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input_data.py
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input_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 downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
import PIL.Image as Image
import random
import numpy as np
import cv2
import time
def get_frames_data(filename, num_frames_per_clip=16,temporal_elastic_deformation=False,random_dropping=False,random_rotate_range=0,random_scale_range=0,random_shift_range=(0,0)):
'''
Given a directory containing extracted frames, return a video clip of
(num_frames_per_clip) consecutive frames as a list of np arrays
filename: path like VIVA_Gesture_Root/03_01_01/
num_frames_per_clip: num of frames
temporal_elastic_deformation: if true, temporal elastic deformation will be performed.
random_dropping: if true, random pixels' value will be set to zero.
random_rotate_range: rotate angle will be in range(-random_rotate_range,random_rotate_range).
random_scale_range: scale will be in range(1-random_scale_range,1+random_scale_range)
random_shift_range: x in (-random_shift_range[0],random_shift_range[0]), y in (-random_shift_range[1],random_shift_range[1])
return (list of frames,indexes of extracted frames), frame is opened with PIL.Image
'''
ret_arr = []
s_index = 0
for parent, dirnames, filenames in os.walk(filename):
if(len(filenames)<num_frames_per_clip):
return [], s_index
filenames = sorted(filenames)
if temporal_elastic_deformation==True:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
clf = Pipeline([('poly', PolynomialFeatures(degree=2)),
('linear', LinearRegression(fit_intercept=False))])
originLength = len(filenames)
M = originLength
n = random.normalvariate(mu=M,sigma=3)
m = random.normalvariate(mu=n,sigma=4*(1-abs(n-M)/M))
x = np.array([0,n,M-1])
y = np.array([0,m,M-1])
clf.fit(x[:, np.newaxis], y)
x_test = np.arange(0,len(filenames),1)
y_test = clf.predict(x_test[:, np.newaxis])
y_test = [int(e) for e in y_test]
ss_index = []
for i in range(num_frames_per_clip):
index = int(1.0*i*originLength/num_frames_per_clip)
if index>=originLength:
index = originLength-1
value=y_test[index]
if value>=originLength:
value = originLength-1
if value<0:
value = 0
ss_index.append(value)
s_index = ss_index
#import matplotlib.pyplot as plt
#plt.plot(x_test,y_test,linewidth=2)
#plt.show()
else:
#start_index = random.randint(0, len(filenames) - num_frames_per_clip+1)
#s_index = [start_index+i for i in range(num_frames_per_clip)]
# average index
s_index = []
originLength = len(filenames)
for i in range(1,num_frames_per_clip+1,1):
index = int(i/num_frames_per_clip*originLength+0.5)
if index>originLength-1:
index=originLength-1
s_index.append(index)
rotate_angle = 0
scale = 1
shift_x = 0
shift_y = 0
shift_matrix = np.float32([[1,0,0],[0,1,0]])
rotate_matrix = np.float32([[1,0,0],[0,1,0]])
for i in range(num_frames_per_clip):
index = s_index[i]
image_name = str(filename) + '/' + str(filenames[index])
img = Image.open(image_name)
img_data = np.array(img)
(h, w) = img_data.shape[:2]
center = (w / 2, h / 2)
if i==0:
# construct affine matrix
if random_rotate_range>0:
rotate_angle = np.random.uniform(-random_rotate_range,random_rotate_range)
if random_scale_range>0:
scale = np.random.uniform(1-random_scale_range,1+random_scale_range)
if random_shift_range[0]>0:
shift_x = np.random.uniform(-random_shift_range[0],random_shift_range[0])
if random_shift_range[1]>0:
shift_y = np.random.uniform(-random_shift_range[1],random_shift_range[1])
shift_matrix = np.float32([[1,0,shift_x],[0,1,shift_y]])
rotate_matrix = cv2.getRotationMatrix2D(center, rotate_angle, scale)
# shift and rotate
shifted = cv2.warpAffine(img_data,shift_matrix,(w,h))
img_data = cv2.warpAffine(shifted, rotate_matrix, (w, h))
# random_dropping
if random_dropping == True:
random_dropping_arr = np.random.rand(img_data.shape[0],img_data.shape[1],img_data.shape[2])
img_data[random_dropping_arr<0.3]=0
ret_arr.append(img_data)
return ret_arr, s_index
def read_clip_and_label(rootdir,filename,batch_size, lines=None,start_pos=-1, num_frames_per_clip=16, crop_size=(112,112), shuffle=False,phase='TRAIN'):
'''
rootdir: dataset root
filename: label file path
batch_size: int
lines: [] if first call this function, the fixed list which will be used along one epoch.
