这是使用阈值+形态学操作的简单方法。
获取二值图像。加载图像,转换为灰度,然后自适应阈值
填充矩形轮廓。查找轮廓并填充轮廓以创建填充的矩形块。
执行变形打开。我们创建一个矩形结构元素并变形打开以删除线条
围绕最大矩形绘制矩形查找轮廓并围绕面积高于特定阈值的矩形绘制边界矩形。
以下是每个步骤的可视化:
获取二值图像
自适应阈值
填充矩形轮廓
执行变形开放
围绕最大的矩形绘制矩形
在代码中:
import numpy as np
import cv2
#load the image
image = cv2.imread("mtF6y.jpg")
# grayscale
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# adaptive threshold
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)
# Fill rectangular contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(thresh, [c], -1, (255,255,255), -1)
# Morph open
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=4)
# Draw rectangles, the 'area_treshold' value was determined empirically
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
area_treshold = 4000
for c in cnts:
if cv2.contourArea(c) > area_treshold :
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)
cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()
Run Code Online (Sandbox Code Playgroud)