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最编程
2024-03-10 22:42:25
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import cv2
import numpy as np
# 输入灰度图，返回hash
def getHash(image):
avreage = np.mean(image) # 计算像素平均值
hash = []
for i in range(image.shape[0]):
for j in range(image.shape[1]):
if image[i, j] > avreage:
hash.append(1)
else:
hash.append(0)
return hash
# 计算汉明距离 计算相似度越大越不相似
def Hamming_distance(hash1, hash2):
num = 0
for index in range(len(hash1)):
if hash1[index] != hash2[index]:
num += 1
return num
img = cv2.imread("3.png")
# 灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = gray.shape[:2]
m = np.reshape(gray, [1, w * h]) # 降维
mean = m.sum() / (w * h) # 求均值
_, thresh1 = cv2.threshold(gray, mean, 255, cv2.THRESH_BINARY) # 利用均值进行图像二值化
# 查找轮廓
_, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
img2 = img.copy()
cv2.drawContours(img2, contours, -1, (0, 0, 255), 0)
# 记录裁剪的坐标 ，裁剪图像的时候使用
list_coor = []
# 计算轮廓面积
for i in range(len(contours)):
if i == 0:
continue
area = cv2.contourArea(contours[i])
if area > 3000 and area < 5000:
rect = cv2.minAreaRect(contours[i])
box = cv2.boxPoints(rect)
box = np.int0(box)
list_coor.append(box)
# 要进行对比的图像
Image = cv2.imread('mei.jpg')
# 灰度化
Image = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
hash_1 = getHash(Image)
# 随便设置一个大值
error = 100000.0
error_coor = []
# 计算分割好的图像hash
for i in range(len(list_coor)):
x, y = list_coor[i][1]
x1, y1 = list_coor[i][3]
img_temp = gray[min(x, x1):max(x, x1), min(y, y1):max((y, y1))]
img_temp = cv2.resize(img_temp, (80, 80))
cv2.imwrite("./img/%s.jpg" % i, img_temp)
hash_2 = getHash(img_temp)
ret = Hamming_distance(hash_1, hash_2)
# print(ret)
if ret < error:
error = ret
error_coor.clear()
error_coor.append([min(x, x1), max(x, x1)])
error_coor.append([min(y, y1), max(y, y1)])
print(ret)
print(error_coor) # [array([146, 144]), array([215, 74])]
x, y = error_coor[0]
x1, y1 = error_coor[1]
cv2.rectangle(img, (x, y), (x1, y1), (0, 0, 255), 3)
cv2.imshow('paper', img)
cv2.waitKey(0)
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