内容来自OpenCV-Python Tutorials 自己翻译整理

长宽比：
边界矩形的宽高比
<span class="MathJax" id="MathJax-Element-15-Frame" tabindex="0" data-mathml="AspectRation=WidthHeight" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">AspectRation=WidthHeight

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

x,y,w,h = cv2.boundingRect(cnt)#获取边界

aspect_ratio = float(w)/h#计算比率

<span class="MathJax" tabindex="0" data-mathml="AspectRation=WidthHeight" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-wrap: normal; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">

print(aspect_ratio)

面积比
轮廓面积与边界矩形的面积比

<span class="MathJax" id="MathJax-Element-25-Frame" tabindex="0" data-mathml="Extent=ObjectAreaBoundingRectangleArea" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">Extent=ObjectAreaBoundingRectangleArea

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

area = cv2.contourArea(cnt)#获取轮廓面积

x,y,w,h = cv2.boundingRect(cnt)#获取边界

rect_area = w*h

extent = float(area)/rect_area

<span class="MathJax" tabindex="0" data-mathml="Extent=ObjectAreaBoundingRectangleArea" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-wrap: normal; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">

print(extent)

凸包面积比
轮廓面积与凸包面积的比

<span class="MathJax" id="MathJax-Element-31-Frame" tabindex="0" data-mathml="Solidity=ContourAreaConvexHullArea" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">Solidity=ContourAreaConvexHullArea

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

area = cv2.contourArea(cnt)

hull = cv2.convexHull(cnt)

hull_area = cv2.contourArea(hull)

solidity = float(area)/hull_area

<span class="MathJax" tabindex="0" data-mathml="Solidity=ContourAreaConvexHullArea" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-wrap: normal; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">

print(solidity)

轮廓直径
与轮廓面积相等的圆形的直径

<span class="MathJax" id="MathJax-Element-41-Frame" tabindex="0" data-mathml="EquivalentDiameter=4&#xD7;ContourArea&#x3C0;" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">EquivalentDiameter=4×ContourAreaπ−−−−−−−−−−√

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

area = cv2.contourArea(cnt)

equi_diameter = np.sqrt(4*area/np.pi)

<span class="MathJax" tabindex="0" data-mathml="EquivalentDiameter=4&#xD7;ContourArea&#x3C0;" role="presentation" style="box-sizing: border-box; outline: 0px; display: inline; line-height: normal; text-align: left; word-wrap: normal; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; word-break: break-all; position: relative;">

print(equi_diameter)

长短轴方向

对象的方向，下面的方法还会返回长轴和短轴的长度

(x,y),(MA,ma),angle = cv2.fitEllipse(cnt)

蒙板与像素

有时我们需要目标对象在图像里所有像素信息

（这里没使用灰度图）

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

mask = np.zeros(img.shape,np.uint8)

cv2.drawContours(mask,[cnt],0,255,-1)

pixelpoints = np.transpose(np.nonzero(mask))#像素

此处使用了两种方法，分别为numpy的函数和使用opencv的函数。结果相同，但是numpy给出的是行列值，而opencv给出的是(x,y)形式

最大值和最小值及位置

要用到mask 像素蒙板

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(img,mask = mask)

平均颜色和平均灰度

可以使用相同的蒙板求一个对象的平均颜色或平均灰度

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

mask = np.zeros(img.shape,np.uint8)

mean_val = cv2.mean(img,mask = mask)

print(mean_val)

极值点

就是求图像中某个对象的最高、最低、最左、最右的点

import cv2

import numpy as np

img = cv2.imread('3.jpg',0)

#ret,thresh = cv2.threshold(img,127,255,0)

img,contours,hierarchy = cv2.findContours(img, 1, 2)

cnt = contours[1]

leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])

rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])

topmost = tuple(cnt[cnt[:,:,1].argmin()][0])

bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])

print(leftmost)

奈斯，优秀