diff --git a/README.md b/README.md
index 6e70352b..79f0b988 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,24 @@
+## Updates of this repo
+**9/1/2022**
+- Open --downscale parameter to speed up track speed.
+- Add --legacy parameter to support un-normalization input.
+- Add --save_size parameter to adjust the size of saved video/image.
+- Add time counter for det, track, save separately.
+
+## Experiments of this repo
+### GMC downscale
+downscale in GMC defaults to 2. It cost unbearable 3.6s. Time increase 10 times when downscale increase in 2 times.
+
+| Tracker | input_size | downscale | time |
+|:--------------|:-------------:|:------:|:------:|
+| BoT-SORT | (768, 1280) | 2 | 3.6 |
+| BoT-SORT | (768, 1280) | 4 | 0.26 |
+| BoT-SORT | (768, 1280) | 8 | 0.02 |
+
+
## Highlights 🚀
- YOLOX & YOLOv7 support
@@ -216,16 +234,16 @@ Demo with BoT-SORT(-ReID) based YOLOX and multi-class.
cd
# Original example
-python3 tools/demo.py video --path -f yolox/exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar --with-reid --fuse-score --fp16 --fuse --save_result
+python3 tools/demo.py video --path -f yolox/exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar --with-reid --fuse-score --fp16 --fuse --save_result --legacy
# Multi-class example
-python3 tools/mc_demo.py video --path -f yolox/exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar --with-reid --fuse-score --fp16 --fuse --save_result
+python3 tools/mc_demo.py video --path -f yolox/exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar --with-reid --fuse-score --fp16 --fuse --save_result --legacy
```
Demo with BoT-SORT(-ReID) based YOLOv7 and multi-class.
```shell
cd
-python3 tools/mc_demo_yolov7.py --weights pretrained/yolov7-d6.pt --source --fuse-score --agnostic-nms (--with-reid)
+python3 tools/mc_demo_yolov7.py --weights pretrained/yolov7-d6.pt --source --fuse-score --agnostic-nms (--with-reid) --legacy
```
## Note
diff --git a/tools/demo.py b/tools/demo.py
index b2c0bd2e..9780a806 100644
--- a/tools/demo.py
+++ b/tools/demo.py
@@ -29,6 +29,7 @@ def make_parser():
parser.add_argument("--path", default="", help="path to images or video")
parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id")
parser.add_argument("--save_result", action="store_true",help="whether to save the inference result of image/video")
+ parser.add_argument("--save_size", default=None, type=str, help="save size of image/video, used to adjust output size")
parser.add_argument("-f", "--exp_file", default=None, type=str, help="pls input your expriment description file")
parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt for eval")
parser.add_argument("--device", default="gpu", type=str, help="device to run our model, can either be cpu or gpu")
@@ -39,6 +40,7 @@ def make_parser():
parser.add_argument("--fp16", dest="fp16", default=False, action="store_true",help="Adopting mix precision evaluating.")
parser.add_argument("--fuse", dest="fuse", default=False, action="store_true", help="Fuse conv and bn for testing.")
parser.add_argument("--trt", dest="trt", default=False, action="store_true", help="Using TensorRT model for testing.")
+ parser.add_argument("--legacy", dest="legacy", default=False, action="store_true", help="legacy code, such as mean/std normalization.")
# tracking args
parser.add_argument("--track_high_thresh", type=float, default=0.6, help="tracking confidence threshold")
@@ -52,6 +54,7 @@ def make_parser():
# CMC
parser.add_argument("--cmc-method", default="orb", type=str, help="cmc method: files (Vidstab GMC) | orb | ecc")
+ parser.add_argument("--downscale", default=2, type=int, help="cmc downscale, large image leads to very slow gmc, increase downscale to increase speed")
# ReID
parser.add_argument("--with-reid", dest="with_reid", default=False, action="store_true", help="test mot20.")
