-
Notifications
You must be signed in to change notification settings - Fork 13
/
Data_Reader.py
191 lines (172 loc) · 9.9 KB
/
Data_Reader.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
import numpy as np
import os
import scipy.misc as misc
import random
import cv2
# ------------------------Class for reading training and validation data---------------------------------------------------------------------
class Data_Reader:
################################Initiate folders were files are and list of train images############################################################################
def __init__(self, ImageDir, GTLabelDir="", BatchSize=1, Suffle=True):
# ImageDir directory were images are
# GTLabelDir Folder wehere ground truth Labels map are save in png format (same name as corresponnding image in images folder)
self.NumFiles = 0 # Number of files in reader
self.Epoch = 0 # Training epochs passed
self.itr = 0 # Iteration
# Image directory
self.Image_Dir = ImageDir # Image Dir
if GTLabelDir == "": # If no label dir use
self.ReadLabels = False
else:
self.ReadLabels = True
self.Label_Dir = GTLabelDir # Folder with ground truth pixels was annotated (optional for training only)
self.OrderedFiles = []
# Read list of all files
self.OrderedFiles += [each for each in os.listdir(self.Image_Dir) if
each.endswith('.PNG') or each.endswith('.JPG') or each.endswith('.TIF') or each.endswith(
'.GIF') or each.endswith('.png') or each.endswith('.jpg') or each.endswith(
'.tif') or each.endswith('.gif')] # Get list of training images
self.BatchSize = BatchSize # Number of images used in single training operation
self.NumFiles = len(self.OrderedFiles)
self.OrderedFiles.sort() # Sort files by names
self.SuffleBatch() # suffle file list
####################################### Suffle list of files in group that fit the batch size this is important since we want the batch to contain images of the same size##########################################################################################
def SuffleBatch(self):
self.SFiles = []
Sf = np.array(range(np.int32(np.ceil(self.NumFiles / self.BatchSize) + 1))) * self.BatchSize
random.shuffle(Sf)
self.SFiles = []
for i in range(len(Sf)):
for k in range(self.BatchSize):
if Sf[i] + k < self.NumFiles:
self.SFiles.append(self.OrderedFiles[Sf[i] + k])
###########################Read and augment next batch of images and labels#####################################################################################
def ReadAndAugmentNextBatch(self):
if self.itr >= self.NumFiles: # End of an epoch
self.itr = 0
self.SuffleBatch()
self.Epoch += 1
batch_size = np.min([self.BatchSize, self.NumFiles - self.itr])
Sy = Sx = 0
XF = YF = 1
Cry = 1
Crx = 1
# --------------Resize Factor--------------------------------------------------------
if np.random.rand() < 1:
YF = XF = 0.3 + np.random.rand() * 0.7
# ------------Stretch image-------------------------------------------------------------------
if np.random.rand() < 0.8:
if np.random.rand() < 0.5:
XF *= 0.5 + np.random.rand() * 0.5
else:
YF *= 0.5 + np.random.rand() * 0.5
# -----------Crop Image------------------------------------------------------
if np.random.rand() < 0.0:
Cry = 0.7 + np.random.rand() * 0.3
Crx = 0.7 + np.random.rand() * 0.3
# -----------Augument Images and labeles-------------------------------------------------------------------
for f in range(batch_size):
# .............Read image and labels from files.........................................................
Img = misc.imread(self.Image_Dir + "/" + self.SFiles[self.itr])
Img = Img[:, :, 0:3]
LabelName = self.SFiles[self.itr][0:-4] + ".png" # Assume Label name is same as image only with png ending
if self.ReadLabels:
Label = misc.imread(self.Label_Dir + "/" + LabelName)
self.itr += 1
# ............Set Batch image size according to first image in the batch...................................................
