def _py_func_rgb_test_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)
total_rgb_file = []
try:
index_list = np.arange(0, rgb_len,
rgb_len // _frame_counts)[:_frame_counts]
except:
index_list = np.arange(0, rgb_len)[:_frame_counts]
image_list = []
for index in index_list:
rgb_cap.set(cv2.CAP_PROP_POS_FRAMES, index)
_, 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]
image = np.stack(image_list, axis=0)
img = image
if _test_crop == 'center':
rgb_file = DataAugmentation.center_crop(img, 224, 224)
rgb_file = np.float32(rgb_file)
rgb_file = normalize(rgb_file)
rgb_file_flip = rgb_file[:, :, ::-1, :]
total_rgb_file.append(rgb_file)
total_rgb_file.append(rgb_file_flip)
else:
for k in range(5):
image = DataAugmentation.random_Crop(img, 1, k)
image = np.float32(image)
image = normalize(image)
image_flip = image[:, :, ::-1, :]
total_rgb_file.append(image)
total_rgb_file.append(image_flip)
rgb_cap.release()
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return total_rgb_file, one_hot_label
return total_rgb_file
def _py_func_rgb_test_vp(rgb_path, label):
rgb_path = rgb_path.decode()
_batch_size = 25
rgb_cap = cv2.VideoCapture(rgb_path)
rgb_len = rgb_cap.get(cv2.CAP_PROP_FRAME_COUNT) - 10
total_rgb_file = []
if (rgb_len) <= _batch_size:
factor = int((_batch_size - 1) // (rgb_len) + 1)
index_list = np.concatenate([np.arange(0, rgb_len)] * factor,
axis=-1)[:_batch_size]
else:
index_list = np.arange(0, rgb_len,
rgb_len // _batch_size)[:_batch_size]
for index in index_list:
rgb_cap.set(cv2.CAP_PROP_POS_FRAMES, index)
_, image = rgb_cap.read()
if image is not None:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = np.float32(image)
image = cv2.resize(image, (340, 256))
if test_crop == 'center':
image_ = DataAugmentation.center_crop(image, 224, 224)
if _preprocess_name == 'pytorch':
image_ = normalize(image_)
elif _preprocess_name == 'tf':
image_ = tf_preprocess(image_)
image_flip = np.fliplr(image_)
total_rgb_file.append(image_)
total_rgb_file.append(image_flip)
else:
for i in range(5):
image_ = DataAugmentation.random_Crop(image, 1, i)
if _preprocess_name == 'pytorch':
image_ = normalize(image_)
elif _preprocess_name == 'tf':
image_ = tf_preprocess(image_)
image_flip = np.fliplr(image_)
total_rgb_file.append(image_)
total_rgb_file.append(image_flip)
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return total_rgb_file, one_hot_label
return total_rgb_file
def _py_func_rgb_test_vp(rgb_path, label):
rgb_path = rgb_path.decode ()
rgb_file = os.listdir (rgb_path)
rgb_file = sorted(rgb_file)
_batch_size = 25
if len(rgb_file) < _batch_size:
index_list = np.arange(0,len(rgb_file))
index_list = np.concatenate([index_list]*(_batch_size//len(rgb_file) + 1),axis=0)[:_batch_size]
else:
index_list = np.arange(0,len(rgb_file),len(rgb_file)//_batch_size)[:_batch_size]
img_list = []
for i in index_list:
rgb_file_path = os.path.join(rgb_path,rgb_file[i])
img = cv2.imread(rgb_file_path)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img = cv2.resize(img,(340,256))
for j in range(5):
image = DataAugmentation.random_Crop(img,1,j)
image = cv2.resize(image,(224,224))
image = np.float32(image)
image = normalize(image)
image_flip = np.fliplr(image)
img_list.append(image)
img_list.append(image_flip)
if label is not None:
one_hot_label = np.zeros (101, dtype=np.float32)
one_hot_label[label] = 1
return img_list, one_hot_label
return img_list
def _py_func_flow_test_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)
flow_len = len(flow_file)
try:
index_list = np.arange(0, flow_len - _new_length,
(flow_len - _new_length) //
_frame_counts)[:_frame_counts]
except:
index_list = np.arange(0, flow_len - _new_length)[:_frame_counts]
total_img_list = []
video_list = []
for index in index_list:
img_list = []
for i in range(index, index + _new_length):
img_u_path = os.path.join(flow_u_path, flow_file[i])
img_v_path = os.path.join(flow_v_path, flow_file[i])
img_u = cv2.imread(img_u_path, 0)
img_v = cv2.imread(img_v_path, 0)
if img_u is not None and img_v is not None:
img = np.stack([img_u, img_v], axis=-1)
img = cv2.resize(img, (340, 256))
img_list.append(img)
img = np.concatenate(img_list, axis=-1)
video_list.append(img)
