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Copy path图片比对工具python版.py
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图片比对工具python版.py
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#coding:utf8
import sys
import os
import cv2
import numpy as np
def AssertImage( old_img, new_img, diff_img,check=None, nocheck=None,standard=90):
'''
old_img:原始的图片
new_img:需要对比的图片
diff_img:输出显示比对区域的图片
standard:相似度结果合格的标准数值，默认为90，可由前台设置
check：需要对比的区域,(左上角坐标，又下角坐标)
check = [
((0,0),(0,1)),
((10,15),(50,70)),
]
nocheck：不需要对比的区域
nocheck = [
((0,0),(0,1)),
((10,15),(50,70)),
]
当check和nocheck同时为空的时候，对比整张图片
'''
res = []
deep = 3 # 检测深度，作为每块长宽的分母，deep值越大，分的块越小，检测越严格
result = 0
bResult = 100
'''
if check is 'None':
check = []
else:
#将字符串str当成有效的表达式来求值并返回计算结果
check = eval(check)
if nocheck is 'None':
nocheck = []
else:
#将字符串str当成有效的表达式来求值并返回计算结果
nocheck = eval(nocheck)
'''
if not os.path.exists(old_img):
err = "Image({0}) not exeist".format(old_img)
print (err)
raise RunActivceException(err)
if not os.path.exists(new_img):
err = "Image({0}) not exeist".format(new_img)
print (err)
raise RunActivceException(err)
#cv2.imread() 读取图片，并显示
cv2_old_img = cv2.imread(old_img)
cv2_new_img = cv2.imread(new_img)
#图像矩阵的shape属性表示图像的大小，shape会返回tuple元组，第一个元素表示矩阵行数，第二个元组表示矩阵列数，第三个元素是3，表示像素值由光的三原色组成
oh, ow, _ = cv2_old_img.shape
nh, nw, _ = cv2_new_img.shape
#图片不一样大
if oh != nh or ow != nw:
print("The resolution of the old img is:[{0}*{1}]".format(oh, ow))
print("The resolution of the new img is:[{0}*{1}]".format(nh, nw))
raise RunActivceException("The resolution of the two pictures is different")
# 此处保留原本算法，用于计算整体相似度，并给出比对区域的图片
if nocheck is None:
nocheck = []
if check is None:
compare(cv2_new_img, cv2_old_img, check, nocheck, diff_img=diff_img,oh=oh, ow=ow, nh=nh, nw=nw)
else:
for ((sx, sy), (ex, ey)) in check:
compare(cv2_new_img, cv2_old_img, check, nocheck, diff_img=diff_img,
sx=sx, sy=sy, ex=ex, ey=ey)
# 此处开始精确比对，深度deep默认为3
oh /= deep # 图片块的宽度
ow /= deep
nh /= deep
nw /= deep
output = None
if check is None:
for t in range(deep):
for s in range(deep):
bResult, output = compare(cv2_new_img, cv2_old_img, check, nocheck,
t, s, oh=oh, ow=ow, nh=nh, nw=nw,standard=standard)
res.append(100 - bResult)
else:
for ((sx, sy), (ex, ey)) in check:
a = (ex - sx) / deep # 图片块的长度
b = (ey - sy) / deep # 图片块的宽度
for t in range(deep):
for s in range(deep):
bResult, output = compare(cv2_new_img, cv2_old_img, check, nocheck,
t, s, sx=a * s + sx, sy=b * t + sy, ex=a * (s + 1) + sx,
ey=b * (t + 1) + sy,standard=standard)
res.append(100 - bResult)
res.sort()
final_result = sum(res[:3]) / len(res[:3])
output = 'The image similarity is [{0}%],standard is [{1}%]'.format(final_result, standard)
print (output)
return final_result, [output, old_img, new_img, diff_img]
def compare( cv2_new_img, cv2_old_img, check, nocheck, t=0, s=0, diff_img=None, oh=0, ow=0, nh=0, nw=0, sx=0,
sy=0, ex=0, ey=0,standard=90):
bResult = 100
result = 0
if oh != nh or ow != nw:
print ("The resolution of the old img is:[{0}*{1}]".format(oh, ow))
print ("The resolution of the new img is:[{0}*{1}]".format(nh, nw))
print ("The resolution of the two pictures is different")
#empty 会创建一个没有使用特定值来初始化的数组
cv2_diff_img = np.empty(cv2_old_img.shape, np.uint8)
#初始化为255
cv2_diff_img.fill(255)
