diff --git a/.gitignore b/.gitignore index 9fce72c..d511419 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +results/ weights/ data/ tmp/ diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..60ece6d --- /dev/null +++ b/demo.py @@ -0,0 +1,64 @@ +import os +import os.path as osp +import cv2 +import logging +import argparse +import motmetrics as mm + +from tracker.multitracker import JDETracker +from utils import visualization as vis +from utils.utils import * +from utils.log import logger +from utils.timer import Timer +from utils.evaluation import Evaluator +import utils.datasets as datasets +import torch +from track import eval_seq + + +def track(opt): + logger.setLevel(logging.INFO) + result_root = opt.output_root if opt.output_root!='' else '.' + mkdir_if_missing(result_root) + + # run tracking + timer = Timer() + accs = [] + n_frame = 0 + + logger.info('start tracking...') + dataloader = datasets.LoadVideo(opt.input_video, opt.img_size) + result_filename = os.path.join(result_root, 'results.txt') + frame_rate = dataloader.frame_rate + + frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame') + try: + eval_seq(opt, dataloader, 'mot', result_filename, + save_dir=frame_dir, show_image=False, frame_rate=frame_rate) + except Exception as e: + logger.info(e) + + if opt.output_format == 'video': + output_video_path = osp.join(result_root, 'result.mp4') + cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path) + os.system(cmd_str) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog='demo.py') + parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') + parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file') + parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension') + parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected') + parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold') + parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression') + parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes') + parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer') + parser.add_argument('--input-video', type=str, help='path to the input video') + parser.add_argument('--output-format', type=str, default='video', help='expected output format, can be video, tracklet or text') + parser.add_argument('--output-root', type=str, default='results', help='expected output root path') + opt = parser.parse_args() + print(opt, end='\n\n') + + track(opt) + diff --git a/track.py b/track.py index f705bfc..18f4d3e 100644 --- a/track.py +++ b/track.py @@ -12,11 +12,8 @@ from utils.timer import Timer from utils.evaluation import Evaluator import utils.datasets as datasets import torch +from utils.utils import * -def mkdirs(path): - if os.path.exists(path): - return - os.makedirs(path) def write_results(filename, results, data_type): if data_type == 'mot': @@ -41,9 +38,7 @@ def write_results(filename, results, data_type): def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30): - if save_dir is not None: - mkdirs(save_dir) - + mkdir_if_missing(save_dir) tracker = JDETracker(opt, frame_rate=frame_rate) timer = Timer() results = [] @@ -85,7 +80,7 @@ def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), save_images=False, save_videos=False, show_image=True): logger.setLevel(logging.INFO) result_root = os.path.join(data_root, '..', 'results', exp_name) - mkdirs(result_root) + mkdir_if_missing(result_root) data_type = 'mot' # run tracking @@ -131,8 +126,7 @@ def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), if __name__ == '__main__': - parser = argparse.ArgumentParser(prog='test.py') - parser.add_argument('--batch-size', type=int, default=8, help='size of each image batch') + parser = argparse.ArgumentParser(prog='track.py') parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file') parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension') @@ -140,7 +134,6 @@ if __name__ == '__main__': parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold') parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression') parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes') - parser.add_argument('--pixel-mean', type=float, default=[0,0,0], nargs='+', help='pixel mean') parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer') parser.add_argument('--test-mot16', action='store_true', help='tracking buffer') parser.add_argument('--save-images', action='store_true', help='save tracking results (image)') diff --git a/utils/datasets.py b/utils/datasets.py index d4752a2..14b4ff2 100644 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -75,6 +75,54 @@ class LoadImages: # for inference return self.nF # number of files +class LoadVideo: # for inference + def __init__(self, path, img_size=(1088, 608)): + self.cap = cv2.VideoCapture(path) + self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS))) + self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.vh = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + self.width = img_size[0] + self.height = img_size[1] + self.count = 0 + + self.w, self.h = self.get_size(self.vw, self.vh, self.width, self.height) + print('Lenth of the video: {:d} frames'.format(self.vn)) + + def get_size(self, vw, vh, dw, dh): + wa, ha = float(dw) / vw, float(dh) / vh + a = min(wa, ha) + return int(vw *a), int(vh*a) + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if self.count == len(self): + raise StopIteration + # Read image + res, img0 = self.cap.read() # BGR + assert img0 is not None, 'Failed to load frame {:d}'.format(self.count) + img0 = cv2.resize(img0, (self.w, self.h)) + + # Padded resize + img, _, _, _ = letterbox(img0, height=self.height, width=self.width) + + # Normalize RGB + img = img[:, :, ::-1].transpose(2, 0, 1) + img = np.ascontiguousarray(img, dtype=np.float32) + img /= 255.0 + + # cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image + return self.count, img, img0 + + def __len__(self): + return self.vn # number of files + + class LoadImagesAndLabels: # for training def __init__(self, path, img_size=(1088,608), augment=False, transforms=None): with open(path, 'r') as file: diff --git a/utils/utils.py b/utils/utils.py index 195a5a7..bc22ff8 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -52,27 +52,6 @@ def model_info(model): # Plots a line-by-line description of a PyTorch model print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g)) -def coco_class_weights(): # frequency of each class in coco train2014 - weights = 1 / torch.FloatTensor( - [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671, - 6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689, - 4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004, - 5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933, - 1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]) - weights /= weights.sum() - return weights - - -def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) - # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ - # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') - # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') - # x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco - x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] - return x - def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img tl = line_thickness or round(0.0004 * max(img.shape[0:2])) + 1 # line thickness @@ -505,28 +484,6 @@ def strip_optimizer_from_checkpoint(filename='weights/best.pt'): torch.save(a, filename.replace('.pt', '_lite.pt')) -def coco_class_count(path='../coco/labels/train2014/'): - # histogram of occurrences per class - - nC = 80 # number classes - x = np.zeros(nC, dtype='int32') - files = sorted(glob.glob('%s/*.*' % path)) - for i, file in enumerate(files): - labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5) - x += np.bincount(labels[:, 0].astype('int32'), minlength=nC) - print(i, len(files)) - - -def coco_only_people(path='../coco/labels/val2014/'): - # find images with only people - - files = sorted(glob.glob('%s/*.*' % path)) - for i, file in enumerate(files): - labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5) - if all(labels[:, 0] == 0): - print(labels.shape[0], file) - - def plot_results(): # Plot YOLO training results file 'results.txt' # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v1.txt')