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input_test_data.py
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input_test_data.py
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# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for load test data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import PIL.Image as Image
import random
import numpy as np
import cv2
import time
def get_data(filename, num_frames_per_clip=64, s_index=0):
ret_arr = []
filenames = ''
for parent, dirnames, filenames in os.walk(filename):
if len(filenames)==0:
print('DATA_ERRO: %s'%filename)
return [], s_index, True
if (len(filenames)-s_index) <= num_frames_per_clip:
filenames = sorted(filenames)
if len(filenames) < num_frames_per_clip:
for i in range(num_frames_per_clip):
if i >= len(filenames):
i = len(filenames)-1
image_name = str(filename) + '/' + str(filenames[i])
img = Image.open(image_name)
img_data = np.array(img)
ret_arr.append(img_data)
else:
for i in range(num_frames_per_clip):
image_name = str(filename) + '/' + str(filenames[len(filenames)-num_frames_per_clip+i])
img = Image.open(image_name)
img_data = np.array(img)
ret_arr.append(img_data)
return ret_arr, s_index, True
filenames = sorted(filenames)
for i in range(s_index, s_index + num_frames_per_clip):
image_name = str(filename) + '/' + str(filenames[i])
img = Image.open(image_name)
img_data = np.array(img)
ret_arr.append(img_data)
return ret_arr, s_index, False
def get_frames_data(filename, num_frames_per_clip, s_index, add_flow):
''' Given a directory containing extracted frames, return a video clip of
(num_frames_per_clip) consecutive frames as a list of np arrays '''
filename_i = os.path.join(filename, 'i')
rgb_ret_arr, _ , is_end= get_data(filename, num_frames_per_clip, s_index)
if not add_flow:
return rgb_ret_arr, [], s_index, is_end
filename_x = os.path.join(filename, 'x')
flow_x, _, _ = get_data(filename_x, num_frames_per_clip, s_index)
flow_x = np.expand_dims(flow_x, axis=-1)
filename_y = os.path.join(filename, 'y')
flow_y, _, _ = get_data(filename_y, num_frames_per_clip, s_index)
flow_y = np.expand_dims(flow_y, axis=-1)
flow_ret_arr = np.concatenate((flow_x, flow_y), axis=-1)
return rgb_ret_arr, flow_ret_arr, s_index, is_end
def get_frames(filename, s_index, num_frames_per_clip, crop_size, add_flow):
''' Given a directory containing extracted frames, return a video clip of
(num_frames_per_clip) consecutive frames as a list of np arrays '''
rgb_ret_arr, _s_index, _ = get_data(filename, num_frames_per_clip, int(s_index))
rgb_ret_arr = data_process(rgb_ret_arr, crop_size)
if not add_flow:
return rgb_ret_arr, [], _s_index
def data_process(tmp_data, crop_size):
img_datas = []
for j in xrange(len(tmp_data)):
img = Image.fromarray(tmp_data[j].astype(np.uint8))
if img.width > img.height:
scale = float(256) / float(img.height)
img = np.array(cv2.resize(np.array(img), (int(img.width * scale + 1), 256))).astype(np.float32)
else:
scale = float(256) / float(img.width)
img = np.array(cv2.resize(np.array(img), (256, int(img.height * scale + 1)))).astype(np.float32)
crop_x = int((img.shape[0] - crop_size) / 2)
crop_y = int((img.shape[1] - crop_size) / 2)
img = img[crop_x:crop_x + crop_size, crop_y:crop_y + crop_size, :]
img_datas.append(img)
return img_datas
def read(filename, batch_size, s_index, num_frames_per_clip=64, crop_size=224, add_flow=False):
rgb_data = []
flow_data = []
label = []
line = filename.strip('\n').split()
dirname = line[0]
tmp_label = line[1]
print("Loading a video clip from {}...strat_frame = {}".format(dirname, s_index))
tmp_rgb_data, tmp_flow_data, _, is_end = get_frames_data(dirname, num_frames_per_clip, s_index, add_flow)
if len(tmp_rgb_data) != 0:
rgb_img_datas = data_process(tmp_rgb_data, crop_size)
if add_flow:
flow_img_datas = data_process(tmp_flow_data, crop_size)
flow_data.append(flow_img_datas)
rgb_data.append(rgb_img_datas)
label.append(int(tmp_label))
else:
return [], [], [], s_index + num_frames_per_clip, is_end
np_arr_rgb_data = np.array(rgb_data).astype(np.float32)
np_arr_flow_data = np.array(flow_data).astype(np.float32)
np_arr_label = np.array(label).astype(np.int64)
return np_arr_rgb_data, np_arr_flow_data, np_arr_label.reshape(batch_size), s_index+num_frames_per_clip, is_end
def read_clip_and_label(filenames, batch_size, s_index, step, num_frames_per_clip=64, crop_size=224, add_flow=False):
s_index = s_index
video_id = step
rgb_data = []
flow_data = []
labels = []
ids = []
while True:
if len(rgb_data) >= batch_size or video_id == len(filenames):
break
filename = filenames[video_id]
data, flow, label, s_index, is_end = read(filename, 1, s_index, num_frames_per_clip=num_frames_per_clip, crop_size=crop_size, add_flow=add_flow)
if len(data) != 0:
id = video_id
rgb_data.append(data)
flow_data.append(flow)
ids.append(id)
if is_end:
labels.extend(label)
if is_end:
video_id += 1
s_index = 0
# pad (duplicate) data/label if less than batch_size
valid_len = len(rgb_data)
pad_len = batch_size - valid_len
if pad_len:
for i in range(pad_len):
rgb_data.append(data)
flow_data.append(flow)
ids.append(int(id))
np_arr_rgb_data = np.array(rgb_data).astype(np.float32)
np_arr_flow_data = np.array(flow_data).astype(np.float32)
np_arr_label = np.array(labels).astype(np.int64)
np_arr_ids = np.array(ids).astype(np.int64)
return np_arr_rgb_data.reshape(batch_size, num_frames_per_clip, crop_size, crop_size, 3),\
np_arr_flow_data, np_arr_label, \
np_arr_ids.reshape(batch_size), s_index, video_id