-
Notifications
You must be signed in to change notification settings - Fork 0
/
crop_for_columns.py
executable file
·485 lines (414 loc) · 19.3 KB
/
crop_for_columns.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
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
#! /usr/bin/env python
import argparse
def process_image(args):
import os
from scipy.ndimage.filters import rank_filter
import numpy as np
from PIL import Image, ImageEnhance, ImageFilter, ImageDraw
import matplotlib.pyplot as plt
import cv2
path = args.input
out_path = args.output
def deskew(im, save_directory, direct, max_skew=10):
if direct == "Y":
height, width = im.shape[:2]
print(height)
print(width)
# Create a grayscale image and denoise it
if channels != 0:
im_gs = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_gs = cv2.fastNlMeansDenoising(im_gs, h=3)
else:
im_gs = cv2.fastNlMeansDenoising(im, h=3)
# print("De-noise ok.")
# Create an inverted B&W copy using Otsu (automatic) thresholding
im_bw = cv2.threshold(im_gs, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# print("Otsu ok.")
# Detect lines in this image. Parameters here mostly arrived at by trial and error.
# If the initial threshold is too high, then settle for a lower threshold value
try:
lines = cv2.HoughLinesP(im_bw, 1, np.pi / 180, 200, minLineLength=width / 12, maxLineGap=width / 150)
# Collect the angles of these lines (in radians)
angles = []
for line in lines:
x1, y1, x2, y2 = line[0]
geom = np.arctan2(y2 - y1, x2 - x1)
print(np.rad2deg(geom))
angles.append(geom)
except:
lines = cv2.HoughLinesP(im_bw, 1, np.pi / 180, 150, minLineLength=width / 12, maxLineGap=width / 150)
# Collect the angles of these lines (in radians)
angles = []
for line in lines:
x1, y1, x2, y2 = line[0]
geom = np.arctan2(y2 - y1, x2 - x1)
print(np.rad2deg(geom))
angles.append(geom)
angles = [angle for angle in angles if abs(angle) < np.deg2rad(max_skew)]
if len(angles) < 5:
# Insufficient data to deskew
print("Insufficient data to deskew. Cropped image might already be straight. Cropped image saved.")
cv2.imwrite(img=im,
filename=save_directory + cropped_jpeg_list[pg_count])
#im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
#im_pil = Image.fromarray(im)
#im_pil.save(save_directory + cropped_jpeg_list[pg_count])
print("Cropped image saved.")
return im
else:
# Average the angles to a degree offset
angle_deg = np.rad2deg(np.median(angles))
# Rotate the image by the residual offset
M = cv2.getRotationMatrix2D((width / 2, height / 2), angle_deg, 1)
im = cv2.warpAffine(im, M, (width, height), borderMode=cv2.BORDER_REPLICATE)
# Plot if a full run
# Always save deskewed image
if args.type == "full":
plt.subplot(111),plt.imshow(im)
plt.title('Deskewed Image'), plt.xticks([]), plt.yticks([])
plt.show()
cropped_jpeg = cropped_jpeg_list[pg_count]
cv2.imwrite(img = im,
filename = save_directory + cropped_jpeg[:-5] + "_rotated.jpeg")
print("Only de-skewed cropped image saved.")
return im
else:
height, width = im.shape[:2]
print(height)
print(width)
# Create a grayscale image and denoise it
im_gs = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_gs = cv2.fastNlMeansDenoising(im_gs, h=3)
# Create an inverted B&W copy using Otsu (automatic) thresholding
im_bw = cv2.threshold(im_gs, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# Detect lines in this image. Parameters here mostly arrived at by trial and error.
# If the initial threshold is too high, then settle for a lower threshold value
try:
lines = cv2.HoughLinesP(im_bw, 1, np.pi / 180, 200, minLineLength=width / 12, maxLineGap=width / 150)
# Collect the angles of these lines (in radians)
angles = []
for line in lines:
x1, y1, x2, y2 = line[0]
geom = np.arctan2(y2 - y1, x2 - x1)
print(np.rad2deg(geom))
angles.append(geom)
except TypeError:
lines = cv2.HoughLinesP(im_bw, 1, np.pi / 180, 150, minLineLength=width / 12, maxLineGap=width / 150)
# Collect the angles of these lines (in radians)
angles = []
for line in lines:
x1, y1, x2, y2 = line[0]
geom = np.arctan2(y2 - y1, x2 - x1)
print(np.rad2deg(geom))
angles.append(geom)
except:
print ("TypeError encountered with HoughLines. Check cropped image output. Only cropped image saved.")