## 权重越大，透明度越低 标量加到每一个和 函数把两个图片融合在一起
cv2_old_img = cv2.addWeighted(cv2_old_img, 0.4, cv2_diff_img, 0.5, 0)
cv2_new_img = cv2.addWeighted(cv2_new_img, 0.4, cv2_diff_img, 0.5, 0)
cv2_diff_img = cv2_old_img.copy()
#宽
width = cv2_old_img.shape[0]
#高
height = cv2_old_img.shape[1]
if check is not None:
tmp = 0
# sx/=2
# ex/=2
continueFlag = False
#x 方向的像素
for i in range(sx, ex):
if continueFlag:
continueFlag = False
continue
#y方向的像素
for j in range(sy, ey):
for ((nosx, nosy), (noex, noey)) in nocheck:
#不在对比范围之内
if (nosx < i and i < noex) and (nosy < j and j < noey):
# print "check point:({0}) in no check point {1}-{2}".format((i,j),(nosx,nosy),(noex,noey))
continueFlag = True
break
if continueFlag:
continue
diff = int(cv2_old_img[j, i][0]) - int(cv2_new_img[j, i][0])
tmp += 1
if diff < 0:
if _clmap(abs(diff), 10, 255) != 255:
result += 1
cv2_diff_img[j, i] = [0, _clmap(abs(diff), 10, 255), 0]
elif diff > 0:
if _clmap(abs(diff), 10, 255) != 255:
result += 1
cv2_diff_img[j, i] = [0, 0, _clmap(abs(diff), 10, 255)]
else:
pass
if tmp != 0:
bResult = float(result * 100) / float(tmp)
#v2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)画出矩行
#参数解释
#第一个参数：img是原图
#第二个参数：（x，y）是矩阵的左上点坐标
#第三个参数：（x+w，y+h）是矩阵的右下点坐标
#第四个参数：（0,255,0）是画线对应的rgb颜色
#第五个参数：2是所画的线的宽度
cv2.rectangle(cv2_diff_img, (sx, sy), (ex, ey), (0, 255, 0), thickness=3, lineType=16)
else:
for i in range(oh * t, oh * (t + 1)):
continueFlag = False
for j in range(ow * s, ow * (s + 1)):
if continueFlag:
continueFlag = False
continue
for ((nosx, nosy), (noex, noey)) in nocheck:
if (nosx < j and j < noex) and (nosy < i and i < noey):
# print "check point:({0}) in no check point {1}-{2}".format((i,j),(nosx,nosy),(noex,noey))
continueFlag = True
break
if continueFlag:
continue
diff = int(cv2_old_img[i, j][0]) - int(cv2_new_img[i, j][0])
if diff < 0:
if _clmap(abs(diff), 10, 255) != 255:
result += 1
cv2_diff_img[i, j] = [0, _clmap(abs(diff), 10, 255), 0]
elif diff > 0:
if _clmap(abs(diff), 10, 255) != 255:
result += 1
cv2_diff_img[i, j] = [0, 0, _clmap(abs(diff), 10, 255)]
else:
pass
# result +=(abs(diff))
bResult = float(result * 100) / float(oh * ow)
cv2.rectangle(cv2_diff_img, (ow * s, oh * t), (ow * (s + 1), oh * (t + 1)), (0, 255, 0),
thickness=3, lineType=16)
for ((nosx, nosy), (noex, noey)) in nocheck:
cv2.rectangle(cv2_diff_img, (nosx, nosy), (noex, noey), (255, 0, 0), thickness=3, lineType=16)
if diff_img is not None:
output = 'The image general similarity is [{0}%],standard is [{1}%]'.format(100 - bResult,
standard)
cv2.imwrite(diff_img, cv2_diff_img)
else:
output = 'The image area({0},{1}) similarity is [{2}%],standard is [{3}%]'.format(t, s, 100 - bResult,
standard)
print (output)
return bResult, output
def _clmap( v, k, upBound):
val = v * k
if standard == 'None':
standard = int(90)
else:
standard = int(standard)
if val > upBound:
return val
else:
return upBound
if __name__ == '__main__':
args = sys.argv[:]
print args
old_img = sys.argv[1]
new_img = sys.argv[2]
diff_img = sys.argv[3]
check = sys.argv[4]
nocheck = sys.argv[5]
standard = sys.argv[6]
print type(check)
check = eval(check)
nocheck = eval(nocheck)
standard = eval(standard)
AssertImage(old_img,new_img,diff_img, check=check, nocheck=nocheck, standard=standard)
#AssertImage('112.png','111.png','diff.png')