def main():
parser = ArgumentParser()
parser.add_argument('config', help='Config file')
parser.add_argument('--input', help='input video file')
parser.add_argument('--output', help='output video file (mp4 format)')
parser.add_argument('--checkpoint', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--show',
action='store_true',
default=False,
help='whether to show visualizations.')
parser.add_argument(
'--score-thr', type=float, default=0.8, help='bbox score threshold')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
cap = cv2.VideoCapture(args.input)
if args.output is not None:
save_out_video = True
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
videoWriter = cv2.VideoWriter(args.output, fourcc, fps, size)
frame_id = 0
while (cap.isOpened()):
flag, frame = cap.read()
if not flag:
break
# test a single image
result = inference_vid(model, frame, frame_id)
vis_frame = model.show_result(
frame, result, score_thr=args.score_thr, show=False)
if save_out_video:
videoWriter.write(vis_frame)
if args.show:
cv2.imshow(args.input, vis_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
frame_id += 1
cap.release()
if save_out_video:
videoWriter.release()
cv2.destroyAllWindows()
def main(args):
url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
with open(args.video, 'rb') as video:
tmp_res = requests.post(url, video)
content = tmp_res.content
if args.result_video:
with open(args.result_video, 'wb') as file:
file.write(content)
video_path = args.result_video
else:
video_path = tempfile.NamedTemporaryFile().name
with open(video_path, 'wb') as file:
file.write(content)
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
while cap.isOpened():
flag, frame = cap.read()
if not flag:
break
cv2.imshow('torchserve_result', frame)
if cv2.waitKey(int(1000 / fps)) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
model = init_model(args.config, args.checkpoint, device=args.device)
cap = cv2.VideoCapture(args.video)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_id = 0
while cap.isOpened():
flag, frame = cap.read()
if not flag:
break
# test a single image
result = inference_vid(model, frame, frame_id)
vis_frame = model.show_result(frame,
result,
score_thr=args.score_thr,
show=False)
cv2.imshow('pytorch_result', vis_frame)
if cv2.waitKey(int(1000 / fps)) & 0xFF == ord('q'):
break
frame_id += 1
cap.release()
cv2.destroyAllWindows()
def initialize(self, context):
properties = context.system_properties
self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device = torch.device(self.map_location + ':' +
str(properties.get('gpu_id')) if torch.cuda.
is_available() else self.map_location)
self.manifest = context.manifest
model_dir = properties.get('model_dir')
serialized_file = self.manifest['model']['serializedFile']
checkpoint = os.path.join(model_dir, serialized_file)
self.config_file = os.path.join(model_dir, 'config.py')
self.model = init_model(self.config_file, checkpoint, self.device)
self.score_thr = 0.5
self.initialized = True
def main():
parser = ArgumentParser()
parser.add_argument('config', help='Config file')
parser.add_argument('--input', help='input video file')
parser.add_argument('--output', help='output video file (mp4 format)')
parser.add_argument('--checkpoint', help='Checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument('--show',
action='store_true',
default=False,
help='whether to show visualizations.')
parser.add_argument('--color',
default=(0, 255, 0),
help='Color of tracked bbox lines.')
