Object detection using OpenCV

object_detection.py

    import cv2
    import numpy as np
    path = 'Resources/shapes.png'
    img = cv2.imread(path)
    imgContour = img.copy()
    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_SIMPLE)[-2:]
        for cnt in contours:
            area = cv2.contourArea(cnt)
            print(area)
            cv2.drawContours(imgContour,cnt,-1,(255,0,0),3)
            if area > 500:
                cv2.drawContours(imgContour,cnt,-1,(255,0,0),3)
                peri = cv2.arcLength(cnt,True)
                print(peri)
                approx = cv2.approxPolyDP(cnt,0.02*peri,True)
                print(len(approx))  # 3 is triangle 4 can be square /rectnage anything >4 is circle
                objCor = len(approx)
                # Now we will create a bounding box around our detected object
                x,y,w,h = cv2.boundingRect(approx)
                # Now clasisifying the object
                objectType=""
                if objCor == 3:objectType="Triangle"
                elif objCor == 4:
                    aspRatio = w/float(h)
                    if aspRatio > 0.95 and aspRatio <1.05 : objectType = "Sqaure"
                    else:objectType="Rectangle"
                elif objCor>4:objectType="Circles"

                cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 255, 0), 2)  # this will draw a rectannge over the detected shape
                cv2.putText(imgContour,objectType,
                            (x+(w//2)-10,y+(h//2)-10),
                            cv2.FONT_HERSHEY_PLAIN,
                            0.6,
                            (0,0,0),2)


    ## First we will convert our image to gray scale and then we will determine the edges
    imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
    imgCanny = cv2.Canny(imgBlur,50,50) #Used to detect images

    #cv2.imshow("Original",img)
    #cv2.imshow("Gray",imgGray)
    #cv2.imshow("Blur",imgBlur)

    getContours(imgCanny)

    imgBlank = np.zeros_like(img)
    imgStack = stackImages(0.6,([img,imgGray,imgBlur],
                                [imgCanny,imgContour,imgBlank]))
    cv2.imshow("Stack",imgStack)
    cv2.waitKey(0)