def _py_func_rgb_vp(rgb_path, label):
rgb_path = rgb_path.decode()
rgb_cap = cv2.VideoCapture(rgb_path)
rgb_len = rgb_cap.get(cv2.CAP_PROP_FRAME_COUNT)
image_list = []
index = np.random.randint(0, rgb_len - _frame_counts)
index_list = np.arange(index, index + _frame_counts)
for index in index_list:
rgb_cap.set(cv2.CAP_PROP_POS_FRAMES, index)
flag, image = rgb_cap.read()
if image is not None:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (340, 256))
image_list.append(image)
if len(image_list) != _frame_counts:
image_list = np.concatenate([image_list] * _frame_counts,
axis=0)[:_frame_counts]
assert image_list != []
rgb_cap.release()
image = np.stack(image_list, axis=0)
rgb_file = DataAugmentation.randomCrop(image, 224, 224)
rgb_file = DataAugmentation.horizontal_flip(rgb_file)
rgb_file = np.float32(rgb_file)
rgb_file = normalize(rgb_file)
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return rgb_file, one_hot_label
return rgb_file
def _py_func_flow_vp(flow_path, label):
f_upath, f_vpath = flow_path
flow_u_path = f_upath.decode()
flow_v_path = f_vpath.decode()
flow_file = os.listdir(flow_u_path)
flow_file = sorted(flow_file)
index = np.random.randint(0, len(flow_file) - _frame_counts)
img_list = []
for j in range(index, index + _frame_counts):
img_u_path = os.path.join(flow_u_path, flow_file[j])
img_v_path = os.path.join(flow_v_path, flow_file[j])
img_u = cv2.imread(img_u_path, 0)
img_v = cv2.imread(img_v_path, 0)
if img_u is None or img_v is None:
continue
img = np.stack([img_u, img_v], axis=-1)
img = cv2.resize(img, (340, 256))
img_list.append(img)
img = np.stack(img_list, axis=0)
img = DataAugmentation.randomCrop(img, 224, 224)
img = DataAugmentation.horizontal_flip(img)
img = np.float32(img)
img = (img / 255 - 0.5) / 0.226
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return img, one_hot_label
return img
def _py_func_rgb_vp(rgb_path, label):
rgb_path = rgb_path.decode ()
# rgb_file = _rgb_vp (rgb_path)
rgb_cap = cv2.VideoCapture (rgb_path)
rgb_len = rgb_cap.get (cv2.CAP_PROP_FRAME_COUNT)
image_list = []
# while 1:
try:
index = np.random.randint (0, rgb_len-_frame_counts)
except:
index = 0
rgb_cap.set (cv2.CAP_PROP_POS_FRAMES, index)
while 1:
flag, image = rgb_cap.read ()
if image is not None:
# image = np.float32 (image)
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
image = cv2.resize (image, (340, 256))
image_list.append(image)
if len(image_list) == _frame_counts or flag is False:
if len(image_list) == 0:
rgb_cap.set (cv2.CAP_PROP_POS_FRAMES, 0)
continue
break
rgb_cap.release ()
image = np.stack(image_list,axis=0)
# some bug in rgb video,maybe you should use ffmpeg for image ,but read video is faster than read image due to IO spare
assert 0 not in image.shape
if _frame_counts != image.shape[0]:
image = np.repeat(image,(_frame_counts//image.shape[0])+1,axis=0)[:_frame_counts,...]
assert (_frame_counts,256,340,3) == image.shape
if _crop == 'random':
rgb_file = DataAugmentation.randomCrop(image,224,224)
# print('random crop is used')
else:
rgb_file = DataAugmentation.Multiscale_crop(image)
# print('mutltisacle crop is used')
rgb_file = np.float32([cv2.resize(x,(224,224)) for x in rgb_file])
assert 0 not in rgb_file.shape
rgb_file = DataAugmentation.horizontal_flip (rgb_file)
rgb_file = np.float32(rgb_file)
rgb_file = normalize (rgb_file)
# rgb_file = (rgb_file / 255) * 2 - 1
assert rgb_file is not None
if label is not None:
one_hot_label = np.zeros (101, dtype=np.float32)
one_hot_label[label] = 1
return rgb_file, one_hot_label
return rgb_file