start_pos: read start pos index
num_frames_per_clip: num of frames to generate
crop_size: img size
shuffle: if in the beginning of one epoch, shuffle=False
phase: 'TRAIN' to turn on data online augmentation, 'TEST' to turn off data online augmentation
return: data(nparr),label(nparr),next_batch_start,read_dirnames,valid_len of data, lines(sequences keeps the same along one epoch)
'''
if lines==None:
lines = open(filename,'r')
lines = list(lines)
read_dirnames = []
data = []
label = []
batch_index = 0
next_batch_start = -1
#np_mean = np.load('crop_mean.npy').reshape([num_frames_per_clip, crop_size[0], crop_size[1], 3])
np_mean = np.ones([num_frames_per_clip,crop_size[0],crop_size[1],3])*128
# Forcing shuffle, if start_pos is not specified
if start_pos < 0:
shuffle = True
start_pos=0
if shuffle:
video_indices = list(range(len(lines)))
random.seed(time.time())
random.shuffle(lines)
else:
# Process videos sequentially
video_indices = range(start_pos, len(lines))
for i in range(len(video_indices)+1):
if i >= len(video_indices):
next_batch_start = -1
break
index = video_indices[i]
if(batch_index>=batch_size):
next_batch_start = index
break
line = lines[index].strip('\n').split()
dirname = line[0]
if os.path.exists(rootdir) == True:
dirname = os.path.join(rootdir,dirname)
tmp_label = line[2] # to serve C3D caffe project
#if not shuffle:
# print("Loading a video clip from {}...".format(dirname))
if phase == 'TRAIN':
tmp_data, _ = get_frames_data(dirname, num_frames_per_clip,
temporal_elastic_deformation=False,
random_dropping=True,
random_rotate_range=10,
random_scale_range=0.3) # default open temporal elastic deformation
elif phase == 'TEST':
tmp_data, _ = get_frames_data(dirname, num_frames_per_clip,temporal_elastic_deformation=False,random_dropping=False)
img_datas = [];
if(len(tmp_data)!=0):
for j in range(len(tmp_data)):
img = tmp_data[j].astype(np.uint8)
if img.shape[0]!=crop_size[0] or img.shape[1]!=crop_size[1]:
img = np.array(cv2.resize(img,(crop_size[1],crop_size[0]))).astype(np.float32)
else:
img = np.array(img).astype(np.float32)
img-=128
img/=128.0
img_datas.append(img)
data.append(img_datas)
label.append(int(tmp_label))
batch_index = batch_index + 1
read_dirnames.append(dirname)
# pad (duplicate) data/label if less than batch_size
valid_len = len(data)
pad_len = batch_size - valid_len
if pad_len:
for i in range(pad_len):
data.append(img_datas)
label.append(int(tmp_label))
np_arr_data = np.array(data).astype(np.float32)
np_arr_label = np.array(label).astype(np.int64)
#print('next_batch_start:%d'%next_batch_start)
return np_arr_data, np_arr_label, next_batch_start, read_dirnames, valid_len,lines
if __name__ == '__main__':
tmp_data, _ = get_frames_data(r'E:\dataset\VIVA_avi_group\VIVA_avi_part0\train\03_01_01', 16,temporal_elastic_deformation=True)
for e in tmp_data:
import cv2
cv2.imshow('t',e)
cv2.waitKey(0)
pass