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word_recongination.py
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word_recongination.py
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#-*- coding:utf-8 -*-
'''
通过pytesseract包进行图片中文字识别
'''
import ImageProcess.img_pre_process as ppro
import ImageProcess.img_process as pro
from PIL import Image
import get_images
import numpy as np
import pytesseract
import os
def word_in_picture(folderpath):
# 导入图片
raw_imagepath = os.path.join(folderpath,'LEDCapturePics\\ledVideCapture.bmp')
dir_name = os.path.dirname(os.path.abspath(raw_imagepath))
image_class1 = get_images.Get_Image()
image_array = image_class1.image_ndarray(raw_imagepath) # 导入图片形成图像数组
#image_object = image_class1.image_object(raw_imagepath) # 导入图片形成图像对象
# '''
##############################无需裁剪时可注释掉该块代码
#2592*1520像素
# room1 = [(75,589,291,634),
# (82,652,287,696),
# (92,711,294,758),
# (112,773,263,809),
# (375,578,576,626),
# (362,642,578,691),
# (368,705,587,752),
# (372,769,580,806)]
# room2 = [(698,570,925,616),
# (705,633,913,677),
# (705,695,922,740),
# (706,757,915,802),
# (714,817,922,854),
# (1019,565,1226,612),
# (1014,630,1218,672),
# (1016,690,1223,736),
# (1016,752,1235,797),
# (1020,813,1235,850)
# ]
# room3 = [(1387,558,1559,606),
# (1388,622,1603,668),
# (1356,685,1631,730),
# (1394,748,1603,790),
# (1388,810,1599,844),
# (1699,556,1910,599),
# (1700,618,1913,659),
# (1701,680,1905,725),
# (1703,743,1906,785),
# (1705,804,1881,838)]
# room4 = [(2056,553,2246,599),
# (2054,616,2241,660),
# (2051,678,2243,720),
# (2050,735,2235,777),
# (2052,794,2210,832),
# (2341,551,2515,597),
# (2335,614,2516,657),
# (2335,670,2502,714),
# (2326,733,2502,771),
# (2339,789,2483,825)]
#####2560*1440像素
room1 = [(59,551,283,591),
(59,611,283,651),
(59,670,283,710),
(59,727,283,767),
(336,541,560,583),
(336,604,560,644),
(336,664,560,704),
(336,722,560,762)]
room2 = [(686,534,910,574),
(686,597,910,638),
(686,658,910,698),
(686,719,910,759),
(686,779,910,814),
(990,525,1214,571),
(990,590,1214,630),
(990,652,1214,692),
(990,715,1214,755),
(990,774,1214,809),
]
room3 = [(1363,519,1587,559),
(1363,583,1587,623),
(1363,647,1587,687),
(1363,707,1587,747),
(1363,768,1587,803),
(1681,518,1905,558),
(1681,578,1905,618),
(1681,641,1905,681),
(1681,702,1905,742),
(1681,762,1905,796)]
room4 = [(2018,514,2242,554),
(2018,573,2242,613),
(2018,635,2242,675),
(2018,697,2242,737),
(2018,753,2242,788),
(2295,513,2519,553),
(2295,572,2519,612),
(2295,632,2519,672),
(2295,691,2519,731),
(2295,747,2519,781)]
room = [room1,room2,room3,room4]
room_train_list = {}
for j in range(4):
room_name = j+1
room_train_list[room_name] = []
for i in room[j]:
# 图像预处理:裁剪和旋转
image_object = image_class1.image_object(raw_imagepath)
preimg = ppro.Cut_rotate(raw_imagepath)
# box_x = (180, 100) # 左上点像素位置
# box_y = (879, 244) # 右下点像素位置
box_x = (i[0], i[1]) # 左上点像素位置
box_y = (i[2], i[3]) # 右下点像素位置
tem_image_path = preimg.image_crop(image_object, x=box_x, y=box_y)
'''
raw_matrix = np.float32([[5, 93], [2525, 55], [25, 389], [2520, 343]]) # 待投影图指定区域四个顶点像素位置
aim_matrix = np.float32([[0, 0], [2550, 0], [0, 350], [2550, 350]]) # 目标图四个顶点像素位置
preimg.image_rotate(tem_image_path, raw_matrix, aim_matrix)
'''
# 图像处理
# 1.再次导入图片
image_class2 = get_images.Get_Image()
image_object = image_class2.image_object(tem_image_path)
##########################################
# '''
# 2.进行处理
# myimg = pro.Img_pro()
# myimg.initTable()
# image = myimg.image_split2(image_object) # 通道分离
# image.save(dir_name + '\\split.png')
# image = myimg.image_filter(image) # 图片滤波
# image.save(dir_name + '\\filter.png')
# image = myimg.image_enlarge_compress(image,r=1) # 图片的压缩和扩大,默认系数r=1
# image = myimg.image_sharp(image) # 图片锐化，默认系数r=2.0
# image.save(dir_name + '\\sharp.png')
# image = myimg.image_binary(image) # 图片二值化
# image.save(dir_name + '\\binary.png')
# image = myimg.image_binary_invert(image) # 图片二值反转
# image.save(dir_name + '\\invert.png')
# image = myimg.image_enlarge_compress(image, r=1) #默认r=1
# image.save(dir_name + '\\compress.png')
# 图像文字识别
text = pytesseract.image_to_string(image_object) # 文字识别
room_train_list[room_name].append(text)
return room_train_list
#return sorted(room_train_list.iteritems(),key = lambda room_train_list:room_train_list[0])
if __name__ == "__main__":
foldername = os.path.dirname(os.path.abspath(__file__))
text = word_in_picture(foldername)
print text