pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
runtime_profile = {
'dt': [],
'pt': [],
'pn': []
}
# Init data writer
writer = DataWriter(args.save_video).start()
data_len = data_loader.length()
im_names_desc = tqdm(range(data_len))
batchSize = args.posebatch
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
continue
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
def call_alphapose(input_dir, output_dir, format='open', batchSize=1):
if not os.path.exists(output_dir):
os.mkdir(output_dir)
for root, dirs, files in os.walk(input_dir):
im_names = files
print(files)
data_loader = ImageLoader(im_names,
batchSize=batchSize,
format='yolo',
dir_path=input_dir).start()
det_loader = DetectionLoader(data_loader, batchSize=batchSize).start()
det_processor = DetectionProcessor(det_loader).start()
# Load pose model
pose_dataset = Mscoco()
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
# Init data writer
writer = DataWriter(False).start()
data_len = data_loader.length()
im_names_desc = tqdm(range(data_len))
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, pt1,
pt2) = det_processor.read()
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
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)].cuda()
hm_j = pose_model(inps_j)
hm.append(hm_j)
hm = torch.cat(hm)
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
hm = hm.cpu()
writer.save(boxes, scores, hm, pt1, pt2, orig_img,
im_name.split('/')[-1])
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
while (writer.running()):
pass
writer.stop()
final_result = writer.results()
write_json(final_result, output_dir, _format=format)
correct_json_save(output_dir)
print('Over')
def test():
inputpath = args.inputpath
inputlist = args.inputlist
mode = args.mode
#if not os.path.exists(args.outputpath):
#os.mkdir(args.outputpath)
#if len(inputlist):
#im_names = open(inputlist, 'r').readlines()
#elif len(inputpath) and inputpath != '/':
for root, dirs, files in os.walk(inputpath):
im_names = files
#else:
#raise IOError('Error: must contain either --indir/--list')
im_names = sorted(im_names, key=lambda x: int(os.path.splitext(x)[0]))
print(im_names)
# Load input images
data_loader = ImageLoader(im_names, batchSize=1, format='yolo').start()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=1).start()
det_processor = DetectionProcessor(det_loader).start()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
# Init data writer
writer = DataWriter(args.save_video).start()
data_len = data_loader.length()
im_names_desc = tqdm(range(data_len))
batchSize = args.posebatch
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, pt1,
pt2) = det_processor.read()
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
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)].cuda()
hm_j = pose_model(inps_j)
hm.append(hm_j)
hm = torch.cat(hm)
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
hm = hm.cpu()
writer.save(boxes, scores, hm, pt1, pt2, orig_img,
im_name.split('/')[-1])
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
if args.profile:
# TQDM
im_names_desc.set_description(
'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'
.format(dt=np.mean(runtime_profile['dt']),
pt=np.mean(runtime_profile['pt']),
pn=np.mean(runtime_profile['pn'])))
print('===========================> Finish Model Running.')
if (args.save_img or args.save_video) and not args.vis_fast:
print(
'===========================> Rendering remaining images in the queue...'
)
print(
'===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).'
)
while (writer.running()):
pass
writer.stop()
final_result = writer.results()
write_json(final_result, args.outputpath)
return final_result
def handle_video(video_file):
# =========== common ===============
args.video = video_file
base_name = os.path.basename(args.video)
video_name = base_name[:base_name.rfind('.')]
# =========== end common ===============
img_path = f'outputs/alpha_pose_{video_name}/split_image/'
# =========== image ===============
args.inputpath = img_path
args.outputpath = f'outputs/alpha_pose_{video_name}'
if os.path.exists(args.outputpath):
shutil.rmtree(f'{args.outputpath}/vis', ignore_errors=True)
else:
os.mkdir(args.outputpath)
# if not len(video_file):
# raise IOError('Error: must contain --video')
if len(img_path) and img_path != '/':
for root, dirs, files in os.walk(img_path):
im_names = sorted([f for f in files if 'png' in f or 'jpg' in f])
else:
raise IOError('Error: must contain either --indir/--list')
# Load input images
data_loader = ImageLoader(im_names, batchSize=args.detbatch,
format='yolo').start()
print(f'Totally {data_loader.datalen} images')
# =========== end image ===============
# =========== video ===============
# args.outputpath = f'outputs/alpha_pose_{video_name}'
# if os.path.exists(args.outputpath):
# shutil.rmtree(f'{args.outputpath}/vis', ignore_errors=True)
# else:
# os.mkdir(args.outputpath)
#
# videofile = args.video
# mode = args.mode
#
# if not len(videofile):
# raise IOError('Error: must contain --video')
#
# # Load input video
# data_loader = VideoLoader(videofile, batchSize=args.detbatch).start()
# (fourcc, fps, frameSize) = data_loader.videoinfo()
#
# print('the video is {} f/s'.format(fps))
# =========== end video ===============
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
# start a thread to read frames from the file video stream
det_processor = DetectionProcessor(det_loader).start()
# Load pose model
pose_dataset = Mscoco()
if args.fast_inference:
pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
# Data writer
save_path = os.path.join(
args.outputpath,
'AlphaPose_' + ntpath.basename(video_file).split('.')[0] + '.avi')
# writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
writer = DataWriter(args.save_video).start()
print('Start pose estimation...')
