1 """
2 1.先将文件夹内的图片进行遍历，获取所有文件名称，放到列表中。只需要获取一个文件夹内的名称即可，因为新旧文件夹内图片的名称都是一样的，只是截图时候的版本不同
3 2.通过ssim将新旧文件夹内的图片进行对比，得到相似度评分，并将差异处进行框选
4 3.将旧图、新图、对比图、对比结果 放置在excel表中
5 4.将excel表和对比报告通过email发送给指定人
6 """
7
8 import os
9 import cv2
10 import imutils
11 import xlsxwriter
12 import numpy as np
13 from skimage.metrics import structural_similarity as ssim
14
15 """找到图片的坐标"""
16 def find_all_pic(new_pic_location, old_pic_location):
17 new_pic_location_list = []
18 old_pic_location_list = []
19 for root, dirs, files in os.walk(new_pic_location):
20 for file in files:
21 new_pic_location_list.append(file)
22 # print(new_pic_location_list)
23 for root, dirs, files in os.walk(old_pic_location):
24 for file in files:
25 old_pic_location_list.append(file)
26 # print(old_pic_location_list)
27 if new_pic_location_list == old_pic_location_list:
28 """如果说新截图的图片名和旧截图的名一样的话，说明坐标位置是相同的，随便取一组就好了"""
29 pic_location_list = new_pic_location_list
30 # print(pic_location_list)
31 return pic_location_list
32 else:
33 print('=-=-=-=-=-=')
34
35 def ssim_check(new_pic, old_pic):
36 new_pic = cv2.imread(new_pic)
37 old_pic = cv2.imread(old_pic)
38
39 "转灰度图"
40 new_pic_gray = cv2.cvtColor(new_pic, cv2.COLOR_BGR2GRAY)
41 old_pic_gray = cv2.cvtColor(old_pic, cv2.COLOR_BGR2GRAY)
42
43 (score, diff) = ssim(new_pic_gray, old_pic_gray,win_size = 111, gaussian_weights = True, full = True)
44 print("图片相似度: {:.4f}%".format(score * 100))
45
46
47 return score, diff, new_pic, old_pic
48
49 def find_contours(diff, before, after):
50
51 """diff 图像包含两个图像之间的实际图像差异，并表示为 0到 1 范围内的浮点数据类型
52 将数组转换为 [0，255] 范围内的整型8位无符号"""
53 diff = (diff * 255).astype("uint8")
54 # print(diff)
55 # cv2.imshow('two img different',diff)
56
57 # diff_box = cv2.merge([diff, diff, diff])
58 '''使用numpy stack()函数将数组进行2维堆叠 结果和cv2.merge算法相同，在大型矩阵中使用numpy速度更快'''
59 diff_box = np.stack((diff, diff, diff), axis = 2) # 灰度图层面对比
60 # print(diff_box)
61
62
63 '''二值化并对差异图像进行阈值设置阈值，查找轮廓以获得两个图像的不同区域 使用cv2.THRESH_OTSU方法时注意阈值要设定为0 '''
64 thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] # [0]返回的是阈值 [1]是返回灰度图二值化后的结果
65 # print('这是thresh{}'.format(thresh))
66 # cv2.imshow('thresh', thresh)
67
68 '''轮廓提取'''
69 contours = cv2.findContours(thresh.copy(),
70 cv2.RETR_EXTERNAL, # CV.RETR_EXTERNAL仅检索外轮廓。并为所有轮廓设置层级结构
71 cv2.CHAIN_APPROX_SIMPLE) # CV.CHAIN_APPROX_SIMPLE -压缩水平、垂直、对角线方向的元素，仅留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
72 # print('轮廓坐标{}'.format(contours))
73 # print(len(contours))
74 # print('=============')
75 contours = imutils.grab_contours(contours)
76
77 for c in contours:
78 area = cv2.contourArea(c)
79 if area > 500: # 异常点绿框阈值控制
80 x, y, w, h = cv2.boundingRect(c)
81 # cv2.rectangle(before, (x, y), (x + w, y + h), (0, 255, 0), 2)
82 cv2.rectangle(after, (x, y), (x + w, y + h), (0, 255, 0), 2)
83 # cv2.rectangle(diff_box, (x, y), (x + w, y + h), (0, 255, 0), 2)
84
85 '''原始图片对比结果'''
86 # cv2.imshow('before', before)
87
88 '''更改后图片对比结果'''
89 # cv2.imshow('after', after)
90
91 '''灰度图片'''
92 # cv2.imshow('diff', diff)
93 # cv2.imshow('diff_box', diff_box)
94
95 cv2.waitKey()
96 cv2.destroyAllWindows()
97 return after
98
99 def put_pic_into_contrast(pic_name, after):
100 if not os.path.exists("contrast_jpg"):
101 os.makedirs(r'D:\opencvtest\contrast_jpg')
102 cv2.imwrite(r'D:\opencvtest\contrast_jpg\{}'.format(pic_name), after)
103
104 def excel_workbook(pic_name, score, worksheet, row, col):
105 score *= 100
106 worksheet.write(row, col, pic_name)
107 worksheet.insert_image(row, col + 1, r'D:\opencvtest\old_jpg\{}'.format(pic_name),
108 {'x_scale': 1, 'y_scale': 1})
109 worksheet.insert_image(row, col + 2, r'D:\opencvtest\contrast_jpg\{}'.format(pic_name),
110 {'x_scale': 0.81, 'y_scale': 0.81})
111 worksheet.write(row, col + 3, str(round(score, 3))+'%')
112 print(row)
113
114 def main(new_jpg_location, old_jpg_location):
115 workbook = xlsxwriter.Workbook('version_contrast.xlsx')
116 worksheet = workbook.add_worksheet("版本截图对比")
117 worksheet.set_column('A:A', 20)
118 worksheet.set_column('B:C', 40)
119 worksheet.set_column('D:D', 10)
120 worksheet.write(0, 0, '图片名称')
121 worksheet.write(0, 1, '旧版本截图')
122 worksheet.write(0, 2, '新版本截图')
123 worksheet.write(0, 3, '图像相似度')
124 row, col = 1, 0
125 for pic_name in find_all_pic(new_jpg_location, old_jpg_location):
126 # print(pic_name)
127 pic_old = r'D:\opencvtest\old_jpg\{}'.format(pic_name)
128 pic_new = r'D:\opencvtest\new_jpg\{}'.format(pic_name)
129
130 score, diff, new_pic, old_pic = ssim_check(pic_new, pic_old)
131
132 after = find_contours(diff, before = old_pic, after = new_pic)
133
134 put_pic_into_contrast(pic_name, after = after)
135
136 excel_workbook(pic_name, score, worksheet, row, col)
137 row += 1
138
139
140 workbook.close()
141
142
143 if __name__ == '__main__':
144 new_jpg_location = r'D:\opencvtest\new_jpg'
145 old_jpg_location = r'D:\opencvtest\old_jpg'
146 main(new_jpg_location, old_jpg_location)
标签：基于,old,pic,cv2,location,new,diff,对比,图片 From： https://www.cnblogs.com/pantom0122/p/17644592.html