parser.add_argument('--thickness',
default=3,
type=int,
help='Thickness of bbox lines.')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
cap = cv2.VideoCapture(args.input)
save_out_video = False
if args.output is not None:
save_out_video = True
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
videoWriter = cv2.VideoWriter(args.output, fourcc, fps, size)
frame_id = 0
while (cap.isOpened()):
flag, frame = cap.read()
if not flag:
break
if frame_id == 0:
init_bbox = list(cv2.selectROI(args.input, frame, False, False))
# convert (x1, y1, w, h) to (x1, y1, x2, y2)
init_bbox[2] += init_bbox[0]
init_bbox[3] += init_bbox[1]
# test a single image
result = inference_sot(model, frame, init_bbox, frame_id)
vis_frame = model.show_result(frame,
result,
color=args.color,
thickness=args.thickness,
show=False)
if save_out_video:
videoWriter.write(vis_frame)
if args.show:
cv2.imshow(args.input, vis_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
frame_id += 1
cap.release()
if save_out_video:
videoWriter.release()
cv2.destroyAllWindows()
def main():
parser = ArgumentParser()
parser.add_argument('config', help='config file')
parser.add_argument('--input', help='input video file or folder')
parser.add_argument('--output',
help='output video file (mp4 format) or folder')
parser.add_argument('--checkpoint', help='checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='device used for inference')
parser.add_argument('--show',
action='store_true',
help='whether show the results on the fly')
parser.add_argument('--backend',
choices=['cv2', 'plt'],
default='cv2',
help='the backend to visualize the results')
parser.add_argument('--fps', help='FPS of the output video')
args = parser.parse_args()
assert args.output or args.show
# load images
if osp.isdir(args.input):
imgs = sorted(os.listdir(args.input))
IN_VIDEO = False
else:
imgs = mmcv.VideoReader(args.input)
IN_VIDEO = True
# define output
if args.output is not None:
if args.output.endswith('.mp4'):
OUT_VIDEO = True
out_dir = tempfile.TemporaryDirectory()
out_path = out_dir.name
_out = args.output.rsplit('/', 1)
if len(_out) > 1:
os.makedirs(_out[0], exist_ok=True)
else:
OUT_VIDEO = False
out_path = args.output
os.makedirs(out_path, exist_ok=True)
fps = args.fps
if args.show or OUT_VIDEO:
if fps is None and IN_VIDEO:
fps = imgs.fps
if not fps:
raise ValueError('Please set the FPS for the output video.')
fps = int(fps)
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
# test and show/save the images
raw_video_path = "/data/taofuyu/tao_dataset/car_reid/high_video/f73dc1dc-a5b3040f/"
all_video = []
for dir, _, files in os.walk(raw_video_path):
for ff in files:
if ff.split(".")[-1] == "mp4":
all_video.append(os.path.join(dir, ff))
for video in all_video:
appeared_id = []
imgs = mmcv.VideoReader(video)
print(video)
print(
os.path.join("/data/taofuyu/tao_dataset/car_reid/high/",
video.split("/")[-1].split(".")[0]))
prog_bar = mmcv.ProgressBar(len(imgs))
for i, img in enumerate(imgs):
if isinstance(img, str):
img = osp.join(video, img)
result = inference_mot(model, img, frame_id=i)
result = result['track_results']
car_result = result[6] #6: roof
for car in car_result:
video_path = video
sample_save_path = os.path.join(
"/data/taofuyu/tao_dataset/car_reid/roof/",
video_path.split("/")[-1].split(".")[0])
if not os.path.exists(sample_save_path):
os.makedirs(sample_save_path)
car_id = str(int(car[0]))
car_box = car[1:-1]
x_min, y_min, x_max, y_max = map(int, list(car_box))
car_patch = img[y_min:y_max, x_min:x_max]
h, w, _ = car_patch.shape
if h <= 0 or w <= 0:
continue
if not car_id in appeared_id:
cv2.imwrite(
os.path.join(sample_save_path,
"id_" + car_id + ".jpg"), car_patch)
appeared_id.append(car_id)
track_save_path = os.path.join(sample_save_path,
"id_" + car_id)
if not os.path.exists(track_save_path):
os.makedirs(track_save_path)
cv2.imwrite(
track_save_path + "/" + str(i) + "_" + car_id + ".jpg",
car_patch)
# if args.output is not None:
# if IN_VIDEO or OUT_VIDEO:
# out_file = osp.join(out_path, f'{i:06d}.jpg')
# else:
# out_file = osp.join(out_path, img.rsplit('/', 1)[-1])
# else:
# out_file = None
# model.show_result(
# img,
# result,
# show=args.show,
# wait_time=int(1000. / fps) if fps else 0,
# out_file=out_file,
# backend=args.backend)
prog_bar.update()
def main():
parser = ArgumentParser()
parser.add_argument('config', help='Config file')
parser.add_argument('--input', help='input video file')
parser.add_argument('--output', help='output video file (mp4 format)')
parser.add_argument('--checkpoint', help='Checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument('--show',
action='store_true',
default=False,
help='whether to show visualizations.')
