points2);
+ keypoints[1] = point2fToNumericMatrix(points1_reg);
- // determine extension limits for both images
- int y_max = max(im1Proc.rows, im2.rows);
- int x_max = max(im1Proc.cols, im2.cols);
-
- // extend images
- cv::copyMakeBorder(im1Proc, im1Proc, 0.0, (int) (y_max - im1Proc.rows), 0.0, (x_max - im1Proc.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
- cv::copyMakeBorder(im1NormalProc, im1NormalProc, 0.0, (int) (y_max - im1NormalProc.rows), 0.0, (x_max - im1NormalProc.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
-
- // transform image
- tps->warpImage(im1Proc, im1Proc);
- tps->warpImage(im1NormalProc, im1NormalProc);
+ // warp overlap mask
+ alignmentMask = warpTPSImage(im2Proc, alignmentMask, tps,
+ im2Proc.rows, im2Proc.cols,
+ cv::INTER_NEAREST);
+
+ // transform image using trained tps
+ im1Proc = warpTPSImage(im2Proc, im1Proc, tps,
+ im2Proc.rows, im2Proc.cols, cv::INTER_LINEAR);
+ im1NormalProc = warpTPSImage(im2Proc, im1NormalProc, tps,
+ im2Proc.rows, im2Proc.cols, cv::INTER_LINEAR);
- // resize image
- im1Proc = im1Proc(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
- im1NormalProc = im1NormalProc(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
+ // get matte metric, process
+ accuracy_fine = getAlignmentMetrics(im1Proc, im2Proc, alignmentMask, "Fine");
+ if(compute_matte_map)
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, alignmentMask, 50);
// change color map
cv::addWeighted(im2Proc, 0.7, im1Proc, 0.3, 0, im1Proc);
@@ -807,20 +744,32 @@ void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
}
// [[Rcpp::export]]
-Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::RawVector& query_image,
- const int width1, const int height1,
- const int width2, const int height2,
- const float GOOD_MATCH_PERCENT, const int MAX_FEATURES,
- const bool invert_query, const bool invert_ref,
- Rcpp::String flipflop_query, Rcpp::String flipflop_ref,
- Rcpp::String rotate_query, Rcpp::String rotate_ref,
- Rcpp::String matcher, Rcpp::String method, Rcpp::String nonrigid)
+Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image,
+ Rcpp::RawVector& query_image,
+ const int width1,
+ const int height1,
+ const int width2,
+ const int height2,
+ const float GOOD_MATCH_PERCENT,
+ const int MAX_FEATURES,
+ const bool invert_query,
+ const bool invert_ref,
+ Rcpp::String flipflop_query,
+ Rcpp::String flipflop_ref,
+ Rcpp::String rotate_query,
+ Rcpp::String rotate_ref,
+ Rcpp::String matcher,
+ Rcpp::String method,
+ Rcpp::String nonrigid,
+ const bool compute_matte_map = true)
{
// Return data
- Rcpp::List out(5);
+ Rcpp::List out(8);
Rcpp::List out_trans(2);
Rcpp::List keypoints(2);
Mat imOverlay, imReg, h, imMatches;
+ Mat1d accuracyMatte;
+ std::map accuracy_coarse, accuracy_fine;
// Read reference image
cv::Mat imReference = imageToMat(ref_image, width1, height1);
@@ -841,7 +790,11 @@ Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::R
invert_query, invert_ref,
flipflop_query.get_cstring(), flipflop_ref.get_cstring(),
rotate_query.get_cstring(), rotate_ref.get_cstring(),
- run_Affine, run_TPS);
+ run_Affine, run_TPS,
+ compute_matte_map,
+ accuracyMatte,
+ accuracy_coarse,
+ accuracy_fine);
// transformation matrix, can be either a matrix, set of keypoints or both
out_trans[0] = matToNumericMatrix(h.clone());
@@ -857,10 +810,16 @@ Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::R
out[2] = matToImage(imReg); // registered image
out[3] = matToImage(imMatches); // keypoint matching image
out[4] = matToImage(imOverlay); // overlay image
+ out[5] = matToNumericMatrix(accuracyMatte); // Matte MI metric
+ out[6] = accuracy_coarse; // accuracy scores (coarse)
+ out[7] = accuracy_fine; // accuracy scores (fine)
} else {
out[2] = R_NilValue;
out[3] = R_NilValue;
out[4] = R_NilValue;
+ out[5] = R_NilValue;
+ out[6] = R_NilValue;
+ out[7] = R_NilValue;
}
// release
@@ -872,498 +831,4 @@ Rcpp::List automated_registeration_rawvector(Rcpp::RawVector& ref_image, Rcpp::R
// return
return out;
-}
-
-/////////////////
-/// scratch /////
-/////////////////
-
-// align images with BRUTE FORCE algorithm
-void alignImagesBRUTE(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay, Mat &imMatches, Mat &h,
- const float GOOD_MATCH_PERCENT, const int MAX_FEATURES,
- const bool invert_query, const bool invert_ref,
- const char* flipflop_query, const char* flipflop_ref,
- const char* rotate_query, const char* rotate_ref,
- const bool run_Affine)
-{
-
- // Convert images to grayscale
- Mat im1Gray, im2Gray;
- cvtColor(im1, im1Gray, cv::COLOR_BGR2GRAY);
- cvtColor(im2, im2Gray, cv::COLOR_BGR2GRAY);
-
- // Variables to store keypoints and descriptors
- std::vector keypoints1, keypoints2;
- Mat descriptors1, descriptors2;
-
- // Process images
- Mat im1Proc, im2Proc, im1NormalProc;
- im1Proc = preprocessImage(im1Gray, invert_query, flipflop_query, rotate_query);
- im1NormalProc = preprocessImage(im1, FALSE, flipflop_query, rotate_query);
- im2Proc = preprocessImage(im2Gray, invert_ref, flipflop_ref, rotate_ref);
-
- // Detect ORB features and compute descriptors.
- Ptr orb = ORB::create(MAX_FEATURES);
- orb->detectAndCompute(im1Proc, Mat(), keypoints1, descriptors1);
- orb->detectAndCompute(im2Proc, Mat(), keypoints2, descriptors2);
- Rcout << "DONE: orb based key-points detection and descriptors computation" << endl;
-
- // Match features.
- std::vector matches;
- Ptr matcher = DescriptorMatcher::create("BruteForce-Hamming");
- matcher->match(descriptors1, descriptors2, matches, Mat());
- Rcout << "DONE: BruteForce-Hamming - descriptor matching" << endl;
-
- // Sort matches by score
- std::sort(matches.begin(), matches.end());
-
- // Remove not so good matches
- const int numGoodMatches = matches.size() * GOOD_MATCH_PERCENT;
- matches.erase(matches.begin()+numGoodMatches, matches.end());
- Rcout << "DONE: get good matches by distance thresholding" << endl;
-
- // Extract location of good matches
- std::vector points1, points2;
- for( size_t i = 0; i < matches.size(); i++ )
- {
- points1.push_back( keypoints1[ matches[i].queryIdx ].pt );
- points2.push_back( keypoints2[ matches[i].trainIdx ].pt );
- }
-
- // check variable
- Rcout << "Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
-
- // Find transformation matrix
- cv::Mat mask;
- if(run_Affine){
- std::vector match_mask;
- h = estimateAffine2D(points1,
- points2,
- match_mask,
- cv::RANSAC);
- mask = IntVectorToMat(match_mask);
- } else {
- h = findHomography(points1,
- points2,
- cv::RANSAC,
- 5,
- mask);
- }
-
- // Draw top matches and good ones only
- std::vector top_matches;
- std::vector keypoints1_best, keypoints2_best;
- for(size_t i = 0; i < matches.size(); i++ )
- {
- keypoints1_best.push_back(keypoints1[matches[i].queryIdx]);
- keypoints2_best.push_back(keypoints2[matches[i].trainIdx]);
- }
- std::vector keypoints1_best2, keypoints2_best2;
- int j=0;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- keypoints1_best2.push_back(keypoints1_best[i]);
- keypoints2_best2.push_back(keypoints2_best[i]);
- top_matches.push_back(cv::DMatch(static_cast(j), static_cast(j), 0));
- j++;
- }
- }
- scaledDrawMatches(im1Proc, keypoints1_best2, im2Proc, keypoints2_best2, top_matches, imMatches);
-
- // Use homography to warp image
- Mat im1Warp, im1NormalWarp;
- if(h.rows == 2){
- warpAffine(im1Proc, im1Warp, h, im2Proc.size());
- warpAffine(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- } else {