return
angles = [angle for angle in angles if abs(angle) < np.deg2rad(max_skew)]
if len(angles) < 5:
# Insufficient data to deskew
print("Insufficient data to deskew. Cropped image might already be straight.")
return im
else:
# Average the angles to a degree offset
angle_deg = np.rad2deg(np.median(angles))
# Rotate the image by the residual offset
M = cv2.getRotationMatrix2D((width / 2, height / 2), angle_deg, 1)
im = cv2.warpAffine(im, M, (width, height), borderMode=cv2.BORDER_REPLICATE)
# Plot if a full run
# Always save deskewed image
if args.type == "full":
plt.subplot(111), plt.imshow(im)
plt.title('Deskewed Image'), plt.xticks([]), plt.yticks([])
plt.show()
cropped_jpeg = cropped_jpeg_list[pg_count]
cv2.imwrite(img=im,
filename=save_directory + cropped_jpeg[:-5] + "_rotated.jpeg")
print("Rotated cropped image saved")
return im
def dilate(ary, N, iterations):
"""Dilate using an NxN '+' sign shape. ary is np.uint8."""
kernel = np.zeros((N,N), dtype=np.uint8)
kernel[(N-1)//2,:] = 1
dilated_image = cv2.dilate(ary / 255, kernel, iterations=iterations)
kernel = np.zeros((N,N), dtype=np.uint8)
kernel[:,(N-1)//2] = 1
dilated_image = cv2.dilate(dilated_image, kernel, iterations=iterations)
if args.type == "full":
plt.subplot(111),plt.imshow(dilated_image,cmap = 'gray')
plt.title('Dilated Image'), plt.xticks([]), plt.yticks([])
plt.show()
return dilated_image
def find_components(edges, max_components=16):
"""Dilate the image until there are just a few connected components.
Returns contours for these components."""
# Perform increasingly aggressive dilation until there are just a few
# connected components.
count = 410
dilation = 5
n = 1
while count > 400:
n += 1
dilated_image = dilate(edges, N=3, iterations=n)
# print(dilated_image.dtype)
dilated_image = cv2.convertScaleAbs(dilated_image)
# print(dilated_image.dtype)
contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
count = len(contours)
print(count)
#print dilation
#Image.fromarray(edges).show()
#Image.fromarray(255 * dilated_image).show()
return contours
def props_for_contours(contours, ary):
"""Calculate bounding box & the number of set pixels for each contour."""
c_info = []
for c in contours:
x,y,w,h = cv2.boundingRect(c)
c_im = np.zeros(ary.shape)
cv2.drawContours(c_im, [c], 0, 255, -1)
c_info.append({
'x1': x,
'y1': y,
'x2': x + w - 1,
'y2': y + h - 1,
'sum': np.sum(ary * (c_im > 0))/255
})
return c_info
def union_crops(crop1, crop2):
"""Union two (x1, y1, x2, y2) rects."""
x11, y11, x21, y21 = crop1
x12, y12, x22, y22 = crop2
return min(x11, x12), min(y11, y12), max(x21, x22), max(y21, y22)
def intersect_crops(crop1, crop2):
x11, y11, x21, y21 = crop1
x12, y12, x22, y22 = crop2
return max(x11, x12), max(y11, y12), min(x21, x22), min(y21, y22)
def crop_area(crop):
x1, y1, x2, y2 = crop
return max(0, x2 - x1) * max(0, y2 - y1)
def find_border_components(contours, ary):
borders = []
area = ary.shape[0] * ary.shape[1]
for i, c in enumerate(contours):
x,y,w,h = cv2.boundingRect(c)
if w * h > 0.5 * area:
borders.append((i, x, y, x + w - 1, y + h - 1))
return borders
def angle_from_right(deg):
return min(deg % 90, 90 - (deg % 90))
def remove_border(contour, ary):
"""Remove everything outside a border contour."""
