Contour And Shape Detection - opencv cv2 tutorial chapter8

chapter8

import cv2
import numpy as np
from cv2tools import stackImages


def getContours(img):
    # cv2.findContours 在二值图像中检测轮廓 
    #     mode:
    #         cv2.RETR_EXTERNAL：只检索最外层的轮廓。
    #         cv2.RETR_LIST：检索所有轮廓，但不建立等级关系。
    #         cv2.RETR_CCOMP：检索所有轮廓，并建立一个两级连通组件的等级关系。
    #         cv2.RETR_TREE：检索所有轮廓，并建立完整的等级关系。
    #     method:
    #         cv2.CHAIN_APPROX_SIMPLE：只保留轮廓的终点。
    #         cv2.CHAIN_APPROX_NONE：保留所有的轮廓点。
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for cnt in  contours:
        # 面积
        area = cv2.contourArea(cnt)
        if area > 500:
            # cv2.drawContours 绘制轮廓
            #   第三个参数为轮廓索引, -1表示绘制所有轮廓
            cv2.drawContours(img_copy, cnt, -1, (255, 0, 0), 3)
            # cv2.arcLength 连续曲线长度
            arclen = cv2.arcLength(cnt, True)
            # cv2.approxPolyDP 对轮廓进行多边形近似
            #   第二个参数为精度 越大越简化轮廓
            approx = cv2.approxPolyDP(cnt, 0.02 * arclen, True)
            print(len(approx))
            objCor = len(approx)
            # 生成bbox
            x, y, w, h = cv2.boundingRect(approx)
            if objCor == 3:
                objectType = "Tri"
            elif objCor == 4:
                aspRatio = w / float(h)
                if aspRatio > 0.98 and aspRatio < 1.03:
                    objectType = "Square"
                else:
                    objectType = "Rectangle"
            elif objCor > 4:
                objectType = "Circles"
            else:
                objectType = "None"

            cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.putText(img_copy, objectType,
                        (x + (w // 2) - 15, y + (h // 2) - 15), cv2.FONT_HERSHEY_COMPLEX, 0.6,
                        (0, 0, 0), 1)

img = cv2.imread('Resources/shapes.png')
img_copy = img.copy()
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.GaussianBlur(img_gray, (7,7), 1)
img_canny = cv2.Canny(img_blur, 30, 30)
img_blank = np.zeros((img.shape[0],img.shape[1]), np.uint8)

getContours(img_canny)

stack_img = stackImages(0.6, ([img, img_gray, img_blur], [img_canny, img_copy, img_blank]))

cv2.imshow('stack', stack_img)

cv2.waitKey(0)