- warpPerspective(im1Proc, im1Warp, h, im2Proc.size());
- warpPerspective(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- }
-
- // Reverse process
- im1Reg = reversepreprocessImage(im1NormalWarp, flipflop_ref, rotate_ref);
-
- // return as rgb
- cvtColor(im2Proc, im2, cv::COLOR_GRAY2BGR);
-
- // resize image to visualize faster later in Shiny
- im2 = resize_image(im2, 500);
- im1Overlay = resize_image(im1Reg, 500);
-}
-
-// align images with FLANN algorithm
-void alignImagesFLANN(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
- Mat &imMatches, Mat &h, Rcpp::List &keypoints,
- const bool invert_query, const bool invert_ref,
- const char* flipflop_query, const char* flipflop_ref,
- const char* rotate_query, const char* rotate_ref,
- const bool run_Affine, const bool run_TPS)
-{
-
- // parameters
- cv::setRNGSeed(0);
- SIFTParameters params;
-
- //////////////////////
- /// Process Images ///
- //////////////////////
-
- // Convert images to grayscale
- Mat im1Gray, im2Gray;
- cvtColor(im1, im1Gray, cv::COLOR_BGR2GRAY);
- cvtColor(im2, im2Gray, cv::COLOR_BGR2GRAY);
-
- // Process images
- Mat im1Proc, im2Proc, im1NormalProc;
- im1Proc = preprocessImage(im1Gray, invert_query, flipflop_query, rotate_query);
- im1NormalProc = preprocessImage(im1, FALSE, flipflop_query, rotate_query);
- im2Proc = preprocessImage(im2Gray, invert_ref, flipflop_ref, rotate_ref);
-
- // ////////////////////////////////////
- // /// Compute SIFT+FLANN+Homograpy ///
- // ////////////////////////////////////
-
- // RUN SIFT+FLANN+Homography with retry
- bool is_faulty = FALSE;
- cv::Mat mask;
- std::vector points1, points2;
- // getSIFTTransformationMatrix(im1Proc, im2Proc, im1, im2, h, mask, imMatches,
- // points1, points2, run_Affine, params, is_faulty);
-
- // check result
- is_faulty = check_transformation_metrics(points1, points2, im2, h, mask);
- Rcout << "MESSAGE: Registration is " << (is_faulty ? "degenerate!" : "not degenerate!") << endl;
-
- // Use homography to warp image
- Mat im1Warp, im1NormalWarp;
- if(h.rows == 2){
- warpAffine(im1Proc, im1Warp, h, im2Proc.size());
- warpAffine(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- } else {
- warpPerspective(im1Proc, im1Warp, h, im2Proc.size());
- warpPerspective(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- }
-
- Rcout << "DONE: warped query image" << endl;
-
- ///////////////////////
- /// Find Homography ///
- ///////////////////////
-
- // continue with TPS or do FLANN only
- Mat im1Reg_Warp_nonrigid;
- Mat im1Reg_NormalWarp_nonrigid;
- Mat im1Combine;
- if(is_faulty || !run_TPS){
-
- // change color map
- cv::addWeighted(im2Proc, 0.7, im1Warp, 0.3, 0, im1Combine);
-
- // Reverse process
- im1Reg = reversepreprocessImage(im1NormalWarp, flipflop_ref, rotate_ref);
-
- // return as rgb
- cvtColor(im1Combine, im1Overlay, cv::COLOR_GRAY2BGR);
- cvtColor(im2Proc, im2, cv::COLOR_GRAY2BGR);
-
- // TPS is requested (only if FLANN succeeded)
- } else {
-
- Rcout << "MESSAGE: Running Thin-Plate-Spline Alignment" << endl;
-
- // Filtered points (inliers) based on the mask
- std::vector filtered_points1;
- std::vector filtered_points2;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- filtered_points1.push_back(points1[i]);
- filtered_points2.push_back(points2[i]);
- }
- }
- removeCloseMatches(filtered_points1, filtered_points2);
-
- // transform query
- std::vector filtered_points1_reg;
- if (h.rows == 2){
- cv::transform(filtered_points1, filtered_points1_reg, h);
- } else {
- cv::perspectiveTransform(filtered_points1, filtered_points1_reg, h);
- }
-
- // get TPS matches
- std::vector matches;
- for (unsigned int i = 0; i < filtered_points2.size(); i++)
- matches.push_back(cv::DMatch(i, i, 0));
-
- // calculate TPS transformation
- Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
- tps->estimateTransformation(filtered_points2, filtered_points1_reg, matches);
-
- // save keypoints
- keypoints[0] = point2fToNumericMatrix(filtered_points2);
- keypoints[1] = point2fToNumericMatrix(filtered_points1_reg);
-
- // determine extension limits for both images
- int y_max = max(im1Warp.rows, im2.rows);
- int x_max = max(im1Warp.cols, im2.cols);
-
- // extend images
- cv::copyMakeBorder(im1Warp, im1Warp, 0.0, (int) (y_max - im1Warp.rows), 0.0, (x_max - im1Warp.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
- cv::copyMakeBorder(im1NormalWarp, im1NormalWarp, 0.0, (int) (y_max - im1NormalWarp.rows), 0.0, (x_max - im1NormalWarp.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
-
- // transform image
- Mat im1Reg_Warp_nonrigid;
- Mat im1Reg_NormalWarp_nonrigid;
- tps->warpImage(im1Warp, im1Reg_Warp_nonrigid);
- tps->warpImage(im1NormalWarp, im1Reg_NormalWarp_nonrigid);
-
- // resize image
- cv::Mat im1Reg_NormalWarp_nonrigid_cropped = im1Reg_NormalWarp_nonrigid(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
- im1Reg_NormalWarp_nonrigid = im1Reg_NormalWarp_nonrigid_cropped.clone();
-
- cv::Mat im1Reg_Warp_nonrigid_cropped = im1Reg_Warp_nonrigid(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
- im1Reg_Warp_nonrigid = im1Reg_Warp_nonrigid_cropped.clone();
-
- // change color map
- cv::addWeighted(im2Proc, 0.7, im1Reg_Warp_nonrigid, 0.3, 0, im1Combine);
-
- // Reverse process
- im1Reg = reversepreprocessImage(im1Reg_NormalWarp_nonrigid, flipflop_ref, rotate_ref);
-
- // return as rgb
- cvtColor(im1Combine, im1Overlay, cv::COLOR_GRAY2BGR);
- cvtColor(im2Proc, im2, cv::COLOR_GRAY2BGR);
- }
-
- // resize image to visualize faster later in Shiny
- im2 = resize_image(im2, 500);
- im1Overlay = resize_image(im1Overlay, 500);
-}
-
-// align images with FLANN algorithm
-void alignImagesFLANN2(Mat &im1, Mat &im2, Mat &im1Reg, Mat &im1Overlay,
- Mat &imMatches, Mat &h, Rcpp::List &keypoints,
- const bool invert_query, const bool invert_ref,
- const char* flipflop_query, const char* flipflop_ref,
- const char* rotate_query, const char* rotate_ref,
- const bool run_Affine, const bool run_TPS)
-{
-
- // parameters
- cv::setRNGSeed(0);
- SIFTParameters params;
-
- //////////////////////
- /// Process Images ///
- //////////////////////
-
- // Convert images to grayscale
- Mat im1Gray, im2Gray;
- cvtColor(im1, im1Gray, cv::COLOR_BGR2GRAY);
- cvtColor(im2, im2Gray, cv::COLOR_BGR2GRAY);
-
- // Process images
- Mat im1Proc, im2Proc, im1NormalProc;
- im1Proc = preprocessImage(im1Gray, invert_query, flipflop_query, rotate_query);
- im1NormalProc = preprocessImage(im1, FALSE, flipflop_query, rotate_query);
- im2Proc = preprocessImage(im2Gray, invert_ref, flipflop_ref, rotate_ref);
-
- // ////////////////////////////////////
- // /// Compute SIFT+FLANN+Homograpy ///
- // ////////////////////////////////////
-
- // RUN SIFT+FLANN+Homography with retry
- bool is_faulty = FALSE;
- cv::Mat mask;
- std::vector points1, points2;
-
-
- // Variables to store keypoints and descriptors
- std::vector keypoints1, keypoints2;
- Mat descriptors1, descriptors2;
-
- // Detect SIFT features
- // Ptr sift = cv::SIFT::create(params.sift_nfeatures);
- Ptr sift = cv::SIFT::create();
- // computeSIFTTiles(im1Proc, keypoints1, descriptors1, sift, params);
- // computeSIFTTiles(im2Proc, keypoints2, descriptors2, sift, params);
- sift->detectAndCompute(im1Proc, Mat(), keypoints1, descriptors1);
- sift->detectAndCompute(im2Proc, Mat(), keypoints2, descriptors2);
-
- Rcout << "MESSAGE: Generated " << keypoints1.size() << " and " << keypoints2.size() << " keypoints" << endl;