# Use a rotated rectangle (should be a good approximation of a border).
# If it's far from a right angle, it's probably two sides of a border and
# we should use the bounding box instead.
c_im = np.zeros(ary.shape)
r = cv2.minAreaRect(contour)
degs = r[2]
if angle_from_right(degs) <= 10.0:
box = cv2.cv.BoxPoints(r)
box = np.int0(box)
cv2.drawContours(c_im, [box], 0, 255, -1)
cv2.drawContours(c_im, [box], 0, 0, 4)
else:
x1, y1, x2, y2 = cv2.boundingRect(contour)
cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1)
cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4)
return np.minimum(c_im, ary)
def find_optimal_components_subset(contours, edges):
"""Find a crop which strikes a good balance of coverage/compactness.
Returns an (x1, y1, x2, y2) tuple.
"""
c_info = props_for_contours(contours, edges)
c_info.sort(key=lambda x: -x['sum'])
total = np.sum(edges) / 255
area = edges.shape[0] * edges.shape[1]
c = c_info[0]
del c_info[0]
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
crop = this_crop
covered_sum = c['sum']
while covered_sum < total:
changed = False
recall = 1.0 * covered_sum / total
prec = 1 - 1.0 * crop_area(crop) / area
f1 = 2 * (prec * recall / (prec + recall))
#print '----'
for i, c in enumerate(c_info):
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
new_crop = union_crops(crop, this_crop)
new_sum = covered_sum + c['sum']
new_recall = 1.0 * new_sum / total
new_prec = 1 - 1.0 * crop_area(new_crop) / area
new_f1 = 2 * new_prec * new_recall / (new_prec + new_recall)
# Add this crop if it improves f1 score,
# _or_ it adds 25% of the remaining pixels for <15% crop expansion.
# ^^^ very ad-hoc! make this smoother
remaining_frac = c['sum'] / (total - covered_sum)
new_area_frac = 1.0 * crop_area(new_crop) / crop_area(crop) - 1
if new_f1 > f1 or (remaining_frac > 0.25 and new_area_frac < 0.15):
print ('%d %s -> %s / %s (%s), %s -> %s / %s (%s), %s -> %s' % (
i, covered_sum, new_sum, total, remaining_frac,
crop_area(crop), crop_area(new_crop), area, new_area_frac,
f1, new_f1))
crop = new_crop
covered_sum = new_sum
del c_info[i]
changed = True
break
if not changed:
break
return crop
def pad_crop(crop, contours, edges, border_contour, pad_px=15):
"""Slightly expand the crop to get full contours.
This will expand to include any contours it currently intersects, but will
not expand past a border.
"""
bx1, by1, bx2, by2 = 0, 0, edges.shape[0], edges.shape[1]
if border_contour is not None and len(border_contour) > 0:
c = props_for_contours([border_contour], edges)[0]
bx1, by1, bx2, by2 = c['x1'] + 5, c['y1'] + 5, c['x2'] - 5, c['y2'] - 5
def crop_in_border(crop):
x1, y1, x2, y2 = crop
x1 = max(x1 - pad_px, bx1)
y1 = max(y1 - pad_px, by1)
x2 = min(x2 + pad_px, bx2)
y2 = min(y2 + pad_px, by2)
return crop
crop = crop_in_border(crop)
c_info = props_for_contours(contours, edges)
changed = False
for c in c_info:
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
this_area = crop_area(this_crop)
int_area = crop_area(intersect_crops(crop, this_crop))
new_crop = crop_in_border(union_crops(crop, this_crop))
if 0 < int_area < this_area and crop != new_crop:
print ('%s -> %s' % (str(crop), str(new_crop)))
changed = True
crop = new_crop
if changed:
return pad_crop(crop, contours, edges, border_contour, pad_px)
else:
return crop
def downscale_image(im, max_dim=2048):
"""Shrink im until its longest dimension is <= max_dim.
Returns new_image, scale (where scale <= 1).
