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crop_by_word_bb_syn90k.py
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crop_by_word_bb_syn90k.py
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# Crop by word bounding box
# Locate script with gt.mat
# $ python crop_by_word_bb.py
import os
import re
import cv2
import scipy.io as sio
from itertools import chain
import numpy as np
import math
mat_contents = sio.loadmat('gt.mat')
image_names = mat_contents['imnames'][0]
cropped_indx = 0
start_img_indx = 0
gt_file = open('gt_oabc.txt', 'a')
err_file = open('err_oabc.txt', 'a')
for img_indx in range(start_img_indx, len(image_names)):
# Get image name
image_name_new = image_names[img_indx][0]
# print(image_name_new)
image_name = '/home/yxwang/pytorch/dataset/SynthText/img/'+ image_name_new
# print('IMAGE : {}.{}'.format(img_indx, image_name))
print('evaluating {} image'.format(img_indx), end='\r')
# Get text in image
txt = mat_contents['txt'][0][img_indx]
txt = [re.split(' \n|\n |\n| ', t.strip()) for t in txt]
txt = list(chain(*txt))
txt = [t for t in txt if len(t) > 0 ]
# print(txt) # ['Lines:', 'I', 'lost', 'Kevin', 'will', 'line', 'and', 'and', 'the', '(and', 'the', 'out', 'you', "don't", 'pkg']
# assert 1<0
# Open image
#img = Image.open(image_name)
img = cv2.imread(image_name, cv2.IMREAD_COLOR)
img_height, img_width, _ = img.shape
# Validation
if len(np.shape(mat_contents['wordBB'][0][img_indx])) == 2:
wordBBlen = 1
else:
wordBBlen = mat_contents['wordBB'][0][img_indx].shape[-1]
if wordBBlen == len(txt):
# Crop image and save
for word_indx in range(len(txt)):
# print('txt--',txt)
txt_temp = txt[word_indx]
len_now = len(txt_temp)
# txt_temp = re.sub('[^0-9a-zA-Z]+', '', txt_temp)
# print('txt_temp-1-',txt_temp)
txt_temp = re.sub('[^a-zA-Z]+', '', txt_temp)
# print('txt_temp-2-',txt_temp)
if len_now - len(txt_temp) != 0:
print('txt_temp-2-', txt_temp)
if len(np.shape(mat_contents['wordBB'][0][img_indx])) == 2: # only one word (2,4)
wordBB = mat_contents['wordBB'][0][img_indx]
else: # many words (2,4,num_words)
wordBB = mat_contents['wordBB'][0][img_indx][:, :, word_indx]
if np.shape(wordBB) != (2, 4):
err_log = 'malformed box index: {}\t{}\t{}\n'.format(image_name, txt[word_indx], wordBB)
err_file.write(err_log)
# print(err_log)
continue
pts1 = np.float32([[wordBB[0][0], wordBB[1][0]],
[wordBB[0][3], wordBB[1][3]],
[wordBB[0][1], wordBB[1][1]],
[wordBB[0][2], wordBB[1][2]]])
height = math.sqrt((wordBB[0][0] - wordBB[0][3])**2 + (wordBB[1][0] - wordBB[1][3])**2)
width = math.sqrt((wordBB[0][0] - wordBB[0][1])**2 + (wordBB[1][0] - wordBB[1][1])**2)
# Coord validation check
if (height * width) <= 0:
err_log = 'empty file : {}\t{}\t{}\n'.format(image_name, txt[word_indx], wordBB)
err_file.write(err_log)
# print(err_log)
continue
elif (height * width) > (img_height * img_width):
err_log = 'too big box : {}\t{}\t{}\n'.format(image_name, txt[word_indx], wordBB)
err_file.write(err_log)
# print(err_log)
continue
else:
valid = True
for i in range(2):
for j in range(4):
if wordBB[i][j] < 0 or wordBB[i][j] > img.shape[1 - i]:
valid = False
break
if not valid:
break
if not valid:
err_log = 'invalid coord : {}\t{}\t{}\t{}\t{}\n'.format(
image_name, txt[word_indx], wordBB, (width, height), (img_width, img_height))
err_file.write(err_log)
# print(err_log)
continue
pts2 = np.float32([[0, 0],
[0, height],
[width, 0],
[width, height]])
x_min = np.int(round(min(wordBB[0][0], wordBB[0][1], wordBB[0][2], wordBB[0][3])))
x_max = np.int(round(max(wordBB[0][0], wordBB[0][1], wordBB[0][2], wordBB[0][3])))
y_min = np.int(round(min(wordBB[1][0], wordBB[1][1], wordBB[1][2], wordBB[1][3])))
y_max = np.int(round(max(wordBB[1][0], wordBB[1][1], wordBB[1][2], wordBB[1][3])))
# print(x_min, x_max, y_min, y_max)
# print(img.shape)
# assert 1<0
if len(img.shape) == 3:
img_cropped = img[ y_min:y_max:1, x_min:x_max:1, :]
else:
img_cropped = img[ y_min:y_max:1, x_min:x_max:1]
dir_name = '/home/yxwang/pytorch/dataset/SynthText/cropped-oabc/{}'.format(image_name_new.split('/')[0])
# print('dir_name--',dir_name)
if not os.path.exists(dir_name):
os.mkdir(dir_name)
cropped_file_name = "{}/{}_{}_{}.jpg".format(dir_name, cropped_indx,
image_name.split('/')[-1][:-len('.jpg')], word_indx)
# print('cropped_file_name--',cropped_file_name)
# print('img_cropped--',img_cropped.shape)
if img_cropped.shape[0] == 0 or img_cropped.shape[1] == 0:
err_log = 'word_box_mismatch : {}\t{}\t{}\n'.format(image_name, mat_contents['txt'][0][
img_indx], mat_contents['wordBB'][0][img_indx])
err_file.write(err_log)
# print(err_log)
continue
# print('img_cropped--',img_cropped)
# img_cropped.save(cropped_file_name)
cv2.imwrite(cropped_file_name, img_cropped)
cropped_indx += 1
gt_file.write('%s\t%s\n' % (cropped_file_name, txt[word_indx]))
# if cropped_indx>10:
# assert 1<0
# assert 1 < 0
else:
err_log = 'word_box_mismatch : {}\t{}\t{}\n'.format(image_name, mat_contents['txt'][0][
img_indx], mat_contents['wordBB'][0][img_indx])
err_file.write(err_log)
# print(err_log)
gt_file.close()
err_file.close()