track_cars.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license


import argparse
import os
import sys
from pathlib import Path

import torch
import torch.backends.cudnn as cudnn
import numpy as np

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from yolov5.models.experimental import attempt_load
from yolov5.utils.downloads import attempt_download
from yolov5.models.common import DetectMultiBackend
from yolov5.utils.datasets import LoadImages, LoadStreams, VID_FORMATS
from yolov5.utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from yolov5.utils.general import (LOGGER, check_img_size, non_max_suppression, scale_coords, check_requirements, cv2, 
                                  check_imshow, xyxy2xywh, increment_path, strip_optimizer, colorstr,
                                  print_args, scale_coords, resize_img, warp_points,)
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.plots import Annotator, colors, save_one_box
from deep_sort.utils.parser import get_config
from deep_sort.deep_sort import DeepSort


@torch.no_grad()
def run(
        image_template_path=ROOT / 'data/template/google_earth.jpg',
        warping_matrix_path=ROOT / 'data/template/matrix2.txt',
        yolo_model=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        output_path=ROOT / 'result',  # output directory
        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_vid=False,  # save results to video
        show_vid=False,  # show results to video
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        line_thickness=3,  # bounding box thickness (pixels)
        suffix='_tracked',  # suffix for the name of the processed video/image
        config_deepsort='deep_sort/configs/deep_sort.yaml',
        deep_sort_model='osnet_x0_25', # model name for OsNet

):
    # connect to the sourec
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download

    # create output folder if no existing
    output_path = str(output_path)
    if not os.path.exists(output_path):
        os.makedirs(output_path)
    deliminator = '.'

    # Directories
    if type(yolo_model) is str:  # single yolo model
        exp_name = yolo_model.split(".")[0]
    elif type(yolo_model) is list and len(yolo_model) == 1:  # single models after --yolo_model
        exp_name = yolo_model[0].split(".")[0]
    else:  # multiple models after --yolo_model
        exp_name = "ensemble"
    exp_name = exp_name + "_" + deep_sort_model.split('/')[-1].split('.')[0]

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(yolo_model, device=device, dnn=False, data=data, fp16=False)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size


    # Dataloader
    if webcam:
        show_vid = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
        nr_sources = len(dataset)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
        nr_sources = 1
    vid_path, vid_writer, txt_path = [None] * nr_sources, [None] * nr_sources, [None] * nr_sources

    # initialize deepsort
    cfg = get_config()
    cfg.merge_from_file(opt.config_deepsort)

    # Create as many trackers as there are video sources
    deepsort_list = []
    for i in range(nr_sources):
        deepsort_list.append(
            DeepSort(
                deep_sort_model,
                device,
                max_dist=cfg.DEEPSORT.MAX_DIST,
                max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
                max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
            )
        )
    outputs = [None] * nr_sources

    if  image_template_path:
        print('g')
        warping_matrix = np.loadtxt(warping_matrix_path) # The matrix for warping the view to the google earth view
        image_template = cv2.imread(image_template_path) # Template image for projecting the tracked cars
        image_template_h = np.shape(image_template)[0]
        image_template_w = np.shape(image_template)[1]

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    dt, seen = [0.0, 0.0, 0.0, 0.0], 0
    for path, im, im0s, vid_cap, s in dataset:
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
        im /= 255  # 0 - 255 to 0.0 - 1.0
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        pred = model(im, augment=False, visualize=False)
        t3 = time_sync()
        dt[1] += t3 - t2



        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, False, max_det=max_det)
        dt[2] += time_sync() - t3

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            seen += 1
            if webcam:  # nr_sources >= 1
                p, im0, _ = path[i], im0s[i].copy(), dataset.count
                p = Path(p)  # to Path
                s += f'{i}: '
                txt_file_name = p.name
                save_path = os.path.join(output_path, str(p.parent.name))  # im.jpg, vid.mp4, ...
            else:
                p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
                p = Path(p)  # to Path
                # video file
                if source.endswith(VID_FORMATS):
                    txt_file_name = p.stem
                    save_path = os.path.join(output_path, str(p.parent.name))  # im.jpg, vid.mp4, ...
                # folder with imgs
                else:
                    txt_file_name = p.parent.name  # get folder name containing current img
                    save_path = os.path.join(output_path, str(p.parent.name))  # im.jpg, vid.mp4, ...



