"/home/filip/Documents/image_to_2D/pose_hrnet_w32_256x256.pth"
},
'DEBUG': {
'DEBUG': True,
'SAVE_BATCH_IMAGES_GT': True,
'SAVE_BATCH_IMAGES_PRED': True,
'SAVE_HEATMAPS_GT': True,
'SAVE_HEATMAPS_PRED': True
}
}
# cudnn related setting
cudnn.benchmark = cfg['CUDNN']['BENCHMARK']
torch.backends.cudnn.deterministic = cfg['CUDNN']['DETERMINISTIC']
torch.backends.cudnn.enabled = cfg['CUDNN']['ENABLED']
model = get_pose_net(cfg, is_train=False).cuda()
model.load_state_dict(torch.load(cfg['TEST']['MODEL_FILE']), strict=False)
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(
use_target_weight=cfg['LOSS']['USE_TARGET_WEIGHT']).cuda()
test_transforms = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Data loading code
files = get_all_files_from_path('/media/filip/HDD1/Images/', "jpg")
def main():
with torch.no_grad():
config = 'wholebody_w48_384x288.yaml'
cfg.merge_from_file(config)
# dump_input = torch.randn(1, 3, 256, 256)
# newmodel = PoseHighResolutionNet()
newmodel = get_pose_net(cfg, is_train=False)
print(newmodel)
# dump_output = newmodel(dump_input)
# print(dump_output.size())
checkpoint = torch.load('./hrnet_w48_coco_wholebody_384x288-6e061c6a_20200922.pth')
# newmodel.load_state_dict(checkpoint['state_dict'])
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if 'backbone.' in k:
name = k[9:] # remove module.
if 'keypoint_head.' in k:
name = k[14:] # remove module.
new_state_dict[name] = v
newmodel.load_state_dict(new_state_dict)
newmodel.cuda().eval()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
input_path = '/home/ubuntu/workspace/data/sign/val'
paths = []
names = []
for root, _, fnames in natsorted(os.walk(input_path)):
for fname in natsorted(fnames):
path1 = os.path.join(root, fname)
if 'depth' in fname:
continue
paths.append(path1)
names.append(fname)
print(len(paths))
# paths = paths[:4]
# names = names[:4]
step = 600
start_step = 6
# paths = paths[start_step*step:(start_step+1)*step]
# names = names[start_step*step:(start_step+1)*step]
paths = paths[4200:]
names = names[4200:]
paths = paths[::-1]
names = names[::-1]
for i, path in enumerate(paths):
# if i > 1:
# break
output_npy = 'npy3/{}.npy'.format(names[i])
if os.path.exists(output_npy):
continue
cap = cv2.VideoCapture(path)
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
# frame_width = 256
# frame_height = 256
print(path)
# output_filename = os.path.join('out_test', names[i])
# img = Image.open(image_path)
# fps = cap.get(cv2.CAP_PROP_FPS)
# writer = cv2.VideoWriter(output_filename,cv2.VideoWriter_fourcc('M','P','4','V'), 5, (frame_width,frame_height))
output_list = []
while cap.isOpened():
success, img = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
break
# img = cv2.resize(img, (256,256))
frame_height, frame_width = img.shape[:2]
img = cv2.flip(img, flipCode=1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
out = []
for scale in multi_scales:
if scale != 512:
img_temp = cv2.resize(img, (scale,scale))
else:
img_temp = img
img_temp = stack_flip(img_temp)
img_temp = norm_numpy_totensor(img_temp).cuda()
hms = newmodel(img_temp)
if scale != 512:
out.append(f.interpolate(hms, (frame_width // 4,frame_height // 4), mode='bilinear'))
else:
out.append(hms)
out = merge_hm(out)
# print(out.size())
# hm, _ = torch.max(out, 1)
# hm = hm.cpu().numpy()
# print(hm.shape)
# np.save('hm.npy', hm)
result = out.reshape((133,-1))
result = torch.argmax(result, dim=1)
# print(result)
result = result.cpu().numpy().squeeze()
# print(result.shape)
y = result // (frame_width // 4)
x = result % (frame_width // 4)
pred = np.zeros((133, 3), dtype=np.float32)
pred[:, 0] = x
pred[:, 1] = y
hm = out.cpu().numpy().reshape((133, frame_height//4, frame_height//4))
pred = pose_process(pred, hm)
pred[:,:2] *= 4.0
# print(pred.shape)
assert pred.shape == (133, 3)
# print(arg.cpu().numpy())
# np.save('npy/{}.npy'.format(names[i]), np.array([x,y,score]).transpose())
output_list.append(pred)
# img = np.asarray(img)
# for j in range(133):
# img = cv2.circle(img, (int(x[j]), int(y[j])), radius=2, color=(255,0,0), thickness=-1)
# img = plot_pose(img, pred)
# cv2.imwrite('out/{}.png'.format(names[i]), cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
# writer.write(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
output_list = np.array(output_list)
# print(output_list.shape)
np.save(output_npy, output_list)
cap.release()