- Rcout << "DONE: SIFT based key-points detection and descriptors computation" << endl;
-
- ///////////////////////
- /// Compute FLANN /////
- ///////////////////////
-
- // Match features using FLANN matching
- std::vector> matches;
- cv::FlannBasedMatcher custom_matcher = cv::FlannBasedMatcher(cv::makePtr(5), cv::makePtr(50, 0, TRUE));
- cv::Ptr matcher = custom_matcher.create();
- matcher->knnMatch(descriptors1, descriptors2, matches, 2);
- Rcout << "DONE: FLANN - Fast Library for Approximate Nearest Neighbors - descriptor matching" << endl;
-
- // Find good matches
- // goodMatches = get_good_matches(matches)
- std::vector good_matches;
- getGoodMatches_temp(matches, good_matches);
- Rcout << "DONE: get good matches by distance thresholding" << endl;
-
- ///////////////////////
- /// Find Homography ///
- ///////////////////////
-
- // Extract location of good matches
- for( size_t i = 0; i < good_matches.size(); i++ )
- {
- points1.push_back(keypoints1[good_matches[i].queryIdx].pt);
- points2.push_back(keypoints2[good_matches[i].trainIdx].pt);
- }
-
- // check variable
- Rcout << "MESSAGE: Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
-
- // Find transformation matrix
- Rcout << "MESSAGE: Matching " << points1.size() << " keypoints" << endl;
- if(run_Affine){
- std::vector match_mask;
- h = estimateAffine2D(points1,
- points2,
- match_mask,
- cv::RANSAC);
- mask = IntVectorToMat(match_mask);
- } else {
- h = findHomography(points1, points2, RANSAC);
- }
-
- // Draw top matches and good ones only
- std::vector top_matches;
- std::vector keypoints1_best, keypoints2_best;
- for(size_t i = 0; i < good_matches.size(); i++ )
- {
- keypoints1_best.push_back(keypoints1[good_matches[i].queryIdx]);
- keypoints2_best.push_back(keypoints2[good_matches[i].trainIdx]);
- }
- std::vector keypoints1_best2, keypoints2_best2;
- int j=0;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- keypoints1_best2.push_back(keypoints1_best[i]);
- keypoints2_best2.push_back(keypoints2_best[i]);
- top_matches.push_back(cv::DMatch(static_cast(j), static_cast(j), 0));
- j++;
- }
- }
- // scaledDrawMatches(im1Proc, keypoints1_best2, im2Proc, keypoints2_best2, top_matches, imMatches);
- drawMatches(im1Proc, keypoints1_best2, im2Proc, keypoints2_best2, top_matches, imMatches);
-
- // check result
- is_faulty = check_transformation_metrics(points1, points2, im2, h, mask);
- Rcout << "MESSAGE: Registration is " << (is_faulty ? "degenerate!" : "not degenerate!") << endl;
-
- // Use homography to warp image
- Mat im1Warp, im1NormalWarp;
- if(h.rows == 2){
- warpAffine(im1Proc, im1Warp, h, im2Proc.size());
- warpAffine(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- } else {
- warpPerspective(im1Proc, im1Warp, h, im2Proc.size());
- warpPerspective(im1NormalProc, im1NormalWarp, h, im2Proc.size());
- }
-
- Rcout << "DONE: warped query image" << endl;
-
- ///////////////////////
- /// Find Homography ///
- ///////////////////////
-
- // continue with TPS or do FLANN only
- Mat im1Reg_Warp_nonrigid;
- Mat im1Reg_NormalWarp_nonrigid;
- Mat im1Combine;
- if(is_faulty || !run_TPS){
-
- // change color map
- cv::addWeighted(im2Proc, 0.7, im1Warp, 0.3, 0, im1Combine);
-
- // Reverse process
- im1Reg = reversepreprocessImage(im1NormalWarp, flipflop_ref, rotate_ref);
-
- // return as rgb
- cvtColor(im1Combine, im1Overlay, cv::COLOR_GRAY2BGR);
- cvtColor(im2Proc, im2, cv::COLOR_GRAY2BGR);
-
- // TPS is requested (only if FLANN succeeded)
- } else {
-
- Rcout << "MESSAGE: Running Thin-Plate-Spline Alignment" << endl;
-
- // Filtered points (inliers) based on the mask
- std::vector filtered_points1;
- std::vector filtered_points2;
- for (int i = 0; i < mask.rows; i++) {
- if (mask.at(i)) {
- filtered_points1.push_back(points1[i]);
- filtered_points2.push_back(points2[i]);
- }
- }
- removeCloseMatches(filtered_points1, filtered_points2);
-
- // transform query
- std::vector filtered_points1_reg;
- if (h.rows == 2){
- cv::transform(filtered_points1, filtered_points1_reg, h);
- } else {
- cv::perspectiveTransform(filtered_points1, filtered_points1_reg, h);
- }
-
- // get TPS matches
- std::vector matches;
- for (unsigned int i = 0; i < filtered_points2.size(); i++)
- matches.push_back(cv::DMatch(i, i, 0));
-
- // calculate TPS transformation
- Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
- tps->estimateTransformation(filtered_points2, filtered_points1_reg, matches);
-
- // save keypoints
- keypoints[0] = point2fToNumericMatrix(filtered_points2);
- keypoints[1] = point2fToNumericMatrix(filtered_points1_reg);
-
- // determine extension limits for both images
- int y_max = max(im1Warp.rows, im2.rows);
- int x_max = max(im1Warp.cols, im2.cols);
-
- // extend images
- cv::copyMakeBorder(im1Warp, im1Warp, 0.0, (int) (y_max - im1Warp.rows), 0.0, (x_max - im1Warp.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
- cv::copyMakeBorder(im1NormalWarp, im1NormalWarp, 0.0, (int) (y_max - im1NormalWarp.rows), 0.0, (x_max - im1NormalWarp.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
-
- // transform image
- Mat im1Reg_Warp_nonrigid;
- Mat im1Reg_NormalWarp_nonrigid;
- tps->warpImage(im1Warp, im1Reg_Warp_nonrigid);
- tps->warpImage(im1NormalWarp, im1Reg_NormalWarp_nonrigid);
-
- // resize image
- cv::Mat im1Reg_NormalWarp_nonrigid_cropped = im1Reg_NormalWarp_nonrigid(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
- im1Reg_NormalWarp_nonrigid = im1Reg_NormalWarp_nonrigid_cropped.clone();
-
- cv::Mat im1Reg_Warp_nonrigid_cropped = im1Reg_Warp_nonrigid(cv::Range(0,im2Proc.size().height), cv::Range(0,im2Proc.size().width));
- im1Reg_Warp_nonrigid = im1Reg_Warp_nonrigid_cropped.clone();
-
- // change color map
- cv::addWeighted(im2Proc, 0.7, im1Reg_Warp_nonrigid, 0.3, 0, im1Combine);
-
- // Reverse process
- im1Reg = reversepreprocessImage(im1Reg_NormalWarp_nonrigid, flipflop_ref, rotate_ref);
-
- // return as rgb
- cvtColor(im1Combine, im1Overlay, cv::COLOR_GRAY2BGR);
- cvtColor(im2Proc, im2, cv::COLOR_GRAY2BGR);
- }
-
- // resize image to visualize faster later in Shiny
- im2 = resize_image(im2, 500);
- im1Overlay = resize_image(im1Overlay, 500);
}
\ No newline at end of file
diff --git a/src/auxiliary.cpp b/src/auxiliary.cpp
index ba4a455f..fa78ef8f 100644
--- a/src/auxiliary.cpp
+++ b/src/auxiliary.cpp
@@ -35,81 +35,12 @@ Rcpp::NumericMatrix replaceNaMatrix(Rcpp::NumericMatrix mat, int replace) {
return mat;
}
-////
-// memory
-////
-
-// // memory check
-// void log_mem_usage(const std::string& label = "") {
-// struct rusage usage;
-// getrusage(RUSAGE_SELF, &usage);
-// long rss_b = usage.ru_maxrss;
-//
-// double rss_kb = rss_b / 1024.0;
-// double rss_mb = rss_kb / 1024.0;
-// double rss_gb = rss_mb / 1024.0;
-//
-// Rcpp::Rcout << "Used Memory [" << label << "]: " << rss_gb << " GB" << std::endl;
-// }
-//
-// void log_mem_macos(const std::string& label = "") {
-// mach_task_basic_info info;
-// mach_msg_type_number_t size = MACH_TASK_BASIC_INFO_COUNT;
-// kern_return_t kr = task_info(mach_task_self(), MACH_TASK_BASIC_INFO,
-// (task_info_t)&info, &size);
-//
-// if (kr != KERN_SUCCESS) {
-// Rcpp::Rcerr << "[MEM " << label << "] Failed to get memory info.\n";
-// return;
-// }
-//
-// double rss_gb = static_cast(info.resident_size) / (1024.0 * 1024.0 * 1024.0);
-// double virt_gb = static_cast(info.virtual_size) / (1024.0 * 1024.0 * 1024.0);
-//
-// Rcpp::Rcout << "[MEM " << label << "] Resident (RSS): "
-// << rss_gb << " GB, Virtual: " << virt_gb << " GB\n";
