import time import cv2 import numpy as np from matplotlib import pyplo

  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
import time
import cv2
import numpy as np
from matplotlib import pyplot as plt
from collections import Counter
import matplotlib
matplotlib.rcParams['savefig.dpi'] = 4 * matplotlib.rcParams['savefig.dpi']
img = cv2.imread('../tessdata/1.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)[245:727+245, 90:1198+90]
import math
def q(x):
return math.sqrt(x*x*x)
def findTextColor(img):
tmp = np.copy(img[100:200,100:200])
tmp[tmp < 100] = 0
contours, _ = cv2.findContours(tmp, cv2.RETR_LIST, cv2.RETR_LIST )
colors = []
for c in contours:
region = tmp[c]
region = region[region > 100]
colors.append(max(region.flatten()))
#colors.append(np.sum(region) / len(region))
return max(colors)
# return np.sum(colors) / len(colors)
print 'text color:',findTextColor(img)
def invertSelectionLine(img):
def detectSelectionLine(img):
for i in range(img.shape[0]):
if np.sum(img[i,:]) / img.shape[1] > 100:
for j in range(i, img.shape[0]):
if np.sum(img[j,:]) / img.shape[0] <= 100:
return i, j
return i, img.shape[0]
firstRow, lastRow = detectSelectionLine(img)
textColor = findTextColor(img)
inv = img[firstRow:lastRow,:].astype('int16')
inv = 255 - inv
globalAvg = np.average(img)
areaAvg = np.average(inv)
inv -= areaAvg - globalAvg
inv[inv<0]=0
img[firstRow:lastRow,:] = inv.astype('uint8')
def normalizeColors(img):
tmp = (img.astype('int16') * 1.2)
maxColor = max(tmp.flatten())
maxColor -= 255
tmp -= maxColor
tmp[tmp<0] = 0
tmp *= 3
tmp[tmp>255] = 255
return img
def contrast(img):
THRESHOLD = 80
for i in range(img.shape[0]):
for j in range(img.shape[1]):
if img[i,j] > THRESHOLD:
img[i,j] += min(255 - img[i,j], q(img[i,j]-THRESHOLD))
else:
img[i,j] -= min(img[i,j], q(THRESHOLD - img[i,j]))
def contrast(img, mul=2, shift=0):
tmp = np.copy(img).astype(float) * mul
avg = np.sum(tmp.flatten()) / len(tmp.flatten()) + shift
tmp -= avg
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
return tmp.astype('uint8')
def detectVerticalLines(img):
for col in range(2, img.shape[1]):
diff = np.sum(abs(img[:,col] - img[:,col-1])) / img.shape[0]
if diff > 200:
for col2 in range(col+1, img.shape[1]):
diff2 = np.sum(abs(img[:,col2] - img[:,col2-1])) / img.shape[0]
if diff2 > 200:
width = col2 - col
hw = width / 2
if hw == 0: hw = 1
mid = (col2 + col) / 2
img[:,col:mid] = img[:,col-hw-1:col-1]
img[:,(col+col2)/2:col2] = img[:,col2+1:col2+1+hw]
return
#for i in range(3):
# detectVerticalLines(img)
def removeVerticalLines(img):
minrows = [r[1] for r in sorted([(np.sum(img[row,:]),row) for row in range(img.shape[0])])[:50]]
cols = []
for col in range(100,img.shape[1]):
avg = np.sum(img[minrows,col]) / len(minrows)
cols.append(avg)
freq = np.fft.fft(cols)
freq[100:] = 0
diff = abs(cols - np.fft.ifft(freq))
maxDiff = max(diff)
for i in range(len(diff)):
if diff[i] > maxDiff *0.20:
img[:,i+100] = 0#np.sum(img.flatten()) / len(img.flatten())
def removeHorizontalLines(img):
mincols = [r[1] for r in sorted([(np.sum(img[:,col]),col) for col in range(img.shape[1])])[:50]]
rows = []
for row in range(100,img.shape[0]):
avg = np.sum(img[row, mincols]) / len(mincols)
rows.append(avg)
freq = np.fft.fft(rows)
freq[100:] = 0
diff = abs(rows - np.fft.ifft(freq))
maxDiff = max(diff)
for i in range(len(diff)):
if diff[i] > maxDiff *0.20:
img[i+100,:] = 0#np.sum(img.flatten()) / len(img.flatten())
#img = contrast(img, 2, 40)
#img = contrast(img, 1.5, 20)
#img = contrast(img, 1.5, 10)
#img[img<10] = 0
#img[img>0] = 255
def fftRows(img):
for i in range(img.shape[0]):
row = img[i]
dark = row[row < 100]
fimg = np.fft.fft(dark)
fimg[0] = 0
row[row < 100] = abs(np.fft.ifft(fimg))
def cleanBackgroundFFT(img):
print 'fft start'
start = time.time()
dark = img[img < 100]
chunkSize = img.shape[1] * 10
chunkCount = int(math.ceil(float(len(dark))/chunkSize)*chunkSize)
for chunk in range(0, chunkCount, chunkSize):
fimg = np.fft.fft(dark[chunk:chunk+chunkSize])
fimg[:10] = 0
dark[chunk:chunk+chunkSize] = abs(np.fft.ifft(fimg))
img[img<100] = dark
end = time.time()
print 'fft end'
print 'fft took', end-start
#img[row,:] = abs(np.fft.ifft(fimg))
#fimg = np.fft.fft2(img)
#fimg[0] /= 1000
#fimg[1] /= 1000
#img = abs(np.fft.ifft2(fimg))
invertSelectionLine(img)
removeHorizontalLines(img)
cleanBackgroundFFT(img)
removeVerticalLines(img)
img = contrast(img, 3, 20)
img = contrast(img, 1.5, 10)
cv2.imwrite('q.tiff', img)
#plt.imshow(img, 'gray')