detect.py

"""
Measure the distance between two objects
https://github.com/IntelRealSense/librealsense/issues/6544
https://github.com/soarwing52/RealsensePython
"""

import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import json

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages, LoadRealSense2
from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \
    xyxy2xywh, strip_optimizer, set_logging, increment_path, calc_depth, calc_distancing
from utils.plots import plot_one_box, plot_one_circle
from utils.torch_utils import select_device, load_classifier, time_synchronized
# from Convert2Tensorrt import JetsonInferenceEngine, fromOnnx2TensorRtEngine


def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz, distancing, jetson = opt.source, opt.weights, opt.view_img, \
                                                                     opt.save_txt, opt.img_size, \
                                                                     opt.distancing, opt.jetson
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://')) or source.lower().startswith('intel')

    # Directories
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    if jetson:
        """
        1. convert to .onnx <python models/export.py --weights runs/train/exp2/weights/best.pt --img 640 --batch 1>
        2. save .jet engine into the same dir as 'weights'
        """
        fromOnnx2TensorRtEngine(weights[0].split('.pt')[0])
        model = JetsonInferenceEngine(weights[0].split('.pt')[0] + '.jet')
    else:
        model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        if source.lower().startswith('intel'):
            dataset = LoadRealSense2()
            save_img = True
        else:
            dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    # names = model.module.names if hasattr(model, 'module') else model.names
    names = ['No mask', 'Mask', 'Incorrectly worn']
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once

    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                if source.lower().startswith('intel'):
                    p, s, im0, frame = path, '%g: ' % i, im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            json_path = str(save_dir / p.stem) + '.json'
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                report = {frame: {'Total': len(det),
                                  names[0]: 0,
                                  names[1]: 0,
                                  names[2]: 0}}

                if type(vid_cap) is dict:  # dis dictionary
                    depth, depth_scale, depth_intrin, aligned_depth_frame = vid_cap['distance'], \
                                                                            vid_cap['depth_scale'], \
                                                                            vid_cap['depth_intrin'], \
                                                                            vid_cap['aligned_depth_frame']

                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f'{n} {names[int(c)]}s, '  # add to string

                # Write results
                distancing_list = []
                for *xyxy, conf, cls in reversed(det):
                    report[frame][names[int(cls)]] += 1

                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or view_img:  # Add bbox to image
                        label = f'{names[int(cls)]} {conf:.2f}'

                        # calculate distance
                        if type(vid_cap) is dict:  # dis dictionary
                            xmin = int(xyxy[0])
                            ymin = int(xyxy[1])
                            xmax = int(xyxy[2])
                            ymax = int(xyxy[3])

                            # Calculating depth using CV2.Mean
                            distance_in_meters = calc_depth(xmin, ymin, xmax, ymax, depth, depth_scale)# z axis

                            if distancing:
                                distancing_list.append((xmin, xmax, ymin, ymax, distance_in_meters))

                            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3,
                                         dist=distance_in_meters)
                        else:
                            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

                with open(json_path, 'a') as outfile:
                    json.dump(report, outfile)
                    outfile.write('\n')
                cv2.putText(im0, 'Frame: ' + str(frame), (30, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)

                dp_string = ''
                for k, v in report[frame].items():
                    dp_string += '{}: {}.  '.format(k, v)
                cv2.putText(im0, dp_string, (30, 80), cv2.FONT_HERSHEY_SIMPLEX, .7, (255, 0, 0), 2)
                # compute distancing between objects
                if distancing and len(distancing_list) > 1 and type(vid_cap) is dict:
                    too_close = calc_distancing(distancing_list, depth_intrin, aligned_depth_frame)
                    if len(too_close) > 0:
                        print("Too close!!!!\n\n", too_close)

                        for close_obj in too_close.keys():
                            (obj1, obj2) = close_obj
                            print(obj1, obj2)
                            print(list(reversed(det))[obj1])
                            print(list(reversed(det))[obj2])
                            *xyxy1, _, _ = list(reversed(det))[obj1]
                            print(xyxy1)
                            xyxy2 = list(reversed(det))[obj2][0]
                            plot_one_circle(xyxy1, im0, too_close[close_obj], line_thickness=4)
                    """
                    https://github.com/IntelRealSense/librealsense/issues/2481
                    https://github.com/IntelRealSense/librealsense/tree/master/examples/measure
                    """


            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = 'mp4v'  # output video codec
                        if type(vid_cap) is dict:  # estimate distance_in_meters
                            # TODO hard code
                            w, h, fps = 640, 480, 6
                        else:
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='weights/best.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='data/street.mp4', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--distancing', type=bool, default=False, help='detect social distancing')
    parser.add_argument('--jetson', type=bool, default=False, help='use Jetson nano')
    opt = parser.parse_args()
    print(opt)
    # check_requirements()

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()