-// }
-//
-// double object_size_long(long bsize) {
-//
-// double rss_kb = bsize / 1024.0;
-// double rss_mb = rss_kb / 1024.0;
-// double rss_gb = rss_mb / 1024.0;
-//
-// return rss_gb;
-// }
-//
-// double object_size_double(double bsize) {
-//
-// double rss_kb = bsize / 1024;
-// double rss_mb = rss_kb / 1024;
-// double rss_gb = rss_mb / 1024;
-//
-// return rss_gb;
-// }
-//
-// double get_resident_bytes() {
-// mach_task_basic_info info;
-// mach_msg_type_number_t size = MACH_TASK_BASIC_INFO_COUNT;
-// if (task_info(mach_task_self(), MACH_TASK_BASIC_INFO,
-// (task_info_t)&info, &size) != KERN_SUCCESS) {
-// return 0;
-// }
-// return static_cast(info.resident_size);
-// }
-//
-// double bytes_to_gb(double bytes) {
-// return bytes / (1024.0 * 1024.0 * 1024.0);
-// }
-
////
// Conversion
////
// Function to convert a cv::Mat object to a RawVector for magick images
Rcpp::RawVector matToImage(const cv::Mat &mat) {
- // profiler
- // MemProfiler mp("Mat -> Image");
// Create RawVector object
Rcpp::RawVector rawvec(mat.total() * mat.elemSize());
@@ -123,8 +54,6 @@ Rcpp::RawVector matToImage(const cv::Mat &mat) {
// Function to convert a RawVector for magick images to a cv::Mat object
cv::Mat imageToMat(Rcpp::RawVector &image_data, int width, int height) {
- // profiler
- // MemProfiler mp("Image -> Mat");
// Create cv::Mat object
cv::Mat mat(height, width, CV_8UC3, image_data.begin());
@@ -135,6 +64,23 @@ cv::Mat imageToMat(Rcpp::RawVector &image_data, int width, int height) {
return mat;
}
+// Function to convert a cv::Mat object to a RawVector for magick images
+Rcpp::IntegerVector matToMask(const cv::Mat &mat) {
+
+ cv::Mat intMat;
+ mat.convertTo(intMat, CV_32S);
+ Rcpp::IntegerVector intvec(intMat.total());
+ std::memcpy(
+ intvec.begin(),
+ intMat.data,
+ static_cast(intvec.size()) * sizeof(int)
+ );
+ // intvec.attr("dim") = Rcpp::Dimension(intMat.rows, intMat.cols);
+ intvec.attr("dim") = Rcpp::Dimension(intMat.cols, intMat.rows);
+
+ return intvec;
+}
+
// Function to convert a NumericMatrix object to a cv::Mat
cv::Mat numericMatrixToMat(Rcpp::NumericMatrix nm) {
cv::Mat m(nm.rows(), nm.cols(), CV_64F);
@@ -197,6 +143,17 @@ std::vector Point2fToDoubleVector(std::vector &points) {
return vec;
}
+// Function to convert a cv::Keypoint object to a std::vector
+std::vector KeyPointToPoint2f(std::vector &keypoints) {
+ int n = keypoints.size();
+ std::vector points;
+
+ for (int i = 0; i < n; i++) {
+ points.push_back(keypoints[i].pt);
+ }
+ return points;
+}
+
// Function to convert a cv::Point2f object to a cv::Mat
std::vector matToPoint2f(cv::Mat &mat) {
std::vector points;
@@ -289,3 +246,61 @@ double cppSD(std::vector &points)
std::vector().swap(inVec);
return std::sqrt( sd / (n-1) );
}
+
+double meanDistances(std::vector& pts1,
+ std::vector& pts2)
+{
+ if (pts1.size() != pts2.size() || pts1.empty())
+ return 0.0;
+
+ double sumDist = 0.0;
+ for (size_t i = 0; i < pts1.size(); ++i)
+ {
+ const double dx = pts1[i].x - pts2[i].x;
+ const double dy = pts1[i].y - pts2[i].y;
+ sumDist += std::sqrt(dx * dx + dy * dy);
+ }
+
+ return sumDist / pts1.size();
+}
+
+double medianDistances(std::vector& pts1,
+ std::vector& pts2)
+{
+ if (pts1.size() != pts2.size() || pts1.empty())
+ return 0.0;
+
+ std::vector distances;
+ distances.reserve(pts1.size());
+
+ for (size_t i = 0; i < pts1.size(); ++i)
+ {
+ const double dx = pts1[i].x - pts2[i].x;
+ const double dy = pts1[i].y - pts2[i].y;
+ distances.push_back(std::sqrt(dx * dx + dy * dy));
+ }
+
+ const size_t n = distances.size();
+ const size_t mid = n / 2;
+
+ std::nth_element(distances.begin(),
+ distances.begin() + mid,
+ distances.end());
+
+ if (n % 2 == 1)
+ {
+ return distances[mid];
+ }
+ else
+ {
+ double upper = distances[mid];
+
+ std::nth_element(distances.begin(),
+ distances.begin() + mid - 1,
+ distances.end());
+
+ double lower = distances[mid - 1];
+
+ return (lower + upper) / 2.0;
+ }
+}
\ No newline at end of file
diff --git a/src/auxiliary.h b/src/auxiliary.h
index 0985e819..9d9d5271 100644
--- a/src/auxiliary.h
+++ b/src/auxiliary.h
@@ -19,6 +19,10 @@ Rcpp::NumericMatrix replaceNaMatrix(Rcpp::NumericMatrix mat, int replace);
Rcpp::RawVector matToImage(const cv::Mat &mat);
cv::Mat imageToMat(Rcpp::RawVector &image_data, int width, int height);
+// cv::Mat vs Rcpp::RawVector(Image) with 2 dim (mostly for masks)
+Rcpp::IntegerVector matToMask(const cv::Mat &mat);
+// Rcpp::RawVector matToMask(const cv::Mat &mat);
+
// cv::Mat vs Rcpp::NumericMatrix
cv::Mat numericMatrixToMat(Rcpp::NumericMatrix nm);
Rcpp::NumericMatrix matToNumericMatrix(cv::Mat m);
@@ -37,6 +41,9 @@ cv::Mat IntVectorToMat(std::vector &points);
// std::vector vs std::vector
std::vector KeyPointToDoubleVector(std::vector &points);
std::vector Point2fToDoubleVector(std::vector &points);
+
+// std::vector vs std::vector
+std::vector KeyPointToPoint2f(std::vector &keypoints);
////
// stats
@@ -46,32 +53,8 @@ std::vector Point2fToDoubleVector(std::vector &points);
double cppSD(std::vector &points);
double cppSD(std::vector &points);
-////
-// memory
-////
-
-// void log_mem_usage(const std::string& label);
-// void log_mem_macos(const std::string& label);
-// double object_size_long(long bsize);
-// double object_size_double(double bsize);
-// double get_resident_bytes();
-// double bytes_to_gb(double bytes);
-//
-// struct MemProfiler {
-// size_t start;
-// std::string label;
-//
-// MemProfiler(const std::string& lbl) : label(lbl) {
-// start = get_resident_bytes();
-// }
-//
-// ~MemProfiler() {
-// size_t end = get_resident_bytes();
-// double diff = (double) end - (double) start;
-// if(diff < 0.0) diff = 0.0;
-// double diff_gb = bytes_to_gb(diff);
-// Rcpp::Rcout << "[MEM] " << label << ": +" << diff_gb << " GB" << std::endl;
-// }
-// };
+// mean distance between points
+double meanDistances(std::vector &pts1, std::vector &pts2);
+double medianDistances(std::vector &pts1, std::vector &pts2);
#endif
\ No newline at end of file
diff --git a/src/image.cpp b/src/image.cpp
index 5152d859..07ab3fba 100644
--- a/src/image.cpp
+++ b/src/image.cpp
@@ -126,18 +126,41 @@ void scaledDrawMatches(cv::Mat im1, std::vector &keypoints1,
// draw matches
drawMatches(im1, keypoints1, im2, keypoints2, top_matches, imMatches);
}
+
+cv::Mat warpTPSImage(cv::Mat& ref_image,
+ cv::Mat& query_image,
+ Ptr& tps,
+ const int border_y,
+ const int border_x,
+ const int interpolation){
-// [[Rcpp::export]]
-Rcpp::RawVector warpRcppImage(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
- Rcpp::List mapping,
- const int width1, const int height1,
- const int width2, const int height2)
-{
- // Read reference image
- cv::Mat imReference = imageToMat(ref_image, width1, height1);
+ // determine extension limits for both images
+ int y_max = max(query_image.rows, border_y);
+ int x_max = max(query_image.cols, border_x);
+
+ // extend images
+ cv::copyMakeBorder(query_image, query_image,
+ 0.0, (int) (y_max - query_image.rows),
+ 0.0, (x_max - query_image.cols),
+ cv::BORDER_CONSTANT, Scalar(0, 0, 0));
+
+ // transform image
+ cv::Mat query_image_reg;
+ tps->warpImage(query_image, query_image_reg, interpolation);
+
+ // resize image
+ query_image_reg = query_image_reg(
+ cv::Range(0,ref_image.size().height),
+ cv::Range(0,ref_image.size().width));
+
+ // return
+ return query_image_reg.clone();
+}
+
+void warpImage(cv::Mat& ref_image,
+ cv::Mat& query_image,