if len(video_list) != _frame_counts:
video_list = np.concatenate([video_list] * _frame_counts,
axis=0)[:_frame_counts]
img = np.stack(video_list, axis=0)
if _test_crop == 'center':
image = DataAugmentation.center_crop(img, 224, 224)
image = np.float32(image)
image = (image / 255 - 0.5) / 0.226
image_flip = image[:, :, ::-1, :]
total_img_list.append(image)
total_img_list.append(image_flip)
else:
for k in range(5):
image = DataAugmentation.random_Crop(img, 1, k)
image = np.float32(image)
image = (image / 255 - 0.5) / 0.226
image_flip = image[:, :, ::-1, :]
total_img_list.append(image)
total_img_list.append(image_flip)
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return total_img_list, one_hot_label
return total_img_list
def _py_func_flow_test_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)
_batch_size = 25
if len(flow_file) - _new_length < _batch_size:
index_list = np.arange(0, len(flow_file) - _new_length)
index_list = np.concatenate([index_list] *
(_batch_size //
(len(flow_file) - _new_length) + 1),
axis=0)[:_batch_size]
else:
index_list = np.arange(0,
len(flow_file) - _new_length,
(len(flow_file) - _new_length) //
_batch_size)[:_batch_size]
total_img_list = []
for index in index_list:
img_list = []
for i in range(index, index + _new_length):
img_u_path = os.path.join(flow_u_path, flow_file[i])
img_v_path = os.path.join(flow_v_path, flow_file[i])
img_u = cv2.imread(img_u_path, 0)
img_v = cv2.imread(img_v_path, 0)
# if img_u is not None and img_v is not None:
img = np.stack([img_u, img_v], axis=-1)
img_list.append(img)
img = np.concatenate(img_list, axis=-1)
img = cv2.resize(img, (340, 256))
if test_crop == 'center':
image = DataAugmentation.center_crop(img, 224, 224)
image = np.float32(image)
if _preprocess_name == 'pytorch':
image = (image / 255 - 0.5) / 0.226
elif _preprocess_name == 'tf':
image = tf_preprocess(image)
image_flip = np.fliplr(image)
total_img_list.append(image)
total_img_list.append(image_flip)
else:
for j in range(5):
image = DataAugmentation.random_Crop(img, 1, j)
image = np.float32(image)
if _preprocess_name == 'pytorch':
image = (image / 255 - 0.5) / 0.226
elif _preprocess_name == 'tf':
image = tf_preprocess(image)
image_flip = np.fliplr(image)
total_img_list.append(image)
total_img_list.append(image_flip)
if label is not None:
one_hot_label = np.zeros(101, dtype=np.float32)
one_hot_label[label] = 1
return total_img_list, one_hot_label
return total_img_list
def _py_func_test_vp(rgb_path, flow_path, label):
rgb_path = rgb_path.decode ()
flow_path = flow_path.decode()
rgb_cap = cv2.VideoCapture(rgb_path)
flow_cap = cv2.VideoCapture(flow_path)
rgb_file = []
flow_file = []
while 1:
flag , img = rgb_cap.read()
if flag is False:break
if img is not None:
img = np.float32(img)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img = cv2.resize(img,(340,256))
rgb_file.append(img)
while 1:
flag , img = flow_cap.read()
if flag is False:break
if img is not None:
img = np.float32(img)
img = cv2.resize(img,(340,256))
flow_file.append(img[...,:2])
_batch_size = 25
min_len = min(len(rgb_file),len(flow_file)) - 10
if min_len <= _batch_size:
factor = (_batch_size - 1) // min_len + 1
index_list = np.concatenate([np.arange(0,min_len)]*factor,axis=-1)[:_batch_size]
else:
index_list = np.arange(0,min_len,min_len // _batch_size)[:_batch_size]
rgb_file_list = []
flow_file_list = []
for i in index_list:
r_img = rgb_file[i]
# r_img = DataAugmentation.center_crop(r_img,224,224)
for j in range(5):
image_ = DataAugmentation.random_Crop(r_img,1,j)
image_ = normalize(image_)
rgb_file_list.append(image_)
# r_img_flip = np.fliplr(r_img)
# rgb_file_list.append(r_img_flip)
for i in index_list:
f_img = flow_file[i:i+10]
f_img = np.concatenate(f_img,axis=-1)
for j in range(5):
image_ = DataAugmentation.random_Crop(f_img,1,j)
image_ = (image_ / 255) * 2 - 1
flow_file_list.append(image_)
# f_image = normalize(f_image,mean=[0.5],std=[0.226])
# f_img_flip = np.fliplr(f_img)
# flow_file_list.append(f_img_flip)
r_file = np.stack(rgb_file_list,axis=0)
f_file = np.stack(flow_file_list,axis=0)
one_hot_label = np.zeros (101, dtype=np.float32)
one_hot_label[label] = 1
return r_file, f_file, one_hot_label