文本将讲述如何使用OpenCV识别一个图片中所包含的形状。

安装依赖包：

$ pip install imutils

首先来定义一个类来负责检测图片中的形状：

import cv2
 
class ShapeDetector:
    def __init__(self):
        pass
 
    def detect(self, c):
        # 初始化图片名称与大概的形状
        shape = "unidentified"
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.04 * peri, True)

用于计算图形大至轮廓的算法叫道格拉斯-普克算法 OpenCV中是通过
cv2.approxPolyDP对此进行实现。

道格拉斯-普克算法(Douglas–Peucker algorithm，亦称为拉默-道格拉斯-普克算法、迭代适应点算法、分裂与合并算法)是将曲线近似表示为一系列点，并减少点的数量的一种算法。

这个算法可以在1~5%范围内达到原始图像的边缘，通过它我们可以得到图像的“边”。

···python

if the shape is a triangle, it will have 3 vertices

    if len(approx) == 3:
        shape = "triangle"

    # if the shape has 4 vertices, it is either a square or
    # a rectangle
    elif len(approx) == 4:
        # compute the bounding box of the contour and use the
        # bounding box to compute the aspect ratio
        (x, y, w, h) = cv2.boundingRect(approx)
        ar = w / float(h)

        # a square will have an aspect ratio that is approximately
        # equal to one, otherwise, the shape is a rectangle
        shape = "square" if ar >= 0.95 and ar <= 1.05 else "rectangle"

    # if the shape is a pentagon, it will have 5 vertices
    elif len(approx) == 5:
        shape = "pentagon"

    # otherwise, we assume the shape is a circle
    else:
        shape = "circle"

    # return the name of the shape
    return shape

当写完这个类就要像我在[《OpenCV定位轮廓的中点》](https://www.jianshu.com/p/9f8915caca13)一文中对图片进行灰度化处理并计算其二值图，最后再找出所有的轮廓：

```python
# import the necessary packages
from pyimagesearch.shapedetector import ShapeDetector
import argparse
import imutils
import cv2
 
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to the input image")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
 
# convert the resized image to grayscale, blur it slightly,
# and threshold it
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
 
# find contours in the thresholded image and initialize the
# shape detector
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()

最后在图型中标出其形状：

# loop over the contours
for c in cnts:
    # compute the center of the contour, then detect the name of the
    # shape using only the contour
    M = cv2.moments(c)
    cX = int((M["m10"] / M["m00"]) * ratio)
    cY = int((M["m01"] / M["m00"]) * ratio)
    shape = sd.detect(c)
 
    # multiply the contour (x, y)-coordinates by the resize ratio,
    # then draw the contours and the name of the shape on the image
    c = c.astype("float")
    c *= ratio
    c = c.astype("int")
    cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
    cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
        0.5, (255, 255, 255), 2)
 
    # show the output image
    cv2.imshow("Image", image)
    cv2.waitKey(0)

最终效果如下：