+ Rcpp::List mapping){
- // Read image to be aligned
- cv::Mat im = imageToMat(query_image, width2, height2);
cv::Mat im_temp;
// list
@@ -155,11 +178,11 @@ Rcpp::RawVector warpRcppImage(Rcpp::RawVector ref_image, Rcpp::RawVector query_i
// transform coordinates
if(h.rows == 2){
- cv::warpAffine(im, im_temp, h, imReference.size());
+ cv::warpAffine(query_image, im_temp, h, ref_image.size());
} else {
- cv::warpPerspective(im, im_temp, h, imReference.size());
+ cv::warpPerspective(query_image, im_temp, h, ref_image.size());
}
- im = im_temp;
+ query_image = im_temp;
}
// non-rigid warping
@@ -177,35 +200,47 @@ Rcpp::RawVector warpRcppImage(Rcpp::RawVector ref_image, Rcpp::RawVector query_i
// calculate transformation
Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
- tps->estimateTransformation(ref_mat, query_mat, matches);
-
- // determine extension limits for both images
- int y_max = max(im.rows, imReference.rows);
- int x_max = max(im.cols, imReference.cols);
-
- // extend images
- cv::copyMakeBorder(im, im, 0.0, (int) (y_max - im.rows), 0.0, (x_max - im.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
- // transform image
- tps->warpImage(im, im_temp);
+ // estimate transformation
+ tps->estimateTransformation(ref_mat, query_mat, matches);
- // resize image
- cv::Mat im_temp_cropped = im_temp(cv::Range(0,imReference.size().height), cv::Range(0,imReference.size().width));
- im_temp = im_temp_cropped.clone();
+ // transform image using trained tps
+ im_temp = warpTPSImage(ref_image, query_image, tps,
+ ref_image.rows, ref_image.cols, cv::INTER_LINEAR);
} else {
// pass registered object
- im_temp = im;
+ im_temp = query_image;
}
- im = im_temp;
+ query_image = im_temp;
}
+}
+
+// [[Rcpp::export]]
+Rcpp::RawVector warpRcppImage(Rcpp::RawVector ref_image,
+ Rcpp::RawVector query_image,
+ Rcpp::List mapping,
+ const int width1, const int height1,
+ const int width2, const int height2)
+{
+ // Read reference image
+ cv::Mat imReference = imageToMat(ref_image, width1, height1);
+
+ // Read image to be aligned
+ cv::Mat im = imageToMat(query_image, width2, height2);
+
+ // warp image
+ warpImage(imReference, im, mapping);
// return
return matToImage(im);
}
+/////
+// Legacy ////
+/////
// [[Rcpp::export]]
Rcpp::RawVector warpImageAuto(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
diff --git a/src/image.h b/src/image.h
index 6a9a7b26..c1cfc861 100644
--- a/src/image.h
+++ b/src/image.h
@@ -2,6 +2,10 @@
#include
#include "opencv2/shape/shape_transformer.hpp"
+using namespace Rcpp;
+using namespace std;
+using namespace cv;
+
#ifndef IMAGE_H
#define IMAGE_H
@@ -9,8 +13,10 @@
// Processing
////
-cv::Mat preprocessImage(cv::Mat &im, const bool invert, const char* flipflop, const char* rotate);
-cv::Mat reversepreprocessImage(cv::Mat &im, const char* flipflop, const char* rotate);
+cv::Mat preprocessImage(cv::Mat &im, const bool invert,
+ const char* flipflop, const char* rotate);
+cv::Mat reversepreprocessImage(cv::Mat &im,
+ const char* flipflop, const char* rotate);
cv::Mat resize_image(cv::Mat &im, int width);
std::vector resize_keypoints(std::vector &keypoints,
cv::Mat &im,
@@ -24,17 +30,27 @@ void scaledDrawMatches(cv::Mat im1, std::vector &keypoints1,
// Warping
////
-Rcpp::RawVector warpImage(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
+cv::Mat warpTPSImage(cv::Mat& ref_image,
+ cv::Mat& query_image,
+ Ptr& tps,
+ const int border_y,
+ const int border_x,
+ const int interpolation);
+
+Rcpp::RawVector warpImage(Rcpp::RawVector ref_image,
+ Rcpp::RawVector query_image,
Rcpp::List mapping,
const int width1, const int height1,
const int width2, const int height2);
-Rcpp::RawVector warpImageAuto(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
+Rcpp::RawVector warpImageAuto(Rcpp::RawVector ref_image,
+ Rcpp::RawVector query_image,
Rcpp::List mapping,
const int width1, const int height1,
const int width2, const int height2);
-Rcpp::RawVector warpImageManual(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
+Rcpp::RawVector warpImageManual(Rcpp::RawVector ref_image,
+ Rcpp::RawVector query_image,
Rcpp::List mapping,
const int width1, const int height1,
const int width2, const int height2);
diff --git a/src/manual_registration.cpp b/src/manual_registration.cpp
index a1614b5e..fd165a7c 100644
--- a/src/manual_registration.cpp
+++ b/src/manual_registration.cpp
@@ -4,8 +4,11 @@
#include
#include "opencv2/shape/shape_transformer.hpp"
-// Auxiliary
+// Library
#include "auxiliary.h"
+#include "image.h"
+#include "metrics.h"
+#include "matte_mi.h"
// Namespaces
using namespace Rcpp;
@@ -13,8 +16,17 @@ using namespace std;
using namespace cv;
// align images with TPS algorithm
-void alignImagesTPS(Mat &im1, Mat &im2, Mat &im1Reg, Rcpp::List &keypoints,
- Rcpp::NumericMatrix query_landmark, Rcpp::NumericMatrix reference_landmark)
+void alignImagesTPS(Mat &im1,
+ Mat &im2,
+ Mat &im1Reg,
+ Rcpp::List &keypoints,
+ Rcpp::NumericMatrix query_landmark,
+ Rcpp::NumericMatrix reference_landmark,
+ const bool invert_query,
+ const bool invert_ref,
+ const bool compute_matte_map,
+ Mat1d &accuracyMatte,
+ std::map &accuracy)
{
// seed
@@ -31,6 +43,9 @@ void alignImagesTPS(Mat &im1, Mat &im2, Mat &im1Reg, Rcpp::List &keypoints,
for (unsigned int i = 0; i < ref_mat.size(); i++)
matches.push_back(cv::DMatch(i, i, 0));
+ // message
+ Rcout << "Running Coarse Alignment (Thin-Plate-Spline)" << endl;
+
// calculate transformation
Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
tps->estimateTransformation(ref_mat, query_mat, matches);
@@ -39,25 +54,84 @@ void alignImagesTPS(Mat &im1, Mat &im2, Mat &im1Reg, Rcpp::List &keypoints,
keypoints[0] = point2fToNumericMatrix(ref_mat);
keypoints[1] = point2fToNumericMatrix(query_mat);
- // determine extension limits for both images
- int y_max = max(im1.rows, im2.rows);
- int x_max = max(im1.cols, im2.cols);
-
- // extend images
- cv::copyMakeBorder(im1, im1, 0.0, (int) (y_max - im1.rows), 0.0, (x_max - im1.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
+ // transform image using trained tps
+ im1Reg = warpTPSImage(im2, im1, tps,
+ im2.rows, im2.cols, cv::INTER_LINEAR);
+
+ // process
+ Mat im1Proc, im2Proc;
+ cvtColor(im1Reg, im1Proc, cv::COLOR_BGR2GRAY);
+ cvtColor(im2, im2Proc, cv::COLOR_BGR2GRAY);
+ im1Proc = preprocessImage(im1Proc, invert_query, "None", "0");
+ im2Proc = preprocessImage(im2Proc, invert_ref, "None", "0");
+
+ // get alignment mask
+ cv::Mat alignmentMask = generateOverlapMask(im2Proc,
+ tps,
+ im1.size());
+
+ // get alignment metrics
+ accuracy = getAlignmentMetrics(im1Proc, im2Proc, alignmentMask, "Coarse");
+ if(compute_matte_map)
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, alignmentMask, 50);
+}
- // transform image
- tps->warpImage(im1, im1Reg);
+// align images with TPS algorithm
+void alignImagesTPS_points(Rcpp::NumericMatrix &query_data,
+ Rcpp::NumericMatrix &dataReg,
+ Rcpp::List &keypoints,
+ Rcpp::NumericMatrix query_landmark,
+ Rcpp::NumericMatrix reference_landmark)
+{
+ // seed
+ cv::setRNGSeed(0);
+ RNG rng(12345);
+ Scalar value;
+
+ // Get landmarks as Point2f
+ std::vector query_mat = numericMatrixToPoint2f(query_landmark);
+ std::vector ref_mat = numericMatrixToPoint2f(reference_landmark);
+
+ // Get data as Point2f
+ std::vector query_data_mat = numericMatrixToPoint2f(query_data);
- // resize image
- cv::Mat im1Reg_cropped = im1Reg(cv::Range(0,im2.size().height), cv::Range(0,im2.size().width));