im_names_desc = tqdm(range(data_loader.length()))
batchSize = args.posebatch
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, pt1,
pt2) = det_processor.read()
if orig_img is None:
print(f'{i}-th image read None: handle_video')
break
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
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)].cuda()
hm_j = pose_model(inps_j)
hm.append(hm_j)
hm = torch.cat(hm)
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
hm = hm.cpu().data
writer.save(boxes, scores, hm, pt1, pt2, orig_img,
im_name.split('/')[-1])
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
if args.profile:
# TQDM
im_names_desc.set_description(
'det time: {dt:.4f} | pose time: {pt:.4f} | post processing: {pn:.4f}'
.format(dt=np.mean(runtime_profile['dt']),
pt=np.mean(runtime_profile['pt']),
pn=np.mean(runtime_profile['pn'])))
if (args.save_img or args.save_video) and not args.vis_fast:
print(
'===========================> Rendering remaining images in the queue...'
)
print(
'===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).'
)
while writer.running():
pass
writer.stop()
final_result = writer.results()
write_json(final_result, args.outputpath)
kpts = []
for i in range(len(final_result)):
kpt = max(final_result[i]['result'],
key=lambda x: x['proposal_score'].data[0] * calculate_area(x[
'keypoints']))['keypoints']
kpts.append(kpt.data.numpy())
name = f'{args.outputpath}/{video_name}.npz'
kpts = np.array(kpts).astype(np.float32)
print('kpts npz save in ', name)
np.savez_compressed(name, kpts=kpts)
return kpts
for root, dirs, files in os.walk(inputpath):
im_names = files
else:
raise IOError('Error: must contain either --indir/--list')
# Load input images
data_loader = ImageLoader(im_names, batchSize=args.detbatch,
format='yolo').start()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
print('here will show the det_loader information')
data_loader_length = data_loader.length()
for i in range(data_loader_length):
(orig_img, im_name, boxes, scores, inps, pt1, pt2) = det_loader.read()
print('image_name', im_name)
print('boxes', boxes)
print('scores', scores)
print('inps', inps)
print('pt1', pt1)
print('pt2', pt2)
print('------------------------------------------------------------')
print('data_loader finish+++++++++++++++++++++++++++++++++++')
det_processor = DetectionProcessor(det_loader).start()
print('successful processed')
print('here is the result which is processed')
def Alphapose(
im_names,
pose_model,
):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load input images
data_loader = ImageLoader(im_names, batchSize=args.detbatch,
format='yolo').start()
# Load detection loader
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
det_processor = DetectionProcessor(det_loader).start()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
# Init data writer
writer = DataWriter(args.save_video).start()
data_len = data_loader.length()
im_names_desc = tqdm(range(data_len))
batchSize = args.posebatch
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, pt1,
pt2) = det_processor.read()
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
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)].to(device)
hm_j = pose_model(inps_j)
hm.append(hm_j)
hm = torch.cat(hm)
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
hm = hm.cpu()
writer.save(boxes, scores, hm, pt1, pt2, orig_img,
im_name.split('/')[-1])
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
if args.profile:
# TQDM
im_names_desc.set_description(
'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'
.format(dt=np.mean(runtime_profile['dt']),
pt=np.mean(runtime_profile['pt']),
pn=np.mean(runtime_profile['pn'])))
print('Finish Model Running.')
if (args.save_img or args.save_video) and not args.vis_fast:
print(
'===========================> Rendering remaining images in the queue...'
)
print(
'===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).'
)
while (writer.running()):
pass
writer.stop()
final_result = writer.results()
# write_json(final_result, args.outputpath)
if final_result[0]['result']:
return final_result[0]['result'][0]['keypoints']
else:
return None
