使用Python OpenCV查找图像中的极端外部点 [英] Find extreme outer points in image with Python OpenCV

问题描述

我有这个雕像像.

我正在尝试找到雕像上最顶部，最底部，最左侧和最右侧的点.有没有一种方法可以测量每边的边缘以确定雕像上最外面的点?我想获取每侧的(x,y)坐标.我尝试使用cv2.findContours()和cv2.drawContours()来获取雕像的轮廓.

I'm trying to find the top, bottom, left, and right most points on the statue. Is there a way to measure the edge of each side to determine the outer most point on the statue? I want to get the (x,y) coordinate of each side. I have tried to use cv2.findContours() and cv2.drawContours() to get an outline of the statue.

import cv2

img = cv2.imread('statue.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

contours = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(img, contours, -1, (0, 200, 0), 3)

cv2.imshow('img', img)
cv2.waitKey()

推荐答案

这是一种可能的方法:

• 将图像转换为灰度和高斯模糊
• 阈值以获得一个二进制图片
• 查找轮廓
• 获取外部坐标

• Convert image to grayscale and Gaussian blur
• Threshold to obtain a binary image
• Find contours
• Obtain outer coordinates

转换为灰度并模糊图像后，我们阈值以获取二进制图像

After converting to grayscale and blurring image, we threshold to get a binary image

现在，我们使用cv2.findContours()查找轮廓.由于OpenCV使用Numpy数组对图像进行编码，因此轮廓只是(x,y)坐标的Numpy数组.我们可以对Numpy数组进行切片，并使用 argmin() 或 argmax() 确定左外，右，顶部和底部坐标

Now we find contours using cv2.findContours(). Since OpenCV uses Numpy arrays to encode images, a contour is simply a Numpy array of (x,y) coordinates. We can slice the Numpy array and use argmin() or argmax() to determine the outer left, right, top, and bottom coordinates like this

left = tuple(c[c[:, :, 0].argmin()][0])
right = tuple(c[c[:, :, 0].argmax()][0])
top = tuple(c[c[:, :, 1].argmin()][0])
bottom = tuple(c[c[:, :, 1].argmax()][0])

这是结果

左:(162，527)

left: (162, 527)

右:(463，467)

right: (463, 467)

顶部:(250，8)

底部:(381，580)

bottom: (381, 580)

import cv2
import numpy as np

# Load image, grayscale, Gaussian blur, threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 220, 255, cv2.THRESH_BINARY_INV)[1]

# Find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
c = max(cnts, key=cv2.contourArea)

# Obtain outer coordinates
left = tuple(c[c[:, :, 0].argmin()][0])
right = tuple(c[c[:, :, 0].argmax()][0])
top = tuple(c[c[:, :, 1].argmin()][0])
bottom = tuple(c[c[:, :, 1].argmax()][0])

# Draw dots onto image
cv2.drawContours(image, [c], -1, (36, 255, 12), 2)
cv2.circle(image, left, 8, (0, 50, 255), -1)
cv2.circle(image, right, 8, (0, 255, 255), -1)
cv2.circle(image, top, 8, (255, 50, 0), -1)
cv2.circle(image, bottom, 8, (255, 255, 0), -1)

print('left: {}'.format(left))
print('right: {}'.format(right))
print('top: {}'.format(top))
print('bottom: {}'.format(bottom))
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

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