- im1Reg = im1Reg_cropped.clone();
+ // get matches
+ std::vector matches;
+ for (unsigned int i = 0; i < ref_mat.size(); i++)
+ matches.push_back(cv::DMatch(i, i, 0));
+
+ // calculate transformation
+ Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
+ tps->estimateTransformation(ref_mat, query_mat, matches);
+
+ // apply transformation to coordinates
+ std::vector query_data_reg;
+ tps->applyTransformation(query_data_mat, query_data_reg);
+
+ // save keypoints
+ keypoints[0] = point2fToNumericMatrix(ref_mat);
+ keypoints[1] = point2fToNumericMatrix(query_mat);
+
+ // transform points
+ dataReg = point2fToNumericMatrix(query_data_reg);
}
// align images with FLANN algorithm
-void alignImagesAffineTPS(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h, Rcpp::List &keypoints,
- Rcpp::NumericMatrix query_landmark, Rcpp::NumericMatrix reference_landmark,
- const bool run_Affine, const bool run_TPS)
+void alignImagesAffineTPS(Mat &im1,
+ Mat &im2,
+ Mat &im1Reg,
+ Mat &h,
+ Rcpp::List &keypoints,
+ Rcpp::NumericMatrix query_landmark,
+ Rcpp::NumericMatrix reference_landmark,
+ const bool invert_query,
+ const bool invert_ref,
+ const bool run_Affine,
+ const bool run_TPS,
+ const bool compute_matte_map,
+ Mat1d &accuracyMatte,
+ std::map &accuracy_coarse,
+ std::map &accuracy_fine)
{
// seed
cv::setRNGSeed(0);
@@ -75,6 +149,8 @@ void alignImagesAffineTPS(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h, Rcpp::List &k
// calculate homography transformation
Rcout << "Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
+
+ // warp image
Mat im1Affine;
std::vector query_reg;
if(run_Affine){
@@ -87,16 +163,33 @@ void alignImagesAffineTPS(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h, Rcpp::List &k
cv::perspectiveTransform(query_mat, query_reg, h);
}
+ // get alignment metrics for Coarse registration
+ cv::Mat alignmentMask = generateOverlapMask(im2.size(),
+ h,
+ im1.size());
+
+ // get matte metric, process image before
+ Mat im1Proc, im2Proc;
+ cvtColor(im1Affine, im1Proc, cv::COLOR_BGR2GRAY);
+ cvtColor(im2, im2Proc, cv::COLOR_BGR2GRAY);
+ im1Proc = preprocessImage(im1Proc, invert_query, "None", "0");
+ im2Proc = preprocessImage(im2Proc, invert_ref, "None", "0");
+ accuracy_coarse = getAlignmentMetrics(im1Proc, im2Proc, alignmentMask, "Coarse");
+
if(!run_TPS){
+ // compute matte map if finishing alignment
+ if(compute_matte_map)
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, alignmentMask, 50);
+
// clone and exit
im1Reg = im1Affine.clone();
} else {
// message
- Rcout << "Running Thin-Plate-Spline Alignment" << endl;
-
+ Rcout << "Running Fine Alignment (Thin-Plate-Spline)" << endl;
+
// calculate TPS transformation
Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
tps->estimateTransformation(ref_mat, query_reg, matches);
@@ -105,70 +198,210 @@ void alignImagesAffineTPS(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h, Rcpp::List &k
keypoints[0] = point2fToNumericMatrix(ref_mat);
keypoints[1] = point2fToNumericMatrix(query_reg);
- // determine extension limits for both images
- int y_max = max(im1Affine.rows, im2.rows);
- int x_max = max(im1Affine.cols, im2.cols);
-
- // extend images
- cv::copyMakeBorder(im1Affine, im1Affine, 0.0, (int) (y_max - im1Affine.rows), 0.0, (x_max - im1Affine.cols), cv::BORDER_CONSTANT, Scalar(0, 0, 0));
+ // warp overlap mask
+ alignmentMask = warpTPSImage(im2, alignmentMask, tps,
+ im2.rows, im2.cols,
+ cv::INTER_NEAREST);
- // transform image
- tps->warpImage(im1Affine, im1Reg);
+ // transform image using trained tps
+ im1Reg = warpTPSImage(im2, im1Affine, tps,
+ im2.rows, im2.cols, cv::INTER_LINEAR);
- // resize image
- cv::Mat im1Reg_cropped = im1Reg(cv::Range(0,im2.size().height), cv::Range(0,im2.size().width));
- im1Reg = im1Reg_cropped.clone();
+ // get matte metric, process
+ // im2 is already processed
+ Mat im1Proc;
+ cvtColor(im1Reg, im1Proc, cv::COLOR_BGR2GRAY);
+ im1Proc = preprocessImage(im1Proc, invert_query, "None", "0");
+ accuracy_fine = getAlignmentMetrics(im1Proc, im2Proc, alignmentMask, "Fine");
+ if(compute_matte_map)
+ accuracyMatte = MatteMIMap(im2Proc, im1Proc, alignmentMask, 50);
+ }
+}
+
+// align images with FLANN algorithm
+void alignImagesAffineTPS_points(Rcpp::NumericMatrix &query_data,
+ Rcpp::NumericMatrix &dataReg,
+ Mat &h, Rcpp::List &keypoints,
+ Rcpp::NumericMatrix query_landmark,
+ Rcpp::NumericMatrix reference_landmark,
+ const bool run_Affine,
+ const bool run_TPS)
+{
+ // seed
+ cv::setRNGSeed(0);
+ RNG rng(12345);
+ Scalar value;
+
+ // message
+ Rcout << "Running Coarse Alignment (Manual)" << endl;
+
+ // Get landmarks as Point2f
+ std::vector query_mat = numericMatrixToPoint2f(query_landmark);
+ std::vector ref_mat = numericMatrixToPoint2f(reference_landmark);
+
+ // Get data as Point2f
+ std::vector query_data_mat = numericMatrixToPoint2f(query_data);
+
+ // get matches
+ std::vector matches;
+ for (unsigned int i = 0; i < ref_mat.size(); i++)
+ matches.push_back(cv::DMatch(i, i, 0));
+
+ // calculate homography transformation
+ Rcout << "Calculating" << (run_Affine ? " (Affine) " : " (Homography) ") << "Transformation Matrix" << endl;
+
+ std::vector query_reg;
+ std::vector query_data_reg;
+ if(run_Affine){
+ h = estimateAffine2D(query_mat, ref_mat);
+ cv::transform(query_mat, query_reg, h);
+ cv::transform(query_data_mat, query_data_reg, h);
+ } else {
+ h = findHomography(query_mat, ref_mat);
+ cv::perspectiveTransform(query_mat, query_reg, h);
+ cv::perspectiveTransform(query_data_mat, query_data_reg, h);
}
+
+ if(run_TPS){
+
+ // message
+ Rcout << "Running Fine Alignment (Thin-Plate-Spline)" << endl;
+
+ // calculate TPS transformation
+ Ptr tps = cv::createThinPlateSplineShapeTransformer(0);
+ tps->estimateTransformation(ref_mat, query_reg, matches);
+
+ // apply transformation to coordinates
+ tps->applyTransformation(query_data_reg, query_data_reg);
+
+ // save keypoints
+ keypoints[0] = point2fToNumericMatrix(ref_mat);
+ keypoints[1] = point2fToNumericMatrix(query_reg);
+
+ }
+
+ // save data
+ dataReg = point2fToNumericMatrix(query_data_reg);
}
// [[Rcpp::export]]
-Rcpp::List manual_registeration_rawvector(Rcpp::RawVector ref_image, Rcpp::RawVector query_image,
- Rcpp::NumericMatrix reference_landmark, Rcpp::NumericMatrix query_landmark,
- const int width1, const int height1,
- const int width2, const int height2,
- Rcpp::String method, Rcpp::String nonrigid)
+Rcpp::List manual_registeration_rawvector(Rcpp::RawVector ref_image,
+ Rcpp::RawVector query_image,
+ Rcpp::NumericMatrix reference_landmark,
+ Rcpp::NumericMatrix query_landmark,
+ const int width1,
+ const int height1,
+ const int width2,
+ const int height2,
+ const bool invert_query,
+ const bool invert_ref,
+ Rcpp::String method,
+ Rcpp::String nonrigid,
+ const bool compute_matte_map = true)
{
// Return data
- Rcpp::List out(2);
+ Rcpp::List out(5);
Rcpp::List out_trans(2);
Rcpp::List keypoints(2);
Mat imReg, h;
+ Mat1d accuracyMatte;
+ std::map accuracy_coarse, accuracy_fine;
+
+ // get params
+ const bool run_TPS = (strcmp(method.get_cstring(), "Homography + Non-Rigid") == 0 ||
+ strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0) &&
+ strcmp(nonrigid.get_cstring(), "TPS (OpenCV)") == 0;
+ const bool run_Affine = (strcmp(method.get_cstring(), "Affine") == 0 ||
+ strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0);
- // Read reference image
+ // Read reference and query images