if f == 0:
Sy, Sx, d = Img.shape
Sy, Sx
Sy *= YF
Sx *= XF
Cry *= Sy
Crx *= Sx
Sy = np.int32(Sy)
Sx = np.int32(Sx)
Cry = np.int32(Cry)
Crx = np.int32(Crx)
Images = np.zeros([batch_size, Cry, Crx, 3], dtype=np.float)
if self.ReadLabels: Labels = np.zeros([batch_size, Cry, Crx, 1], dtype=np.int)
# ..........Resize and strecth image and labels....................................................................
Img = misc.imresize(Img, [Sy, Sx], interp='bilinear')
if self.ReadLabels: Label = misc.imresize(Label, [Sy, Sx], interp='nearest')
# -------------------------------Crop Image.......................................................................
MinOccupancy = 501
if not (Cry == Sy and Crx == Sx):
for u in range(501):
MinOccupancy -= 1
Xi = np.int32(np.floor(np.random.rand() * (Sx - Crx)))
Yi = np.int32(np.floor(np.random.rand() * (Sy - Cry)))
if np.sum(Label[Yi:Yi + Cry, Xi:Xi + Crx] > 0) > MinOccupancy:
Img = Img[Yi:Yi + Cry, Xi:Xi + Crx, :]
if self.ReadLabels: Label = Label[Yi:Yi + Cry, Xi:Xi + Crx]
break
# ------------------------Mirror Image-------------------------------# --------------------------------------------
if random.random() < 0.5: # Agument the image by mirror image
Img = np.fliplr(Img)
if self.ReadLabels:
Label = np.fliplr(Label)
# -----------------------Agument color of Image-----------------------------------------------------------------------
Img = np.float32(Img)
if np.random.rand() < 0.8: # Play with shade
Img *= 0.4 + np.random.rand() * 0.6
if np.random.rand() < 0.4: # Turn to grey
Img[:, :, 2] = Img[:, :, 1] = Img[:, :, 0] = Img[:, :, 0] = Img.mean(axis=2)
if np.random.rand() < 0.0: # Play with color
if np.random.rand() < 0.6:
for i in range(3):
Img[:, :, i] *= 0.1 + np.random.rand()
if np.random.rand() < 0.2: # Add Noise
Img *= np.ones(Img.shape) * 0.95 + np.random.rand(Img.shape[0], Img.shape[1], Img.shape[2]) * 0.1
Img[Img > 255] = 255
Img[Img < 0] = 0
# ----------------------Add images and labels to to the batch----------------------------------------------------------
Images[f] = Img
if self.ReadLabels:
# Label=Label[0] #(464, 212)
# print(Label.shape) #(425, 421, 3)
Label = Label[:, :, 0]
# print(Label.shape)
# Label=Label*50
# cv2.imshow('Label', Label)
# cv2.waitKey()
# exit()
Labels[f, :, :, 0] = Label
# .......................Return aumented images and labels...........................................................
if self.ReadLabels:
return Images, Labels # return image and pixelwise labels
else:
return Images # Return image
######################################Read next batch of images and labels with no augmentation######################################################################################################
def ReadNextBatchClean(self): # Read image and labels without agumenting
if self.itr >= self.NumFiles: # End of an epoch
self.itr = 0
# self.SuffleBatch()
self.Epoch += 1
batch_size = np.min([self.BatchSize, self.NumFiles - self.itr])
for f in range(batch_size):
##.............Read image and labels from files.........................................................
Img = misc.imread(self.Image_Dir + "/" + self.OrderedFiles[self.itr])
Img = Img[:, :, 0:3]
LabelName = self.OrderedFiles[self.itr][
0:-4] + ".png" # Assume label name is same as image only with png ending
if self.ReadLabels:
Label = misc.imread(self.Label_Dir + "/" + LabelName)
self.itr += 1
# ............Set Batch size according to first image...................................................
if f == 0:
Sy, Sx, Depth = Img.shape
Images = np.zeros([batch_size, Sy, Sx, 3], dtype=np.float)
if self.ReadLabels: Labels = np.zeros([batch_size, Sy, Sx, 1], dtype=np.int)
# ..........Resize image and labels....................................................................
Img = misc.imresize(Img, [Sy, Sx], interp='bilinear')
if self.ReadLabels: Label = misc.imresize(Label, [Sy, Sx], interp='nearest')
# ...................Load image and label to batch..................................................................
Images[f] = Img
if self.ReadLabels:
Label = Label[:, :, 0]
Labels[f, :, :, 0] = Label
# ...................................Return images and labels........................................
if self.ReadLabels:
return Images, Labels # return image and and pixelwise labels
else:
return Images # Return image