pose_cords = []
for idx in tqdm(range(len(os.listdir(str(img_dir))))):
img_path = img_dir.joinpath('{:05}.png'.format(idx))
img = cv2.imread(str(img_path))
shape_dst = np.min(img.shape[:2])
oh = (img.shape[0] - shape_dst) // 2
ow = (img.shape[1] - shape_dst) // 2
img = img[oh:oh + shape_dst, ow:ow + shape_dst]
img = cv2.resize(img, (512, 512))
multiplier = get_multiplier(img)
with torch.no_grad():
paf, heatmap = get_outputs(multiplier, img, model, 'rtpose')
r_heatmap = np.array([
remove_noise(ht) for ht in heatmap.transpose(2, 0, 1)[:-1]
]).transpose(1, 2, 0)
heatmap[:, :, :-1] = r_heatmap
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
#TODO get_pose
label, cord = get_pose(param, heatmap, paf)
index = 13
crop_size = 25
try:
head_cord = cord[index]
except:
head_cord = pose_cords[
-1] # if there is not head point in picture, use last frame
pose_cords.append(head_cord)
head = img[int(head_cord[1] - crop_size):int(head_cord[1] + crop_size),
test_head_dir.mkdir(exist_ok=True)
pose_cords = []
for idx in tqdm(range(len(os.listdir(str(img_dir))))):
img_path = img_dir.joinpath('{:05}.png'.format(idx))
img = cv2.imread(str(img_path))
shape_dst = np.min(img.shape[:2])
oh = (img.shape[0] - shape_dst) // 2
ow = (img.shape[1] - shape_dst) // 2
img = img[oh:oh + shape_dst, ow:ow + shape_dst]
img = cv2.resize(img, (512, 512))
multiplier = get_multiplier(img)
with torch.no_grad():
paf, heatmap = get_outputs(multiplier, img, model, 'rtpose')
r_heatmap = np.array([remove_noise(ht)
for ht in heatmap.transpose(2, 0, 1)[:-1]]) \
.transpose(1, 2, 0)
heatmap[:, :, :-1] = r_heatmap
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
label, cord = get_pose(param, heatmap, paf)
index = 13
crop_size = 25
try:
head_cord = cord[index]
except:
head_cord = pose_cords[
-1] # if there is not head point in picture, use last frame
pose_cords.append(head_cord)
head = img[int(head_cord[1] - crop_size):int(head_cord[1] + crop_size),
def extract_poses(model, save_dir):
'''make label images for pix2pix'''
train_dir = os.path.join(save_dir, 'train')
os.makedirs(train_dir, exist_ok=True)
train_img_dir = os.path.join(train_dir, 'train_img')
os.makedirs(train_img_dir, exist_ok=True)
train_label_dir = os.path.join(train_dir, 'train_label')
os.makedirs(train_label_dir, exist_ok=True)
train_head_dir = os.path.join(train_dir, 'head_img')
os.makedirs(train_head_dir, exist_ok=True)
img_dir = os.path.join(save_dir, 'images')
pose_cords = []
for idx in tqdm(range(len(os.listdir(img_dir)))):
img_path = os.path.join(img_dir, '{:05}.png'.format(idx))
img = cv2.imread(img_path)
shape_dst = np.min(img.shape[:2])
oh = (img.shape[0] - shape_dst) // 2
ow = (img.shape[1] - shape_dst) // 2
img = img[oh:oh + shape_dst, ow:ow + shape_dst]
img = cv2.resize(img, (512, 512))
multiplier = get_multiplier(img)
with torch.no_grad():
paf, heatmap = get_outputs(multiplier, img, model, 'rtpose', device)
r_heatmap = np.array([remove_noise(ht)
for ht in heatmap.transpose(2, 0, 1)[:-1]]).transpose(1, 2, 0)
heatmap[:, :, :-1] = r_heatmap
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
label, cord = get_pose(param, heatmap, paf)
index = 13
crop_size = 25
try:
head_cord = cord[index]
except IndexError:
try:
head_cord = pose_cords[-1] # if there is not head point in picture, use last frame
except IndexError:
print("skipping 1st frame as pose detection failed")
continue
head = img[int(head_cord[1] - crop_size): int(head_cord[1] + crop_size),
int(head_cord[0] - crop_size): int(head_cord[0] + crop_size), :]
try:
plt.imshow(head)
plt.savefig(os.path.join(train_head_dir, 'pose_{}.jpg'.format(idx)))
plt.clf()
cv2.imwrite(os.path.join(train_img_dir, '{:05}.png'.format(idx)), img)
cv2.imwrite(os.path.join(train_label_dir, '{:05}.png'.format(idx)), label)
except Exception as e:
print(e)
continue
pose_cords.append(head_cord)
pose_cords_arr = np.array(pose_cords, dtype=np.int)
np.save(os.path.join(save_dir, 'pose_source.npy'), pose_cords_arr)
torch.cuda.empty_cache()