@@ -94,7 +97,8 @@ def __init__(
trt_file=None,
decoder=None,
device=torch.device("cpu"),
- fp16=False
+ fp16=False,
+ legacy=False
):
self.model = model
self.decoder = decoder
@@ -115,6 +119,7 @@ def __init__(
self.model = model_trt
self.rgb_means = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
+ self.legacy = legacy
def inference(self, img, timer):
img_info = {"id": 0}
@@ -129,7 +134,7 @@ def inference(self, img, timer):
img_info["width"] = width
img_info["raw_img"] = img
- img, ratio = preproc(img, self.test_size, self.rgb_means, self.std)
+ img, ratio = preproc(img, self.test_size, self.rgb_means, self.std, legacy=self.legacy)
img_info["ratio"] = ratio
img = torch.from_numpy(img).unsqueeze(0).float().to(self.device)
if self.fp16:
@@ -156,11 +161,17 @@ def image_demo(predictor, vis_folder, current_time, args):
timer = Timer()
results = []
+ st_det = 0
+ st_track = 0
+ st_save = 0
for frame_id, img_path in enumerate(files, 1):
+ t0 = time.time()
# Detect objects
outputs, img_info = predictor.inference(img_path, timer)
scale = min(exp.test_size[0] / float(img_info['height'], ), exp.test_size[1] / float(img_info['width']))
+ t_det = time.time()
+ st_det += t_det - t0
detections = []
if outputs[0] is not None:
@@ -194,6 +205,9 @@ def image_demo(predictor, vis_folder, current_time, args):
timer.toc()
online_im = img_info['raw_img']
+ t_track = time.time()
+ st_track += t_track - t_det
+
# result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
if args.save_result:
timestamp = time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
@@ -201,12 +215,19 @@ def image_demo(predictor, vis_folder, current_time, args):
os.makedirs(save_folder, exist_ok=True)
cv2.imwrite(osp.join(save_folder, osp.basename(img_path)), online_im)
+ t_save = time.time()
+ st_save += t_save - t_track
+
if frame_id % 20 == 0:
- logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
+ logger.info('Processing frame {} ({:.2f} fps, det: {:.2f} fps, track: {:.2f} fps, save: {:.2f} fps)'
+ .format(frame_id, 1. / max(1e-5, timer.average_time), 20./st_det, 20./st_track, 20./st_save))
+ st_det = 0
+ st_track = 0
+ st_save = 0
- ch = cv2.waitKey(0)
- if ch == 27 or ch == ord("q") or ch == ord("Q"):
- break
+ # ch = cv2.waitKey(0)
+ # if ch == 27 or ch == ord("q") or ch == ord("Q"):
+ # break
if args.save_result:
res_file = osp.join(vis_folder, f"{timestamp}.txt")
@@ -228,21 +249,38 @@ def imageflow_demo(predictor, vis_folder, current_time, args):
else:
save_path = osp.join(save_folder, "camera.mp4")
logger.info(f"video save_path is {save_path}")
- vid_writer = cv2.VideoWriter(
- save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
- )
+ if args.save_size is not None:
+ vid_writer = cv2.VideoWriter(
+ save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (args.save_size[1], args.save_size[0])
+ )
+ else:
+ vid_writer = cv2.VideoWriter(
+ save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
+ )
tracker = BoTSORT(args, frame_rate=args.fps)
timer = Timer()
frame_id = 0
results = []
+
+ st_det = 1e-5
+ st_track = 1e-5
+ st_save = 1e-5
while True:
if frame_id % 20 == 0:
- logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
+ logger.info('Processing frame {} ({:.2f} fps, det: {:.2f} fps, track: {:.2f} fps, save: {:.2f} fps)'
+ .format(frame_id, 1. / max(1e-5, timer.average_time), 20. / st_det, 20. / st_track,
+ 20. / st_save))
+ st_det = 0
+ st_track = 0
+ st_save = 0
ret_val, frame = cap.read()
if ret_val:
# Detect objects
+ t0 = time.time()
outputs, img_info = predictor.inference(frame, timer)
