-
Notifications
You must be signed in to change notification settings - Fork 1
/
debug_filter.py
167 lines (150 loc) · 5.88 KB
/
debug_filter.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
# -*- coding: utf-8 -*-
from keras.utils.np_utils import to_categorical
import Global_Params
import algorithm
import filter
from load_data import docuChessInfo
from load_data import norm_size
from load_data import str2int
from load_data import CHESS_TABLE
import CNN_train
import CNN_train_w
import numpy as np
import tensorflow as tf
import cv2
import os
from keras.preprocessing.image import img_to_array
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Activation
from keras.layers import Dense, Dropout, Flatten
from keras.preprocessing.image import ImageDataGenerator
# from imutils import paths
import sys
sys.dont_write_bytecode = True
def RgbMedianFilter(img_set, size):
"""
对RGB图片集做中值滤波（暂时只考虑了奇数大小的窗口，如3x3、5x5）
(中值滤波好像作用不大)
:param img_set: 图片集, shape = [num, height, width, 3]
:param size: 中值滤波窗口大小(size, size)
:return: 中值滤波后的图片集
"""
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
filted_img_set = np.zeros((num, height, width, 3))
edge = (int)(size/2)
idx = 0
for img in img_set:
for i in range(height):
for j in range(width):
if i < edge or i > height-edge-1 or j < edge or j > width-edge-1:
filted_img_set[idx, i, j, :] = img[i, j, :]
else:
for color in range(3):
filted_img_set[idx, i, j, color] = np.median(img[i-edge:i+edge, j-edge:j+edge, color])
cv2.imshow("img", img/255.0)
cv2.imshow("filted_img", filted_img_set[idx]/255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
idx += 1
return filted_img_set
def RedBlackBoost(img_set, IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY):
'''
增强图片中的红黑色（红色棋子增强红色，黑色棋子增强黑色）
:param img_set: 图片集, shape = [num, height, width, 3]
:return: 处理后的图片集
'''
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
boosted_img_set = np.zeros((num, height, width, 3))
hsv_img = np.zeros((height, width, 3))
threshold = 0.02
idx = 0
count = 0
radius = Global_Params.M_norm_size/2
mid =Global_Params.M_norm_size/2.0
mid = int(mid)
for img in img_set:
count += 1
red_cnt = 0
red_flag = False
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
for i in range(height):
for j in range(width):
if ((0 <= hsv_img[i, j, 0] <= 10) or (310 <= hsv_img[i, j, 0] <= 360)) and (70 <= hsv_img[i, j, 1]*255 <= 255) and (50 <= hsv_img[i, j, 2] <= 255):
red_cnt += 1
if red_cnt >= threshold * height * width:
red_flag = True
break
if red_flag is True:
break
if red_flag is True: # 红棋子红色增强
# print("RedBlackBoost \t\t<", count, "> \t\tred")
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if ((0 <= hsv_img[i, j, 0] <= 17) or (345 <= hsv_img[i, j, 0] <= 360)) and \
(66.0 <= hsv_img[i, j, 1]*255 <= 255) and (33.0 <= hsv_img[i, j, 2] <= 255):
boosted_img_set[idx, i, j, :] = [0, 0, 255]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
else: # 黑棋子黑色增强
# print("RedBlackBoost \t\t<", count, "> \t\tblack")
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if (0 <= hsv_img[i, j, 0] <= 360) and \
(0 <= hsv_img[i, j, 1]*255 <= 255) and (0 <= hsv_img[i, j, 2] <= 70):
boosted_img_set[idx, i, j, :] = [0, 0, 0]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
print("RedBlackBoost Processing...")
print("hsv_img[mid, mid, :] -> ", hsv_img[mid, mid, :])
cv2.imshow("img_" + str(idx), img/255.0)
cv2.imshow("boosted_img_" + str(idx), boosted_img_set[idx]/255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
idx += 1
del img_set
return boosted_img_set
def load_data(image_paths, norm_size):
data = []
label = []
test_data = []
test_label = []
classes = Global_Params.M_num_classes
count_image = 0
for each_image in image_paths:
count_image += 1
print(each_image)
image = cv2.imread(each_image)
image = cv2.resize(image, (norm_size, norm_size))
image = img_to_array(image)
data.append(image)
maker = str2int(each_image.split(os.path.sep)[-2])
label.append(maker)
if count_image % 10 == 0:
test_data.append(image)
test_label.append(maker)
data = np.array(data)
print("data shape = ", data.shape, " ===============================")
data = RedBlackBoost(data, True, 30)
data = data/255.0
label = np.array(label)
label = to_categorical(label, num_classes=classes)
print("Data loaded.")
return data, label, test_data, test_label
def main(): # 主函数
pathChessChoose = Global_Params.M_data_per_360_path
all_chess_data_path = docuChessInfo(pathChessChoose)
data, label, test_data, test_label = load_data(all_chess_data_path, norm_size)
# 调用函数
if __name__ == '__main__':
main()