如何在不丢失任何内容的情况下检测所有矩形框python opencv [英] How to detect all rectangular boxes python opencv without missing anything

问题描述

我正在尝试从关系数据库中检测所有矩形.但是我的脚本未检测到某些框.请帮我做到这一点.谢谢.

I'm trying to detect all the rectangles from the relational database. But some of the boxes are not being detected by my script. Please help me to do that. Thank you.

图片:

我的代码:

#!/usr/bin/python
import cv2
import numpy as np

im = cv2.imread("table.png")

image = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(image,0,255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

edge = cv2.Canny(thresh,30,200)
cont = cv2.findContours(edge,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]

for j,i in enumerate(cont):
   x,y,w,h = cv2.boundingRect(i)

   if (w*h>900):
     cv2.drawContours(image,[i],0,(0,0,255),3)

cv2.imshow("Image",image)

cv2.waitKey(0)  

输出:

推荐答案

这是使用阈值+形态学运算的简单方法.

Here's an simple approach using thresholding + morphological operations.

1. 获取二进制图像.加载图像，转换为灰度，然后转换为自适应阈值

1. Obtain binary image. Load image, convert to grayscale, then adaptive threshold

填充矩形轮廓.查找轮廓并填充轮廓以创建填充的矩形块.

Fill rectangular contours. Find contours and fill the contours to create filled rectangular blocks.

执行变形打开.我们创建一个矩形结构元素，然后将其变形打开以删除线条

Perform morph open. We create a rectangular structuring element and morph open to remove the lines

绘制矩形.查找轮廓并绘制边界矩形.

Draw rectangle. Find contours and draw bounding rectangles.

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以下是可视化的每个步骤:

---

Here's each step visualized:

使用此屏幕快照图像(由于提供的图像的矩形太靠近边框，因此包含更多边框).您可以在输入图像上添加边框，而不是截图以获取更多边框区域.看看为图像添加边框

Using this screenshotted image (contains more border since the provided image has the rectangles too close to the border). You could add a border to the input image instead of screenshotting for more border area. Take a look at add border to image

二进制图片

填充矩形轮廓

打开形态

结果

代码

import cv2

# Load iamge, grayscale, adaptive threshold
image = cv2.imread('1.png')
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    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()

注意:根据映像，您可能必须修改内核大小.例如，可能有必要将内核从(5，5)增大为(11，11).另外，执行 cv2.morphologyEx()时，可以增加或减少迭代次数.在增加或减少内核大小时需要权衡取舍，因为您可能会删除更多或更少的行.同样，这一切都取决于输入图像.

Note: Depending on the image, you may have to modify the kernel size. For instance, it may be necessary to increase the kernel from (5, 5) to say (11, 11). In addition, you could increase or decrease the number of iterations when performing cv2.morphologyEx(). There is a trade-off when increasing or decreasing the kernel size as you may remove more or less of the lines. Again, it all varies depending on the input image.

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