chapter8.py

import cv2
import numpy as np

# 检测出物体轮廓，并将其分类，
# 使用gray灰度图，高斯模糊，canny边缘检测，获得contour, 拐角点，按角点分类


def stackImages(scale, imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    # & 输出一个 rows * cols 的矩阵（imgArray）  ([])
    # print(rows,cols)
    # & 判断imgArray[0] 是不是一个list
    rowsAvailable = isinstance(imgArray[0], list)
    # & imgArray[][] 是什么意思呢？
    # & imgArray[0][0]就是指[0,0]的那个图片（我们把图片集分为二维矩阵，第一行、第一列的那个就是第一个图片）
    # & 而shape[1]就是width，shape[0]是height，shape[2]是
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]

    # & 例如，我们可以展示一下是什么含义
    # cv2.imshow("img", imgArray[0][1])

    if rowsAvailable:
        for x in range (0, rows):
            for y in range(0, cols):
                # & 判断图像与后面那个图像的形状是否一致，若一致则进行等比例放缩；否则，先resize为一致，后进行放缩
                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)
                # & 如果是灰度图，则变成RGB图像（为了弄成一样的图像）
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        # & 设置零矩阵
        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)
    # & 如果不是一组照片，则仅仅进行放缩 or 灰度转化为RGB
    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)  # 图片，检索方法，cv2.RETR_EXTERNAL 检索极端外部轮廓, 不需要近似
    for cnt in contours:
        area = cv2.contourArea(cnt)
        print(area)
        if area > 500:           # 忽略噪声影响 形状面积大于500
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)  # contourIdx=-1 代表全部contour
            # 计算曲线长度，长度可以帮助我们得到近似边缘的拐角
            # perimeter 周长
            peri = cv2.arcLength(cnt, True)   # curve closed 边缘 True
            # print(peri)
            # 近似拐角点  进行多边形逼近，得到多边形的角点
            approx = cv2.approxPolyDP(cnt, 0.02*peri, True)   # 拐角点
            print(len(approx))
            objCor = len(approx)           # 拐角点个数
            x, y, w, h = cv2.boundingRect(approx)            # 获取图形的宽和高以及起始点的坐标

            if objCor == 3:
                ObjectType = "Tri"
            elif objCor == 4:
                aspRate = w / float(h)
                if aspRate >= 0.98 and aspRate <= 1.03:
                    ObjectType = "Squre"
                else:
                    ObjectType = "Rect"
            elif objCor > 4:
                ObjectType = "Circle"
            else:
                ObjectType = "None"


            cv2.rectangle(imgContour, (x,y), (x+w,y+h), (0,0,0), 3)
            cv2.putText(imgContour, ObjectType, ((x+w//2)-20, (y+h//2)-10),
                        cv2.FONT_HERSHEY_COMPLEX, 0.6,(0,0,0), 2)


path = 'Resources/shapes.png'
img = cv2.imread(path)
imgContour = img.copy()

imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (7,7), sigmaX=1)  # sigma值越大，高斯模糊效果越好

# 接下来找到图像中的边缘
imgCanny = cv2.Canny(imgBlur, 50, 50)

getContours(imgCanny)

imgBlank = np.zeros_like(img)
imgStack = stackImages(0.6, ([img, imgGray, imgBlur],
                             [imgCanny, imgContour, imgBlank]))

cv2.imshow("Stack", imgStack)
# cv2.imshow("Original", img)
# cv2.imshow("Gray", imgGray)
# cv2.imshow("Blur", imgBlur)
cv2.waitKey(0)