下面附上程序：（备注：里面的代码是在之前某个博主的代码基础上改装过来的，具体是哪个我已经不记得了）
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 反相灰度图，将黑白阈值颠倒
def accessPiexl(img):
height = img.shape[0]
width = img.shape[1]
for i in range(height):
for j in range(width):
img[i][j] = 255 - img[i][j]
return img
# 反相二值化图像
def accessBinary(img, threshold=200):
img = accessPiexl(img)
# 边缘膨胀，不加也可以
kernel = np.ones((3, 3), np.uint8)
img = cv2.dilate(img, kernel, iterations=1)
_, img = cv2.threshold(img, threshold, 0, cv2.THRESH_TOZERO)
return img
# 寻找边缘，返回边框的左上角和右下角（利用cv2.findContours）
def findBorderContours(path, maxArea=50):
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)#(cv2.IMREAD_GRAYSCALE)按灰度模式读取图像
img = accessBinary(img)
contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)#(cv2.RETR_EXTERNAL)检测边缘，(cv2.CHAIN_APPROX_NONE)绘制
borders = []
border_sortt = []
# print("打印",hierarchy)
for contour in contours:
# 将边缘拟合成一个边框
x, y, w, h = cv2.boundingRect(contour)
if w*h > maxArea:
border = [(x, y), (x+w, y+h)]
borders.append(border)
return borders
# 显示结果及边框
def Save_Results(path, borders):
img = cv2.imread(path)
# 绘制
print(img.shape)
for i, border in enumerate(borders):
print("border = ",border)
pic = img[border[0][1] - 30 :border[1][1] + 30 , border[0][0] - 30 : border[1][0] + 30]
# cv2.imshow("img",pic)
# cv2.waitKey(0)
cv2.imwrite("D:/0000{num}.jpg".format(num=i),pic)
if __name__ == '__main__':
# 综合测试
path = './1.jpg'
borders = findBorderContours(path) #要抠图的原图片
Aave_Results(path, borders)
原图：
处理后的：
