def pose_detection(image):
mode = ''
input_source = image
if not os.path.exists(args.outputpath):
os.makedirs(args.outputpath)
det_loader = DetectionLoader(input_source, get_detector(args), cfg, args, batchSize=args.detbatch, mode=mode,
queueSize=args.qsize)
det_worker = det_loader.start()
def run(self):
if os.path.isfile(self.video):
mode, input_source = 'video', self.video
else:
raise IOError(
'Error: --video must refer to a video file, not directory.')
if not os.path.exists(self.outputpath):
os.makedirs(self.outputpath)
det_loader = DetectionLoader(input_source,
get_detector(self),
self.cfg,
self,
batchSize=self.detbatch,
mode=mode,
queueSize=self.qsize)
det_worker = det_loader.start()
# Load pose model
pose_model = builder.build_sppe(self.cfg.MODEL,
preset_cfg=self.cfg.DATA_PRESET)
print(f'Loading pose model from {self.checkpoint}...')
pose_model.load_state_dict(
torch.load(self.checkpoint, map_location=self.device))
if self.pose_track:
tracker = Tracker(tcfg, self)
pose_model.to(self.device)
pose_model.eval()
if self.save_video:
from alphapose.utils.writer import DEFAULT_VIDEO_SAVE_OPT as video_save_opt
video_save_opt['savepath'] = self.outputpath + os.path.basename(
self.video)
video_save_opt.update(det_loader.videoinfo)
writer = DataWriter(self.cfg,
self,
save_video=True,
video_save_opt=video_save_opt,
queueSize=self.qsize).start()
else:
writer = DataWriter(self.cfg,
self,
save_video=False,
queueSize=self.qsize).start()
data_len = det_loader.length
im_names_desc = tqdm(range(data_len), dynamic_ncols=True)
batchSize = self.posebatch
try:
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, ids,
cropped_boxes) = det_loader.read()
if orig_img is None:
break
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
os.path.basename(im_name))
continue
# Pose Estimation
inps = inps.to(self.device)
datalen = inps.size(0)
leftover = 0
if (datalen) % batchSize:
leftover = 1
num_batches = datalen // batchSize + leftover
hm = []
for j in range(num_batches):
inps_j = inps[j * batchSize:min((j + 1) *
batchSize, datalen)]
hm_j = pose_model(inps_j)
hm.append(hm_j)
hm = torch.cat(hm)
#hm = hm.cpu()
if self.pose_track:
boxes, scores, ids, hm, cropped_boxes = track(
tracker, self, orig_img, inps, boxes, hm,
cropped_boxes, im_name, scores)
writer.save(boxes, scores, ids, hm, cropped_boxes,
orig_img, os.path.basename(im_name))
while (writer.running()):
time.sleep(1)
print('===========================> Rendering remaining ' +
str(writer.count()) + ' images in the queue...')
writer.stop()
det_loader.stop()
except KeyboardInterrupt:
det_loader.terminate()
while (writer.running()):
time.sleep(1)
print('===========================> Rendering remaining ' +
str(writer.count()) + ' images in the queue...')
writer.stop()
self.all_results = writer.results()
self._save()
