def handle_video(videofile):
args.video = videofile
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))
# 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(videofile).split('.')[0] + '.avi')
writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'XVID'), fps,
frameSize).start()
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:
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()
# 获取第 0 个框的人
kpts = []
for i in range(len(final_result)):
try:
preds = final_result[i]['result']
# preds[i]['keypoints'] (17,2)
# preds[i]['kp_score'] (17,1)
# preds[i]['proposal_score'] (1)
# 选择 y 坐标最大的人 —— 用于打羽毛球视频
max_index = 0
min_index = 0
# max_y = np.mean(preds[0]['keypoints'].data.numpy()[:, 1])
min_x = np.mean(preds[0]['keypoints'].data.numpy()[:, 0])
max_x = np.mean(preds[0]['keypoints'].data.numpy()[:, 0])
for k in range(len(preds)):
# tmp_y = np.mean(preds[k]['keypoints'].data.numpy()[:, 1])
tmp_x = np.mean(preds[k]['keypoints'].data.numpy()[:, 0])
# if tmp_y > max_y:
if tmp_x < min_x:
min_index = k
# max_y = tmp_y
min_x = tmp_x
for k in range(len(preds)):
# tmp_y = np.mean(preds[k]['keypoints'].data.numpy()[:, 1])
tmp_x = np.mean(preds[k]['keypoints'].data.numpy()[:, 0])
# if tmp_y > max_y:
if tmp_x > max_x:
max_index = k
max_x = tmp_x
mid_index = 0
for k in range(len(preds)):
if k == max_index or k == min_index:
continue
mid_index = k
kpt = preds[mid_index]['keypoints']
# kpt = final_result[i]['result'][0]['keypoints']
kpts.append(kpt.data.numpy())
except:
# print(sys.exc_info())
print('error...')
filename = os.path.basename(args.video).split('.')[0]
name = filename + '.npz'
kpts = np.array(kpts).astype(np.float32)
# print('kpts npz save in ', name)
# np.savez_compressed(name, kpts=kpts)
return kpts
im_name=str(i) + '.jpg')
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
# TQDM
im_names_desc.set_description(
'load time: {ld:.4f} | det time: {dt:.4f} | det NMS: {dn:.4f} | pose time: {pt:.4f} | post process: {pn:.4f}'
.format(ld=np.mean(runtime_profile['ld']),
dt=np.mean(runtime_profile['dt']),
dn=np.mean(runtime_profile['dn']),
pt=np.mean(runtime_profile['pt']),
pn=np.mean(runtime_profile['pn'])))
except KeyboardInterrupt:
break
print(' ')
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)
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 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 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 ===============
# =========== image ===============
# img_path = f'outputs/alpha_pose_{video_name}/split_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)
return final_result, video_name
def main(file_name):
# videofile = args.video
videofile = file_name
mode = args.mode
if not os.path.exists(args.outputpath):
os.mkdir(args.outputpath)
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()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
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(videofile).split('.')[0] + '.avi')
writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'XVID'), fps,
frameSize).start()
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:
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
import ipdb
ipdb.set_trace()
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'])))
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)
def handle_video(videofile, no_nan=True):
args.video = videofile
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()
cam_w = frameSize[0]
cam_h = frameSize[1]
print('the video is {} f/s'.format(fps))
# 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(videofile).split('.')[0] + '.avi')
writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'XVID'), fps,
frameSize).start()
im_names_desc = tqdm(range(data_loader.length()))
batchSize = args.posebatch
frames_w_pose = []
frame_cnt = 0
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:
break
frame_cnt += 1
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
frames_w_pose.append(frame_cnt - 1)
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()
kpts = []
if not no_nan:
for i in range(frame_cnt):
# initialize to NaN so we can interpolate later
kpts.append(np.full((17, 2), np.nan, dtype=np.float32))
for i in range(len(final_result)):
try:
kpt = final_result[i]['result'][0]['keypoints']
if not no_nan:
kpts[frames_w_pose[i]] = kpt.data.numpy()
else:
kpts.append(kpt.data.numpy())
except:
print('error...')
