OpenCV——Python：轮廓和形状检测6

# 轮廓/形状检测
import cv2
import numpy as np

# 图像排列处理函数
def stackImages(scale, imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv2.cvtColor(imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver

def getContours(img):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  # 使用cv点查找轮廓（传入图像对象和检测范围参数）
    for cnt in contours:
        area = cv2.contourArea(cnt)
        print(area)
        if area > 500:  # 设置检测阈值
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)  # 将复制图像放上面，从而框出
            peri = cv2.arcLength(cnt, True)  # 计算周长（轮廓长度）检测外围轮廓
            # print(peri)
            approx = cv2.approxPolyDP(cnt, 0.02*peri, True)  # 统计拐点个数，通过拐点个数确认图形形状
            print(len(approx))  # 打印其长度，3是三角，4是长方形
            objCor = len(approx)  # 检测边界框
            x, y, w, h = cv2.boundingRect(approx)  # 得到xy和对象长宽

            # 对象类型名称设置：
            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(imgContour, (x, y), (x+w, y+h), (0, 255, 0), 2)  # 绘制框，位置宽高（边界框）
            cv2.putText(imgContour, objectType,
                        (x+(w//2)-10, y+(h//2)-10), cv2.FONT_HERSHEY_COMPLEX, 0.7,
                        (0, 0, 0), 2)  # 设置脚本文字（进行了偏移），字体颜色，大小，颜色，粗细

path = '5.png'  # 设置资源文件夹
img = cv2.imread(path)
imgContour = img.copy()

# 预处理，转化为灰度，检测拐角点
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 频道为白色，BG设置为灰色
imgBlur = cv2.GaussianBlur(imgGray, (7, 7), 1)  # 高斯模糊函数，定义内核，设置sigma
imgCanny = cv2.Canny(imgBlur, 50, 50)  # 边缘检测器
getContours(imgCanny)

imgBlank = np.zeros_like(img)  # 创建空白图像
imgStack = stackImages(0.5, ([img, imgGray, imgBlur],
                             [imgCanny, imgContour, imgBlank]))

cv2.imshow('Stack', imgStack)
cv2.waitKey(0)