cv::Mat imReference = imageToMat(ref_image, width1, height1);
-
- // Read image to be aligned
cv::Mat im = imageToMat(query_image, width2, height2);
-
-
+
// AffineHomography + Non-rigid (TPS)
if(strcmp(method.get_cstring(), "Non-Rigid") != 0){
- // const bool run_Affine = ((strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0) || (strcmp(method.get_cstring(), "Affine") == 0));
- // const bool run_TPS = (strcmp(method.get_cstring(), "Homography + Non-Rigid") == 0 || strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0);
- const bool run_TPS = (strcmp(method.get_cstring(), "Homography + Non-Rigid") == 0 ||
- strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0) &&
- strcmp(nonrigid.get_cstring(), "TPS (OpenCV)") == 0;
- const bool run_Affine = (strcmp(method.get_cstring(), "Affine") == 0 ||
- strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0);
alignImagesAffineTPS(im, imReference, imReg,
h, keypoints,
- query_landmark, reference_landmark, run_Affine, run_TPS);
+ query_landmark, reference_landmark,
+ invert_query,
+ invert_ref,
+ run_Affine,
+ run_TPS,
+ compute_matte_map,
+ accuracyMatte,
+ accuracy_coarse,
+ accuracy_fine);
}
// Non-rigid (TPS) only
if(strcmp(method.get_cstring(), "Non-Rigid") == 0){
alignImagesTPS(im, imReference, imReg,
keypoints,
- query_landmark, reference_landmark);
+ query_landmark,
+ reference_landmark,
+ invert_query,
+ invert_ref,
+ compute_matte_map,
+ accuracyMatte,
+ accuracy_coarse);
}
-
+
// transformation matrix, can be either a matrix, set of keypoints or both
out_trans[0] = matToNumericMatrix(h.clone());
out_trans[1] = keypoints;
out[0] = out_trans;
- // registered image
- out[1] = matToImage(imReg.clone());
-
+ // registered image and accuracy if exists
+ out[1] = matToImage(imReg.clone());
+ out[2] = matToNumericMatrix(accuracyMatte); // Matte MI metric
+ out[3] = accuracy_coarse;
+ out[4] = accuracy_fine;
+
return out;
-}
\ No newline at end of file
+}
+
+// [[Rcpp::export]]
+Rcpp::List manual_registeration_matrix(Rcpp::NumericMatrix query_data,
+ Rcpp::NumericMatrix reference_landmark,
+ Rcpp::NumericMatrix query_landmark,
+ Rcpp::String method,
+ Rcpp::String nonrigid)
+{
+ // Return data
+ Rcpp::List out(2);
+ Rcpp::List out_trans(2);
+ Rcpp::List keypoints(2);
+ Mat h;
+ Rcpp::NumericMatrix dataReg;
+
+ // get params
+ const bool run_TPS = (strcmp(method.get_cstring(), "Homography + Non-Rigid") == 0 ||
+ strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0) &&
+ strcmp(nonrigid.get_cstring(), "TPS (OpenCV)") == 0;
+ const bool run_Affine = (strcmp(method.get_cstring(), "Affine") == 0 ||
+ strcmp(method.get_cstring(), "Affine + Non-Rigid") == 0);
+
+ // AffineHomography + Non-rigid (TPS)
+ if(strcmp(method.get_cstring(), "Non-Rigid") != 0){
+ alignImagesAffineTPS_points(query_data, dataReg,
+ h, keypoints,
+ query_landmark, reference_landmark,
+ run_Affine, run_TPS);
+ out_trans[0] = matToNumericMatrix(h.clone());
+ }
+
+ if(strcmp(method.get_cstring(), "Non-Rigid") == 0){
+ alignImagesTPS_points(query_data, dataReg,
+ keypoints,
+ query_landmark,
+ reference_landmark);
+ }
+
+ // transformation matrix, can be either a matrix, set of keypoints or both
+ out_trans[1] = keypoints;
+ out[0] = out_trans;
+ out[1] = dataReg;
+
+ return out;
+}
+
\ No newline at end of file
diff --git a/src/mapping.cpp b/src/mapping.cpp
index 2a10b90a..c3bbca0b 100644
--- a/src/mapping.cpp
+++ b/src/mapping.cpp
@@ -66,13 +66,6 @@ Rcpp::NumericMatrix applyRcppMapping(Rcpp::NumericMatrix coords, Rcpp::List mapp
// apply transformation to coordinates
tps->applyTransformation(coords_mat, coords_temp);
- // // temp position
- // std::vector query_mat_tmp;
- // tps->applyTransformation(query_mat, query_mat_tmp);
- // Rcout << query_mat << endl;
- // Rcout << query_mat_tmp << endl;
- // Rcout << ref_mat << endl;
-
} else {
coords_temp = coords_mat;
}
diff --git a/src/matte_mi.cpp b/src/matte_mi.cpp
new file mode 100644
index 00000000..930949f6
--- /dev/null
+++ b/src/matte_mi.cpp
@@ -0,0 +1,435 @@
+#include "matte_mi.h"
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include
+
+namespace {
+
+using Pixel = unsigned char;
+
+struct IntensityRange {
+ double min;
+ double max;
+};
+
+struct ChunkSize {
+ int height = 50;
+ int width = 50;
+};
+
+struct GlobalCounts {
+ std::array fixed{};
+ std::array moving{};
+ std::size_t validPairs = 0U;
+};
+
+// Source pixels remain CV_8U. All Mattes arithmetic remains double.
+double cubicBSpline(double u) noexcept {
+ u = std::abs(u);
+ if (u < 1.0) {
+ const double u2 = u * u;
+ return (4.0 - 6.0 * u2 + 3.0 * u2 * u) / 6.0;
+ }
+ if (u < 2.0) {
+ const double t = 2.0 - u;
+ return t * t * t / 6.0;
+ }
+ return 0.0;
+}
+
+double scaleToBinPosition(
+ double value,
+ IntensityRange range,
+ double low,
+ double high) {
+ if (!std::isfinite(range.min) ||
+ !std::isfinite(range.max) ||
+ !(range.max > range.min)) {
+ throw std::invalid_argument("Invalid intensity range.");
+ }
+ value = std::clamp(value, range.min, range.max);
+ return low + (value - range.min) * (high - low) /
+ (range.max - range.min);
+}
+
+std::size_t roundToNearestEvenNonnegative(double x) noexcept {
+ const double lowerDouble = std::floor(x);
+ const double fraction = x - lowerDouble;
+ const auto lower = static_cast(lowerDouble);
+ if (fraction < 0.5) return lower;
+ if (fraction > 0.5) return lower + 1U;
+ return (lower % 2U == 0U) ? lower : lower + 1U;
+}
+
+bool isValidRange(IntensityRange range) noexcept {
+ return std::isfinite(range.min) &&
+ std::isfinite(range.max) &&
+ range.max > range.min;
+}
+
+int ceilDividePositive(int value, int divisor) noexcept {
+ return value / divisor + ((value % divisor) != 0 ? 1 : 0);
+}
+
+void validateInputs(
+ const cv::Mat& fixed,
+ const cv::Mat& moving,
+ const cv::Mat& mask,
+ int bins) {
+ if (fixed.empty() || moving.empty()) {
+ throw std::invalid_argument(
+ "fixed and moving images must not be empty.");
+ }
+ if (fixed.type() != CV_8UC1 || moving.type() != CV_8UC1) {
+ throw std::invalid_argument(
+ "The zero-conversion implementation expects fixed and moving "
+ "to be CV_8UC1.");
+ }
+ if (fixed.size() != moving.size()) {
+ throw std::invalid_argument(
+ "fixed and moving must have the same dimensions.");
+ }
+ if (!mask.empty() &&
+ (mask.type() != CV_8UC1 || mask.size() != fixed.size())) {
+ throw std::invalid_argument(
+ "mask must be empty or CV_8UC1 with the same dimensions.");
+ }
+ if (bins < 4) {
+ throw std::invalid_argument(
+ "bins must be >= 4 for cubic B-spline smoothing.");
+ }
+}
+
+GlobalCounts collectGlobalCounts(
+ const cv::Mat& fixed,
+ const cv::Mat& moving,
+ const cv::Mat& mask) {
+ GlobalCounts out;
+ for (int y = 0; y < fixed.rows; ++y) {
+ const Pixel* fixedRow = fixed.ptr(y);
+ const Pixel* movingRow = moving.ptr(y);
+ const Pixel* maskRow = mask.empty() ? nullptr : mask.ptr(y);
+ for (int x = 0; x < fixed.cols; ++x) {
+ if (maskRow != nullptr && maskRow[x] == 0U) continue;
+ ++out.fixed[fixedRow[x]];
+ ++out.moving[movingRow[x]];
+ ++out.validPairs;
+ }
+ }
+ return out;
+}
+
+Pixel valueAtRank(
+ const std::array& counts,
+ std::size_t rank) {
+ std::size_t cumulative = 0U;
+ for (std::size_t value = 0U; value < counts.size(); ++value) {
+ cumulative += counts[value];
+ if (rank < cumulative) return static_cast(value);
+ }
+ throw std::out_of_range("Percentile rank is out of range.");
+}
+
+// Exact NumPy-style linear percentile for CV_8U values, without sorting pixels.