"""
a, b = im.size
if max(a, b) <= max_dim:
return 1.0, im
scale = 1.0 * max_dim / max(a, b)
new_im = im.resize((int(a * scale), int(b * scale)), Image.ANTIALIAS)
return scale, new_im
# Creates an empty list that takes on the filename of each jpeg in the directory
# Then, it will loop through every single one of them
uncropped_jpeg_list = []
cropped_jpeg_list = []
for file in os.listdir(path):
uncropped_jpeg_temp = ""
cropped_jpeg_temp = ""
if file.endswith('.jpeg'):
uncropped_jpeg_temp = "/" + file
# print (uncropped_jpeg)
cropped_jpeg_temp = uncropped_jpeg_temp[:-5] + "_cropped.jpeg"
uncropped_jpeg_list.append(uncropped_jpeg_temp)
cropped_jpeg_list.append(cropped_jpeg_temp)
# print(cropped_jpeg)
pg_count = 0
for uncropped_jpeg in uncropped_jpeg_list:
orig_im = Image.open(path + uncropped_jpeg)
scale, im = downscale_image(orig_im)
# Apply dilation and erosion to remove some noise
kernel = np.ones((1, 1), np.uint8)
img = cv2.dilate(np.asarray(im), kernel, iterations=1)
img = cv2.erode(img, kernel, iterations=1)
# Detect edge and plot
edges = cv2.Canny(img, 100, 400)
if args.type == "full":
plt.subplot(111),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()
# TODO: dilate image _before_ finding a border. This is crazy sensitive!
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
borders = find_border_components(contours, edges)
borders.sort(key=lambda i, x1, y1, x2, y2: (x2 - x1) * (y2 - y1))
border_contour = None
if len(borders):
border_contour = contours[borders[0][0]]
edges = remove_border(border_contour, edges)
edges = 255 * (edges > 0).astype(np.uint8)
# Remove ~1px borders using a rank filter.
maxed_rows = rank_filter(edges, -4, size=(1, 20))
maxed_cols = rank_filter(edges, -4, size=(20, 1))
debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols)
edges = debordered
contours = find_components(edges)
if len(contours) == 0:
# print '%s -> (no text!)' % path
return
crop = find_optimal_components_subset(contours, edges)
crop = pad_crop(crop, contours, edges, border_contour)
crop = [int(x / scale) for x in crop] # upscale to the original image size.
draw = ImageDraw.Draw(im)
c_info = props_for_contours(contours, edges)
for c in c_info:
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
draw.rectangle(this_crop, outline='blue')
draw.rectangle(crop, outline='red')
# im.save(out_path + cropped_jpeg_list[pg_count])
draw.text((50, 50), path, fill='red')
# orig_im.save(out_path + cropped_jpeg_list[pg_count])
if args.type == "full":
im.show()
text_im = orig_im.crop(crop)
w_original, h_original = orig_im.size
w_original_half = w_original/2
w_cropped, h_cropped = text_im.size
if w_cropped < w_original_half:
text_im = orig_im
print ("More than half the page was cropped width-wise. Defaulting to original uncropped image.")
# Converting to np array to calculate number of channels in jpg. Some directories are single channel jpgs
open_cv_image = np.array(text_im)
if open_cv_image.ndim == 2:
channels = 0
else:
channels = open_cv_image.shape[2]
print(channels)
# try:
# print(type(text_im))
# except:
# print("")
# text_im.save(out_path + cropped_jpeg_list[pg_count])
# print '%s -> %s' % (path, out_path)
# Deskew image
direct_wo_saving = ""
try:
direct_wo_saving = "Y"
# Convert RGB to BGR
if channels != 0:
open_cv_image = open_cv_image[:, :, ::-1].copy()
deskewed_image = deskew(im=open_cv_image,
save_directory=out_path,
direct=direct_wo_saving)
pg_count += 1
print("Pg " + str(pg_count) + " de-skew complete")
except:
direct_wo_saving = "N"
text_im.save(out_path + cropped_jpeg_list[pg_count])
cropped_image = cv2.imread(out_path + cropped_jpeg_list[pg_count])
print("Cropped image saved to, and read from file")
deskewed_image = deskew(im=cropped_image,
save_directory=out_path,
direct=direct_wo_saving)
pg_count += 1
def main():
parser=argparse.ArgumentParser(description="Read a scanned street directory image, crop, and deskew.")
parser.add_argument("-type", help="Select a type of image process, full or minimal", dest="type", type=str, required=True)
parser.add_argument("-in", help = "Input file directory", dest="input", type=str, required=True)
parser.add_argument("-out",help="Output file directory" ,dest="output", type=str, required=True)
parser.set_defaults(func=process_image)
args=parser.parse_args()
args.func(args)
if __name__=="__main__":
main()