scale = min(exp.test_size[0] / float(img_info['height'], ), exp.test_size[1] / float(img_info['width']))
+ t_det = time.time()
+ st_det += t_det - t0
if outputs[0] is not None:
outputs = outputs[0].cpu().numpy()
@@ -273,11 +311,22 @@ def imageflow_demo(predictor, vis_folder, current_time, args):
else:
timer.toc()
online_im = img_info['raw_img']
+ t_track = time.time()
+ st_track += t_track - t_det
+
if args.save_result:
+ if args.save_size is not None:
+ online_im = cv2.resize(
+ online_im,
+ (args.save_size[1], args.save_size[0]),
+ interpolation=cv2.INTER_LINEAR,
+ )
vid_writer.write(online_im)
- ch = cv2.waitKey(1)
- if ch == 27 or ch == ord("q") or ch == ord("Q"):
- break
+ t_save = time.time()
+ st_save += t_save - t_track
+ # ch = cv2.waitKey(1)
+ # if ch == 27 or ch == ord("q") or ch == ord("Q"):
+ # break
else:
break
frame_id += 1
@@ -348,7 +397,10 @@ def main(exp, args):
trt_file = None
decoder = None
- predictor = Predictor(model, exp, trt_file, decoder, args.device, args.fp16)
+ if args.save_size is not None:
+ args.save_size = tuple(map(int, args.save_size.split(',')))
+
+ predictor = Predictor(model, exp, trt_file, decoder, args.device, args.fp16, args.legacy)
current_time = time.localtime()
if args.demo == "image" or args.demo == "images":
image_demo(predictor, vis_folder, current_time, args)
diff --git a/tracker/bot_sort.py b/tracker/bot_sort.py
index 6b1bb482..48a873d7 100644
--- a/tracker/bot_sort.py
+++ b/tracker/bot_sort.py
@@ -10,6 +10,8 @@
from fast_reid.fast_reid_interfece import FastReIDInterface
+import time
+
class STrack(BaseTrack):
shared_kalman = KalmanFilter()
@@ -225,7 +227,7 @@ def __init__(self, args, frame_rate=30):
if args.with_reid:
self.encoder = FastReIDInterface(args.fast_reid_config, args.fast_reid_weights, args.device)
- self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
+ self.gmc = GMC(method=args.cmc_method, downscale=args.downscale, verbose=[args.name, args.ablation])
def update(self, output_results, img):
self.frame_id += 1
@@ -294,10 +296,13 @@ def update(self, output_results, img):
# Predict the current location with KF
STrack.multi_predict(strack_pool)
+ t0 = time.time()
# Fix camera motion
warp = self.gmc.apply(img, dets)
STrack.multi_gmc(strack_pool, warp)
STrack.multi_gmc(unconfirmed, warp)
+ t1 = time.time()
+ # print("time: %f" % (t1 - t0))
# Associate with high score detection boxes
ious_dists = matching.iou_distance(strack_pool, detections)
diff --git a/yolox/data/data_augment.py b/yolox/data/data_augment.py
index 99fb30a2..09a8bd6d 100644
--- a/yolox/data/data_augment.py
+++ b/yolox/data/data_augment.py
@@ -186,7 +186,7 @@ def _mirror(image, boxes):
return image, boxes
-def preproc(image, input_size, mean, std, swap=(2, 0, 1)):
+def preproc(image, input_size, mean, std, swap=(2, 0, 1), legacy=False):
if len(image.shape) == 3:
padded_img = np.ones((input_size[0], input_size[1], 3)) * 114.0
else:
@@ -200,12 +200,13 @@ def preproc(image, input_size, mean, std, swap=(2, 0, 1)):
).astype(np.float32)
padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
- padded_img = padded_img[:, :, ::-1]
- padded_img /= 255.0
- if mean is not None:
- padded_img -= mean
- if std is not None:
- padded_img /= std
+ if legacy:
+ padded_img = padded_img[:, :, ::-1]
+ padded_img /= 255.0
+ if mean is not None:
+ padded_img -= mean
+ if std is not None:
+ padded_img /= std
padded_img = padded_img.transpose(swap)
padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
return padded_img, r