+double percentileFromCounts(
+ const std::array& counts,
+ std::size_t count,
+ double percentile) {
+ if (count == 0U) {
+ throw std::invalid_argument("Cannot calculate an empty percentile.");
+ }
+ if (!std::isfinite(percentile) || percentile < 0.0 || percentile > 100.0) {
+ throw std::invalid_argument("Percentile must be in [0, 100].");
+ }
+ if (count == 1U) return static_cast(valueAtRank(counts, 0U));
+
+ const double index = static_cast(count - 1U) * percentile / 100.0;
+ const auto lowerIndex = static_cast(std::floor(index));
+ const auto upperIndex = static_cast(std::ceil(index));
+ const double fraction = index - static_cast(lowerIndex);
+ const double lower = static_cast(valueAtRank(counts, lowerIndex));
+ const double upper = static_cast(valueAtRank(counts, upperIndex));
+ return lower + fraction * (upper - lower);
+}
+
+IntensityRange minMaxRangeFromCounts(
+ const std::array& counts,
+ std::size_t count) {
+ if (count == 0U) {
+ const double nan = std::numeric_limits::quiet_NaN();
+ return {nan, nan};
+ }
+ std::size_t minimum = 0U;
+ while (minimum < counts.size() && counts[minimum] == 0U) ++minimum;
+ std::size_t maximum = counts.size() - 1U;
+ while (maximum > 0U && counts[maximum] == 0U) --maximum;
+ return {
+ static_cast(minimum),
+ static_cast(maximum)
+ };
+}
+
+void addMattesPair(
+ double fixedValue,
+ double movingValue,
+ IntensityRange fixedRange,
+ IntensityRange movingRange,
+ std::size_t bins,
+ std::vector& jointHistogram) {
+ const double fixedPosition = scaleToBinPosition(
+ fixedValue, fixedRange, 0.0, static_cast(bins - 1U));
+ std::size_t fixedBin = roundToNearestEvenNonnegative(fixedPosition);
+ fixedBin = std::min(fixedBin, bins - 1U);
+
+ const double movingPosition = scaleToBinPosition(
+ movingValue, movingRange, 1.0, static_cast(bins - 2U));
+ const auto baseBin = static_cast(std::floor(movingPosition));
+
+ for (int offset = -1; offset <= 2; ++offset) {
+ const std::ptrdiff_t movingBin = baseBin + offset;
+ if (movingBin < 0 || movingBin >= static_cast(bins)) {
+ continue;
+ }
+ const double weight = cubicBSpline(
+ movingPosition - static_cast(movingBin));
+ if (weight <= 0.0) continue;
+ jointHistogram[
+ fixedBin * bins + static_cast(movingBin)
+ ] += weight;
+ }
+}
+
+double mutualInformationFromHistogram(
+ const std::vector& jointHistogram,
+ std::size_t bins) {
+ const double nan = std::numeric_limits::quiet_NaN();
+ const double total = std::accumulate(
+ jointHistogram.begin(), jointHistogram.end(), 0.0);
+ if (!(total > 0.0) || !std::isfinite(total)) return nan;
+
+ std::vector px(bins, 0.0);
+ std::vector py(bins, 0.0);
+
+ for (std::size_t fixedBin = 0; fixedBin < bins; ++fixedBin) {
+ for (std::size_t movingBin = 0; movingBin < bins; ++movingBin) {
+ const double pxy = jointHistogram[
+ fixedBin * bins + movingBin
+ ] / total;
+ px[fixedBin] += pxy;
+ py[movingBin] += pxy;
+ }
+ }
+
+ double mi = 0.0;
+ for (std::size_t fixedBin = 0; fixedBin < bins; ++fixedBin) {
+ for (std::size_t movingBin = 0; movingBin < bins; ++movingBin) {
+ const double pxy = jointHistogram[
+ fixedBin * bins + movingBin
+ ] / total;
+ const double pxPy = px[fixedBin] * py[movingBin];
+ if (pxy > 0.0 && pxPy > 0.0) {
+ mi += pxy * std::log(pxy / pxPy);
+ }
+ }
+ }
+ return mi;
+}
+
+double mattesMiFromValues(
+ const Pixel* fixedValues,
+ const Pixel* movingValues,
+ std::size_t count,
+ std::size_t bins,
+ std::optional fixedRange = std::nullopt,
+ std::optional movingRange = std::nullopt) {
+ const double nan = std::numeric_limits::quiet_NaN();
+ if (count != 0U && (fixedValues == nullptr || movingValues == nullptr)) {
+ throw std::invalid_argument("Input value pointer is null.");
+ }
+ if (count < 2U) return nan;
+ if (bins < 4U) {
+ throw std::invalid_argument(
+ "bins must be >= 4 for cubic B-spline smoothing.");
+ }
+ if (bins > std::numeric_limits::max() / bins) {
+ throw std::length_error("Histogram dimensions are too large.");
+ }
+
+ if (!fixedRange.has_value() || !movingRange.has_value()) {
+ std::array fixedCounts{};
+ std::array movingCounts{};
+ for (std::size_t i = 0; i < count; ++i) {
+ ++fixedCounts[fixedValues[i]];
+ ++movingCounts[movingValues[i]];
+ }
+ if (!fixedRange.has_value()) {
+ fixedRange = minMaxRangeFromCounts(fixedCounts, count);
+ }
+ if (!movingRange.has_value()) {
+ movingRange = minMaxRangeFromCounts(movingCounts, count);
+ }
+ }
+
+ if (!isValidRange(*fixedRange) || !isValidRange(*movingRange)) return nan;
+
+ std::vector jointHistogram(bins * bins, 0.0);
+ for (std::size_t i = 0; i < count; ++i) {
+ addMattesPair(
+ static_cast(fixedValues[i]),
+ static_cast(movingValues[i]),
+ *fixedRange,
+ *movingRange,
+ bins,
+ jointHistogram);
+ }
+ return mutualInformationFromHistogram(jointHistogram, bins);
+}
+
+} // namespace
+
+cv::Mat1d MatteMIMap(
+ const cv::Mat& fixed,
+ const cv::Mat& moving,
+ const cv::Mat& mask,
+ int bins) {
+ validateInputs(fixed, moving, mask, bins);
+
+ constexpr ChunkSize chunkSize{};
+ constexpr std::size_t minValidPixels = 100U;
+ constexpr double minValidFraction = 0.10;
+ constexpr double lowerPercentile = 0.5;
+ constexpr double upperPercentile = 99.5;
+
+ const GlobalCounts global = collectGlobalCounts(fixed, moving, mask);
+ if (global.validPairs == 0U) {
+ throw std::invalid_argument("The mask contains no valid pixels.");
+ }
+
+ const IntensityRange fixedRange{
+ percentileFromCounts(global.fixed, global.validPairs, lowerPercentile),
+ percentileFromCounts(global.fixed, global.validPairs, upperPercentile)
+ };
+ const IntensityRange movingRange{
+ percentileFromCounts(global.moving, global.validPairs, lowerPercentile),
+ percentileFromCounts(global.moving, global.validPairs, upperPercentile)
+ };
+
+ if (!isValidRange(fixedRange)) {
+ throw std::invalid_argument("Invalid fixed intensity range.");
+ }
+ if (!isValidRange(movingRange)) {
+ throw std::invalid_argument("Invalid moving intensity range.");
+ }
+
+ const int nRows = ceilDividePositive(fixed.rows, chunkSize.height);
+ const int nCols = ceilDividePositive(fixed.cols, chunkSize.width);
+ cv::Mat1d nmiMap(nRows, nCols);
+ nmiMap.setTo(cv::Scalar(std::numeric_limits::quiet_NaN()));
+
+ const std::size_t maxChunkPixels =
+ static_cast(std::min(chunkSize.height, fixed.rows)) *
+ static_cast(std::min(chunkSize.width, fixed.cols));
+
+ std::vector fixedChunkValues;
+ std::vector movingChunkValues;
+ fixedChunkValues.reserve(maxChunkPixels);
+ movingChunkValues.reserve(maxChunkPixels);
+
+ for (int row = 0; row < nRows; ++row) {
+ for (int col = 0; col < nCols; ++col) {
+ const int y0 = row * chunkSize.height;
+ const int x0 = col * chunkSize.width;
+ const int y1 = y0 + std::min(chunkSize.height, fixed.rows - y0);
+ const int x1 = x0 + std::min(chunkSize.width, fixed.cols - x0);
+ const std::size_t totalPixels =
+ static_cast(y1 - y0) *
+ static_cast(x1 - x0);
+
+ fixedChunkValues.clear();
+ movingChunkValues.clear();
+
+ for (int y = y0; y < y1; ++y) {
+ const Pixel* fixedRow = fixed.ptr(y);
+ const Pixel* movingRow = moving.ptr(y);
+ const Pixel* maskRow = mask.empty() ? nullptr : mask.ptr(y);
+ for (int x = x0; x < x1; ++x) {
+ if (maskRow != nullptr && maskRow[x] == 0U) continue;
+ fixedChunkValues.push_back(fixedRow[x]);
+ movingChunkValues.push_back(movingRow[x]);
+ }
+ }
+
+ const std::size_t validPixels = fixedChunkValues.size();
+ if (validPixels < minValidPixels) continue;
+
+ const double validFraction =
+ static_cast