            #save_path = str(save_dir / p.name)  # im.jpg
            txt_path = os.path.join(output_path,'tracks', txt_file_name)  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            imc = im0.copy() if save_crop else im0  # for save_crop

            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if image_template_path:
                annotator2 = Annotator(image_template.copy(), line_width=1, example=str(names), contour='circle')

            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(im.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' * (n > 1)}, "  # add to string

                xywhs = xyxy2xywh(det[:, 0:4])
                confs = det[:, 4]
                clss = det[:, 5]

                # pass detections to deepsort
                t4 = time_sync()
                outputs[i] = deepsort_list[i].update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0)
                t5 = time_sync()
                dt[3] += t5 - t4

                # draw boxes for visualization
                if len(outputs[i]) > 0:
                    for j, (output) in enumerate(outputs[i]):

                        bboxes = output[0:4]
                        id = output[4]
                        cls = output[5]
                        conf = output[6]

                        if save_txt:
                            # to MOT format
                            bbox_left = output[0]
                            bbox_top = output[1]
                            bbox_w = output[2] - output[0]
                            bbox_h = output[3] - output[1]
                            # Write MOT compliant results to file
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left,  # MOT format
                                                               bbox_top, bbox_w, bbox_h, -1, -1, -1, i))

                        #if save_vid or save_crop or show_vid:  # Add bbox to image
                        if 1:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = f'{id:0.0f} {names[c]} {conf:.2f}'
                            annotator.box_label(bboxes, label, color=colors(c, True))

                        if image_template_path:
                            # xyxy_np = np.array(
                            #     [bboxes[0].cpu(), bboxes[1].cpu(), bboxes[2].cpu(), bboxes[3].cpu()]).reshape((2, -1))
                            xyxy_np = np.array(
                                [bboxes[0], bboxes[1], bboxes[2], bboxes[3]]).reshape((2, -1))
                            xyxy2 = warp_points(xyxy_np, warping_matrix)
                            xyxy2 = xyxy2.reshape((1, -1)).squeeze()
                            label = f'{id:0.0f} {names[c]}'
                            annotator2.box_label(xyxy2, label, color=colors(c, True))

                            if save_crop:
                                txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else ''
                                save_one_box(bboxes, imc, file=output_path / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True)

                LOGGER.info(f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)')

            else:
                deepsort_list[i].increment_ages()
                LOGGER.info('No detections')

            # Stream results
            im0 = annotator.result()
            if image_template_path:
                im0 = resize_img(im0, (image_template_w, image_template_h))
                im0_2 = annotator2.result()
                im0 = cv2.hconcat([im0, im0_2])


            if view_img:
                cv2.imshow(str(p), im0)
                cv2.imshow(str(p), im0)
                # cv2.imshow('template', projected_image)
                #cv2.imshow('template', im0_2)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                save_path = os.path.join(output_path, ''.join([str(s) for s in Path(path).name.split(deliminator)[0:-1]]) +
                                         suffix + deliminator + Path(path).name.split(deliminator)[-1])
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            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))
                            w = np.shape(im0)[1]
                            h = np.shape(im0)[0]
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

        # Print time (inference-only)
        LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

    # Print results
    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms deep sort update \
        per image at shape {(1, 3, *imgsz)}' % t)

def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--yolo-model', nargs='+', type=str, default=ROOT / 'yolov5m.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'data/videos', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--output-path', type=str, default=ROOT / 'result', help='output directory')
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=200, help='maximum detections per image')
    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='show results', default=False)
    parser.add_argument('--save-txt', action='store_true', help='save MOT compliant results to *.txt')
    parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
    parser.add_argument('--show-vid', action='store_true', help='display tracking video results')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3', default=[0, 1, 2, 3, 4, 6, 7])
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--suffix', default='_tracked', type=str, help='suffix for the processed frames/videos')
    parser.add_argument('--deep-sort-model', type=str, default='osnet_x0_25')
    parser.add_argument('--config-deepsort', type=str, default='deep_sort/configs/deep_sort.yaml')

    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)