5_test.py

import numpy as np
import os
import tensorflow as tf
import cv2
import random

from utils import label_map_util
from utils import visualization_utils as vis_util
from distutils.version import StrictVersion

# module level variables ##############################################################################################
TEST_IMAGE_DIR = os.getcwd() +  "/final_test_images"
FROZEN_INFERENCE_GRAPH_LOC = os.getcwd() + "/inference_graph/frozen_inference_graph.pb"
LABELS_LOC = os.getcwd() + "/" + "label_map.pbtxt"
NUM_CLASSES = 2

#######################################################################################################################
def main():
    print("starting program . . .")

    if StrictVersion(tf.__version__) < StrictVersion('1.5.0'):
        raise ImportError('Please upgrade your tensorflow installation to v1.5.* or later!')
    # end if

    # load a (frozen) TensorFlow model into memory
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(FROZEN_INFERENCE_GRAPH_LOC, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
        # end with
    # end with

    # Loading label map
    label_map = label_map_util.load_labelmap(LABELS_LOC)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)

    imageFilePaths = []
    for imageFileName in os.listdir(TEST_IMAGE_DIR):
        if imageFileName.endswith(".jpg"):
            imageFilePaths.append(TEST_IMAGE_DIR + "/" + imageFileName)


    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            for image_path in imageFilePaths:

                print(image_path)

                Image_Coin_Counter = cv2.imread(image_path)

                if Image_Coin_Counter is None:
                    print("error reading file " + image_path)
                    continue
                # end if

                # Definite input and output Tensors for detection_graph
                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
                # Each box represents a part of the image where a particular object was detected.
                detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
                # Each score represent how level of confidence for each of the objects.
                # Score is shown on the result image, together with the class label.
                detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
                detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
                num_detections = detection_graph.get_tensor_by_name('num_detections:0')

                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
                Image_Coin_Counter_expanded = np.expand_dims(Image_Coin_Counter, axis=0)
                # Actual detection.
                (boxes, scores, classes, num) = sess.run(
                    [detection_boxes, detection_scores, detection_classes, num_detections],
                    feed_dict={image_tensor: Image_Coin_Counter_expanded})
                # Visualization of the results of a detection.

                vis_util.visualize_boxes_and_labels_on_image_array(Image_Coin_Counter,
                                                                   np.squeeze(boxes),
                                                                   np.squeeze(classes).astype(np.int32),
                                                                   np.squeeze(scores),
                                                                   category_index,
                                                                   use_normalized_coordinates=True,
                                                                   line_thickness=8)
                coin = [category_index.get(value) for index, value in enumerate(classes[0]) if scores[0, index] > 0.75]
                print(coin)
                ToonieCount = []
                LoonieCount = []
                
                for i in range(0, int(len(coin))):
                    if str(coin[i]['name']) == "Toonie":
                        ToonieCount.append("t")
                    else:
                        LoonieCount.append("l")
                
                # Singular & Plural
                toonie = " Toonies"
                loonie = " Loonies"
                if len(ToonieCount) <= 1:
                    toonie = " Toonie"
                if len(LoonieCount) <= 1:
                    loonie = " Loonie"
                
                # Add text to image
                text = "You have " + str(len(ToonieCount)) + toonie + " and " + str(len(LoonieCount)) + loonie \
                       +", " + str(len(ToonieCount)+len(ToonieCount)+len(LoonieCount)) + " Bucks in Total."
                
                cv2.putText(img=Image_Coin_Counter, text=text, org=(10, 50),
                            fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1.1, color=[0, 0, 0], lineType=4,
                            thickness=8)
                cv2.putText(img=Image_Coin_Counter, text=text, org=(10, 50),
                            fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1.1, color=[255, 255, 255], lineType=4,
                            thickness=2)
                cv2.imwrite(TEST_IMAGE_DIR + r'/' + str(random.random())[2:] + r'.jpg', Image_Coin_Counter)

if __name__ == "__main__":
    main()