parser.add_argument('--color',
default=(0, 255, 0),
help='Color of tracked bbox lines.')
parser.add_argument('--thickness',
default=3,
type=int,
help='Thickness of bbox lines.')
parser.add_argument('--fps', help='FPS of the output video')
parser.add_argument('--gt_bbox_file', help='The path of gt_bbox file')
args = parser.parse_args()
# load images
if osp.isdir(args.input):
imgs = sorted(filter(lambda x: x.endswith(('.jpg', '.png', '.jpeg')),
os.listdir(args.input)),
key=lambda x: int(x.split('.')[0]))
IN_VIDEO = False
else:
imgs = mmcv.VideoReader(args.input)
IN_VIDEO = True
# define output
if args.output is not None:
if args.output.endswith('.mp4'):
OUT_VIDEO = True
out_dir = tempfile.TemporaryDirectory()
out_path = out_dir.name
_out = args.output.rsplit(os.sep, 1)
if len(_out) > 1:
os.makedirs(_out[0], exist_ok=True)
else:
OUT_VIDEO = False
out_path = args.output
os.makedirs(out_path, exist_ok=True)
fps = args.fps
if args.show or OUT_VIDEO:
if fps is None and IN_VIDEO:
fps = imgs.fps
if not fps:
raise ValueError('Please set the FPS for the output video.')
fps = int(fps)
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
prog_bar = mmcv.ProgressBar(len(imgs))
# test and show/save the images
for i, img in enumerate(imgs):
if isinstance(img, str):
img = osp.join(args.input, img)
img = mmcv.imread(img)
if i == 0:
if args.gt_bbox_file is not None:
bboxes = mmcv.list_from_file(args.gt_bbox_file)
init_bbox = list(map(float, bboxes[0].split(',')))
else:
init_bbox = list(cv2.selectROI(args.input, img, False, False))
# convert (x1, y1, w, h) to (x1, y1, x2, y2)
init_bbox[2] += init_bbox[0]
init_bbox[3] += init_bbox[1]
result = inference_sot(model, img, init_bbox, frame_id=i)
if args.output is not None:
if IN_VIDEO or OUT_VIDEO:
out_file = osp.join(out_path, f'{i:06d}.jpg')
else:
out_file = osp.join(out_path, img.rsplit('os.sep', 1)[-1])
else:
out_file = None
model.show_result(img,
result,
show=args.show,
wait_time=int(1000. / fps) if fps else 0,
out_file=out_file,
thickness=args.thickness)
prog_bar.update()
if args.output and OUT_VIDEO:
print(
f'\nmaking the output video at {args.output} with a FPS of {fps}')
mmcv.frames2video(out_path, args.output, fps=fps, fourcc='mp4v')
out_dir.cleanup()
def main():
parser = ArgumentParser()
parser.add_argument('config', help='config file')
parser.add_argument('--input', help='input video file or folder')
parser.add_argument('--output',
help='output video file (mp4 format) or folder')
parser.add_argument('--checkpoint', help='checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='device used for inference')
parser.add_argument('--show',
action='store_true',
help='whether show the results on the fly')
parser.add_argument('--backend',
choices=['cv2', 'plt'],
default='cv2',
help='the backend to visualize the results')
parser.add_argument('--fps', help='FPS of the output video')
args = parser.parse_args()
assert args.output or args.show
# load images
if osp.isdir(args.input):
imgs = sorted(os.listdir(args.input))
IN_VIDEO = False
else:
imgs = mmcv.VideoReader(args.input)
IN_VIDEO = True
# define output
if args.output is not None:
if args.output.endswith('.mp4'):
OUT_VIDEO = True
out_dir = tempfile.TemporaryDirectory()
out_path = out_dir.name
_out = args.output.rsplit('/', 1)
if len(_out) > 1:
os.makedirs(_out[0], exist_ok=True)
else:
OUT_VIDEO = False
out_path = args.output
os.makedirs(out_path, exist_ok=True)
fps = args.fps
if args.show or OUT_VIDEO:
if fps is None and IN_VIDEO:
fps = imgs.fps
if not fps:
raise ValueError('Please set the FPS for the output video.')
