Getting-Started-with-OpenCV/Chapter8.py at master · devharitwal/Getting-Started-with-OpenCV · GitHub

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#Contours and shape detection

import cv2
import numpy as np

path = "res/shapes.png"
img = cv2.imread(path)
img1 = cv2.resize(img,(450,350))


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(img1):
    contours,hierarchy = cv2.findContours(img1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        area = cv2.contourArea(cnt)
        print("area of contours",area)
        #cv2.drawContours(imgCpy,cnt,-1,(255,255,100),3)
        if area > 500: #to avoid noise
            cv2.drawContours(imgCpy, cnt, -1, (255, 255, 100), 3)
            perimeter = cv2.arcLength(cnt,True)
            print("perimeter",perimeter)
            approx = cv2.approxPolyDP(cnt,0.02*perimeter,True)
            print("approx points",len(approx))
            objCor = len(approx)
            x,y,w,h = cv2.boundingRect(approx)
            cv2.rectangle(imgCpy,(x,y),(x+w,y+h),(0,255,234),2)

            if len(approx) == 3:
                print("Triangle")
            elif len(approx) ==4:
                print("Quadrilateral")
            elif len(approx) >4:
                print("Circle")


#pre-processing images -> grayscale ->edges -> corner points

imgGray = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
imgCanny = cv2.Canny(imgBlur,50,50)
imgBlank = np.zeros_like(img1)
imgCpy = img1.copy()
#cv2.imshow("Blur",imgBlur)
#cv2.imshow("Gray",imgGray)
#cv2.imshow("Original",img1)
getContours(imgCanny)

imgStack = stackImages(0.6,([img1,imgGray,imgBlur],
                            [imgCanny,imgCpy,imgBlank]))

cv2.imshow("image",imgStack)
cv2.waitKey(0)