def _load_pairs(self, vid_info):
length = vid_info["length"]
ft_ids = vid_info["ft_ids"]
ns = self._opt.num_source
replace = ns >= len(ft_ids)
src_ids = list(np.random.choice(ft_ids, ns, replace=replace))
src_ids[0] = ft_ids[0]
tsf_ids = list(
np.random.choice(length, self._opt.time_step, replace=False))
tsf_ids.sort()
pair_ids = src_ids + tsf_ids
smpls = vid_info["smpls"][pair_ids]
images = []
masks = []
images_paths = vid_info['images']
alphas_paths = vid_info["alphas"]
for t in pair_ids:
image = cv_utils.read_cv2_img(images_paths[t])
images.append(image)
mask = cv_utils.read_mask(alphas_paths[t], self._opt.image_size)
# front is 0, and background is 1
mask = 1.0 - mask
masks.append(mask)
return images, smpls, masks, vid_info["offsets"]
def _load_pairs(self, vid_info):
length = vid_info['length']
start = np.random.randint(0, 15)
end = np.random.randint(0, length)
pair_ids = np.array([start, end], dtype=np.int32)
smpls = vid_info["smpls"][pair_ids]
images = []
masks = []
images_paths = vid_info['images']
alphas_paths = vid_info["alphas"]
for t in pair_ids:
image_path = images_paths[t]
image = cv_utils.read_cv2_img(image_path)
images.append(image)
mask = cv_utils.read_mask(alphas_paths[t], self._opt.image_size)
# front is 0, and background is 1
mask = 1.0 - mask
masks.append(mask)
return images, smpls, masks, vid_info["offsets"]
def _load_pairs(self, vid_info):
ns = self._opt.num_source
length = vid_info["length"]
ft_ids = vid_info["ft_ids"]
replace = ns >= len(ft_ids)
src_ids = list(np.random.choice(ft_ids, ns, replace=replace))
src_ids[0] = ft_ids[0]
tsf_ids = list(
np.random.choice(length, self._opt.time_step, replace=False))
tsf_ids.sort()
# take the source and target ids
pair_ids = src_ids + tsf_ids
smpls = vid_info["smpls"][pair_ids]
images = []
masks = []
image_dir = vid_info["img_dir"]
images_names = vid_info["images"]
alphas_paths = vid_info["alpha_paths"]
for t in pair_ids:
image_path = os.path.join(image_dir, images_names[t])
image = cv_utils.read_cv2_img(image_path)
images.append(image)
mask = cv_utils.read_mask(alphas_paths[t], self._opt.image_size)
# front is 0, and background is 1
mask = 1.0 - mask
masks.append(mask)
return images, smpls, masks
def _load_pairs(self, index):
vid_info = self._vids_info[index % self._num_videos]
length = vid_info["length"]
img_dir = vid_info["img_dir"]
src_ids = vid_info["src_ids"]
offsets = vid_info["offsets"]
alpha_paths = vid_info["alpha_paths"]
replaced_paths = vid_info["replaced_paths"]
actual_bg_path = vid_info["actual_bg_path"]
bg_dir = vid_info["bg_dir"]
if length < self._opt.time_step:
replace = True
else:
replace = False
tsf_ids = list(np.random.choice(length, self._opt.time_step, replace=replace))
pair_ids = src_ids + tsf_ids
# print(pair_ids, vid_info["probs"])
# print(len(pair_ids), src_ids, pair_ids)
# print(pair_ids, np.random.random())
# if length > 1:
# tsf_ids = [src_ids[0]] + [index]
# pair_ids = src_ids + tsf_ids
# else:
# pair_ids = [0, 0]
smpls = vid_info["smpls"][pair_ids]
images_name = vid_info["images"]
images = []
masks = []
pseudo_bgs = []
for t in pair_ids:
name = images_name[t]
image_path = os.path.join(img_dir, name)
image = cv_utils.read_cv2_img(image_path)
images.append(image)
mask = cv_utils.read_mask(alpha_paths[t], self._opt.image_size)
# attention! Here, the front is 0, and the background is 1
mask = 1.0 - mask
masks.append(mask)
if self._share_bg:
if actual_bg_path is not None:
bg_path = actual_bg_path
else:
bg_path = np.random.choice(replaced_paths)
bg_img = cv_utils.read_cv2_img(bg_path)
bg_img = cv_utils.normalize_img(bg_img, image_size=self._opt.image_size, transpose=True)
pseudo_bgs = bg_img
else:
if actual_bg_path is not None:
bg_img_paths = [actual_bg_path] * len(src_ids)
else:
bg_img_paths = []
for s_id in src_ids:
name = images_name[s_id]
bg_name = name.split(".")[0] + "_replaced.png"
bg_path = os.path.join(bg_dir, bg_name)
bg_img_paths.append(bg_path)
for bg_path in bg_img_paths:
bg_img = cv_utils.read_cv2_img(bg_path)
bg_img = cv_utils.normalize_img(bg_img, image_size=self._opt.image_size, transpose=True)
pseudo_bgs.append(bg_img)
pseudo_bgs = np.stack(pseudo_bgs)
links_ids = vid_info["links"]
return images, smpls, masks, offsets, pseudo_bgs, links_ids
