def detect(opt, logger, cfg):
demo = FeatureExtractionDemo(cfg, parallel=opt.parallel)
query_loader = []
print(len(os.listdir(opt.input_picture_path)))
for query_image in os.listdir(opt.input_picture_path):
query_image = cv2.imread(
os.path.join(opt.input_picture_path, query_image))
query_image = cv2.cvtColor(query_image,
cv2.COLOR_BGR2RGB) # PIL: (233, 602)
query_image = cv2.resize(query_image, (128, 256))
query_image = np.transpose(query_image, (2, 0, 1))
query_feats = demo.run_on_image(
torch.from_numpy(query_image).unsqueeze(0))
print(query_feats.shape)
query_loader.append(query_feats)
extract_features = torch.cat(query_loader, dim=0).data.cpu().numpy()
# extract_features = torch.nn.functional.normalize(query_feats, dim=1, p=2).data.cpu().numpy()
print('features:', extract_features.shape)
with Timer('All Steps'):
global args
# args = parser.parse_args()
label_path = None
pred_label_path = None
print('=> Use cuda ?: {}'.format(opt.is_cuda))
# with Timer('Extract Feature'):
# extract_features = extract_fature(args)
if eval(opt.is_evaluate):
opt.label_path = 'data/tmp/test.meta'
if not eval(opt.is_cuda):
opt.knn_method = 'faiss-cpu'
with Timer('Face Cluster'):
cluster_main(opt, extract_features)
print(
"=> Face cluster done! The cluster results have been saved in {}".
format(opt.output_picture_path))
class Reid_feature():
def __init__(self):
args = get_parser().parse_args()
cfg = setup_cfg(args)
self.demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
def __call__(self, img_list):
t1 = time.time()
feat = self.demo.run_on_batch(img_list)
# print('reid time:', time.time() - t1, len(img_list))
return feat
default='demo_output',
help='path to save features')
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
demo = FeatureExtractionDemo(cfg,
device=args.device,
parallel=args.parallel)
PathManager.mkdirs(args.output)
if args.input:
if PathManager.isdir(args.input[0]):
args.input = glob.glob(os.path.expanduser(args.input[0]))
assert args.input, "The input path(s) was not found"
for path in tqdm.tqdm(args.input):
img = cv2.imread(path)
feat = demo.run_on_image(img)
feat = feat.numpy()
np.save(
os.path.join(args.output,
path.replace('.jpg', '.npy').split('/')[-1]),
feat)
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
test_loader, num_query = build_reid_test_loader(
cfg, dataset_name=args.dataset_name)
demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
logger.info("Start extracting image features")
feats = []
pids = []
camids = []
for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader),
total=len(test_loader)):
feats.append(feat)
pids.extend(pid)
camids.extend(camid)
feats = torch.cat(feats, dim=0)
q_feat = feats[:num_query]
g_feat = feats[num_query:]
q_pids = np.asarray(pids[:num_query])
def __init__(self):
args = get_parser().parse_args()
cfg = setup_cfg(args)
self.demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
def reid_feature():
args = get_parser().parse_args()
cfg = setup_cfg(args)
demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
return demo
def __init__(self):
args = get_parser().parse_args()
cfg = setup_cfg(args)
self.demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
def __call__(self, img_list):
t1 = time.time()
feat = self.demo.run_on_batch(img_list)
# print('reid time:', time.time() - t1, len(img_list))
return feat
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
PathManager.mkdirs(args.output)
# onnx_output = np.load('../tools/deploy/onnx_output/0065_c6s1_009501_02.npy')
# onnx_output = np.vstack((onnx_output, onnx_output))
# print(onnx_output.shape)
if args.input:
while True:
img_list = []
# if PathManager.isdir(args.input[0]):
# args.input = glob.glob(os.path.expanduser(args.input[0]))
# assert args.input, "The input path(s) was not found"
# for path in tqdm.tqdm(os.listdir(args.input)):
t1 = time.time()
img = cv2.imread(args.input)
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
logger = setup_logger()
cfg = setup_cfg(args)
test_loader, num_query = build_reid_test_loader(cfg, args.dataset_name)
demo = FeatureExtractionDemo(cfg, device=args.device, parallel=args.parallel)
logger.info("Start extracting image features")
feats = []
pids = []
camids = []
for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader), total=len(test_loader.loader)):
feats.append(feat)
pids.extend(pid)
camids.extend(camid)
feats = torch.cat(feats, dim=0)
q_feat = feats[:num_query]
g_feat = feats[num_query:]
q_pids = np.asarray(pids[:num_query])
g_pids = np.asarray(pids[num_query:])
default='/ssd/zphe/data/MOT20_R50-ibn_results',
help='path to save features')
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
)
parser.add_argument("--cores", type=int, default=8)
return parser
# if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
videos = glob.glob(osp.join(args.input_video_dir, '*.mp4'))
demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
# def process_one_video(params):
for video in tqdm(videos):
# device = torch.device("cuda:%d" % gpu_id) if gpu_id>=0 else torch.device("cpu")
# demo.predictor.model.to(device)
videoname = osp.basename(video)
print('processing ' + videoname)
jsonfile = osp.join(args.input_det_dir, args.det_type,
videoname + '.final.reduced.json')
if not osp.exists(jsonfile):
print(jsonfile + ' not exists!!!')
continue
# return
det_results = load_json(jsonfile)
vidcap = cv2.VideoCapture(video)
save_results = {}