kpts = np.array(kpts).astype(np.float32)
#filename = os.path.basename(args.video).split('.')[0]
#name = filename + '.npz'
#print('kpts npz save in ', name)
#np.savez_compressed(name, kpts=kpts, fps=fps, cam_w=cam_w, cam_h=cam_h)
return kpts, fps, cam_w, cam_h
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
class AlphaPose():
def __init__(self, videofile, mode='normal'):
self.videofile = videofile
self.mode = mode
self.data_loader = VideoLoader(self.videofile,
batchSize=args.detbatch).start()
(fourcc, fps, frameSize) = self.data_loader.videoinfo()
self.fourcc = fourcc
self.fps = fps
self.frameSize = frameSize
self.det_loader = DetectionLoader(self.data_loader,
batchSize=args.detbatch).start()
self.det_processor = DetectionProcessor(self.det_loader).start()
self.pose_dataset = Mscoco()
save_path = os.path.join(
args.outputpath, 'AlphaPose_' +
ntpath.basename(self.videofile).split('.')[0] + '.mp4')
self.writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'DIVX'), self.fps,
self.frameSize).start()
self.results = list()
def pose_estimation(self, pose_model):
batchSize = args.posebatch
for i in range(self.data_loader.length()):
with torch.no_grad(
): #不計算導數以此減少運算量 可用在model evaluating時 將inference的code放在其中
(inps, orig_img, im_name, boxes, scores, pt1,
pt2) = self.det_processor.read()
if orig_img is None:
break
if boxes is None or boxes.nelement() == 0:
self.writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
# 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)
hm = hm.cpu().data
self.writer.save(boxes, scores, hm, pt1, pt2, orig_img,
im_name.split('/')[-1])
def run(self):
args.mode = self.mode
if args.fast_inference:
pose_model = InferenNet_fast(4 * 1 + 1, self.pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, self.pose_dataset)
pose_model.cuda()
pose_model.eval()
#pose estimation
print('Start Pose Estimating...')
self.pose_estimation(pose_model)
print('Finish Model Running.')
if (args.save_img or args.save_video) and not args.vis_fast:
print('Rendering remaining images in the queue...')
while (self.writer.running()):
pass
self.writer.stop()
self.results = self.writer.results().copy()
def arm_pos(self):
arms = []
for frame in self.results:
#body是每個人的骨架的dictionary
jpg = frame['imgname'].split('.')
for body in frame['result']:
arm = [
] #[int, tuple, tuple, tuple, tuple] -> [frame_number, left_x1y1, left_x2y2, right_x1y1, right_x2y2]
arm.append(int(jpg[0]))
joint_list = body['keypoints'].tolist()
arm.append((int(joint_list[7][0]), int(joint_list[7][1])))
arm.append((int(joint_list[9][0]), int(joint_list[9][1])))
arm.append((int(joint_list[8][0]), int(joint_list[8][1])))
arm.append((int(joint_list[10][0]), int(joint_list[10][1])))
arms.append(arm)
return arms
def arm_pos_json(self, json_path):
with open(json_path, 'r', encoding='utf-8') as f:
result = json.load(f)
#json_format
arms = []
l_index = [21, 27]
r_index = [24, 30]
for body in result:
#body是每個人的骨架的dictionary
arm = [
] #[int, tuple, tuple, tuple, tuple] -> [frame_number, left_x1y1, left_x2y2, right_x1y1, right_x2y2]
jpg = body['image_id'].split('.')
arm.append(int(jpg[0]))
for i in l_index:
arm.append(
(int(body['keypoints'][i]), int(body['keypoints'][i + 1])))
for i in r_index:
arm.append(
(int(body['keypoints'][i]), int(body['keypoints'][i + 1])))
arms.append(arm)
return arms