fps = int(fps)
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
prog_bar = mmcv.ProgressBar(len(imgs))
# test and show/save the images
for i, img in enumerate(imgs):
if isinstance(img, str):
img = osp.join(args.input, img)
result = inference_mot(model, img, frame_id=i)
if args.output is not None:
if IN_VIDEO or OUT_VIDEO:
out_file = osp.join(out_path, f'{i:06d}.jpg')
else:
out_file = osp.join(out_path, img.rsplit('/', 1)[-1])
else:
out_file = None
model.show_result(img,
result,
show=args.show,
wait_time=int(1000. / fps) if fps else 0,
out_file=out_file,
backend=args.backend)
prog_bar.update()
if OUT_VIDEO:
print(f'making the output video at {args.output} with a FPS of {fps}')
mmcv.frames2video(out_path, args.output, fps=fps)
out_dir.cleanup()
def main():
parser = ArgumentParser()
parser.add_argument('config', help='Config file')
parser.add_argument('--input', help='input video file')
parser.add_argument('--output', help='output video file (mp4 format)')
parser.add_argument('--checkpoint', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--show',
action='store_true',
default=False,
help='whether to show visualizations.')
parser.add_argument(
'--score-thr', type=float, default=0.8, help='bbox score threshold')
parser.add_argument(
'--thickness', default=3, type=int, help='Thickness of bbox lines.')
parser.add_argument('--fps', help='FPS of the output video')
args = parser.parse_args()
# load images
if osp.isdir(args.input):
imgs = sorted(os.listdir(args.input))
IN_VIDEO = False
else:
imgs = mmcv.VideoReader(args.input)
IN_VIDEO = True
# define output
if args.output is not None:
if args.output.endswith('.mp4'):
OUT_VIDEO = True
out_dir = tempfile.TemporaryDirectory()
out_path = out_dir.name
_out = args.output.rsplit('/', 1)
if len(_out) > 1:
os.makedirs(_out[0], exist_ok=True)
else:
OUT_VIDEO = False
out_path = args.output
os.makedirs(out_path, exist_ok=True)
fps = args.fps
if args.show or OUT_VIDEO:
if fps is None and IN_VIDEO:
fps = imgs.fps
if not fps:
raise ValueError('Please set the FPS for the output video.')
fps = int(fps)
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
prog_bar = mmcv.ProgressBar(len(imgs))
# test and show/save the images
for i, img in enumerate(imgs):
if isinstance(img, str):
img = osp.join(args.input, img)
img = mmcv.imread(img)
result = inference_vid(model, img, frame_id=i)
if args.output is not None:
if IN_VIDEO or OUT_VIDEO:
out_file = osp.join(out_path, f'{i:06d}.jpg')
else:
out_file = osp.join(out_path, img.rsplit('/', 1)[-1])
else:
out_file = None
model.show_result(
img,
result,
score_thr=args.score_thr,
show=args.show,
wait_time=int(1000. / fps) if fps else 0,
out_file=out_file,
thickness=args.thickness)
prog_bar.update()
if OUT_VIDEO:
print(
f'\nmaking the output video at {args.output} with a FPS of {fps}')
mmcv.frames2video(out_path, args.output, fps=fps, fourcc='mp4v')
out_dir.cleanup()
