Shape Detection

Contour detection

contours, hierarchy = cv2.findContours(image,mode,method)

• The first parameter input image,
• The second parameter represents the retrieval mode of contour, which has four types:
  1.cv2.RETR_EXTERNAL means that only the outer contour is detected
  2.cv2. RETR_ The contour detected by list does not establish a hierarchical relationship
  3.cv2.RETR_CCOMP establishes two levels of contours. The upper layer is the outer boundary and the inner layer is the boundary information of the inner hole. If there is a connected object in the inner hole, the boundary of the object is also on the top layer.
  4.cv2.RETR_TREE establishes the outline of a hierarchical tree structure.
• cv2.CHAIN_APPROX_NONE stores all contour points. The pixel position difference between two adjacent points does not exceed 1, that is, max (abs (x1-x2), abs (y2-y1)) = = 1
  cv2.CHAIN_APPROX_SIMPLE compresses the elements in the horizontal, vertical and diagonal directions, and only retains the end coordinates of the direction. For example, a rectangular contour only needs 4 points to save the contour information
  cv2.CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS uses teh chinl chain approximation algorithm

Calculate contour area

contourArea(contour,oriented = False)

This function uses Green's formula to calculate the area of the contour. For contours with self intersection points, this function will almost certainly give wrong results.

1.contour: enter a two-dimensional vector and store it as vector(C + +) or Mat.
2.oriented: directional area sign.

• true: this function returns the value of a marked area depending on the direction of the contour (clockwise or counterclockwise).
• false: default value. Means to return an absolute value without direction.

Calculate contour length

cv2.arcLength(InputArray curve, bool closed)

• curve, the input 2D point set (contour vertex), which can be of type vector or Mat.
• Closed, used to indicate whether the curve is closed.

Multilateral fitting function

The main function is to polyline a continuous smooth curve and fit the polygon of the image contour points

cv2.approxPolyDP(InputArray curve, OutputArray approxCurve, double epsilon, bool closed)

• InputArray curve: generally, it is a point composed of contour points of the image
• OutputArray approxCurve: represents the set of polygon points to be output
• double epsilon: it mainly indicates the output accuracy, that is, the maximum distance between another contour point, 5, 6, 7, 8
• bool closed: indicates whether the output polygon is closed

Gets the smallest rectangular border

x,y,w,h = cv2.boundingRect(img)

def geContours(img):
    countors,Hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    for cnt in countors:
        area = cv2.contourArea(cnt)
        print(area)
        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))
            objCor = len(approx)
            x,y,w,h = cv2.boundingRect(approx)
            
            #Judge shape
            if objCor == 3: objType = "Tri"
            elif objCor == 4:
                aspRatio = w/float(h)
                if aspRatio >0.95 and aspRatio <1.05: objType= "Square"
                else:objType="Rectangle"
            elif objCor>4: objType= "circles"
            else:objType="None"

            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
            cv2.putText(imgContour,objType,
                    (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,
                     (0,0,0),2)

module:

import cv2
import numpy as np

def stackImages(scale,imgArray):
'''
Image overlay module
'''
    rows = len(imgArray)
    cols = len(imgArray[0])
    # &Output a matrix of rows * cols (imgArray)
    print(rows,cols)
    # &Determine whether imgaray [0] is a list
    rowsAvailable = isinstance(imgArray[0], list)
    # &What does imgaray [] [] mean?
    # &Imgrarray [0] [0] refers to the picture of [0,0] (we divide the picture set into two-dimensional matrices, and the one in the first row and column is the first picture)
    # &shape[1] is width, shape[0] is height, and shape[2] is height
    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):
                # &Judge whether the shape of the image is consistent with that of the following image. If it is consistent, scale it in equal proportion; Otherwise, resize to be consistent first, and then zoom in and out
                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 it is a grayscale image, it becomes an RGB image (in order to make the same image)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        # &Set zero matrix
        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)
    # &If it is not a group of photos, it is only zoomed or grayscale converted to 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 geContours(img):
    countors,Hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    for cnt in countors:
        area = cv2.contourArea(cnt)
        print(area)
        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))
            objCor = len(approx)
            x,y,w,h = cv2.boundingRect(approx)
            
            if objCor == 3: objType = "Tri"
            elif objCor == 4:
                aspRatio = w/float(h)
                if aspRatio >0.95 and aspRatio <1.05: objType= "Square"
                else:objType="Rectangle"
            elif objCor>4: objType= "circles"
            else:objType="None"

            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
            cv2.putText(imgContour,objType,
                    (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,
                     (0,0,0),2)


path = 'python/OpenCVTutorial/resources/shapes.png'
img = cv2.imread(path)
imgContour = img.copy()
imgGray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
imgCanny = cv2.Canny(imgBlur,50,50)
geContours(imgCanny)


imgBlank = np.zeros_like(img)
imgStack = stackImages(0.4,([img,imgGray,imgBlur],
                           [imgCanny,imgContour,imgBlank]))


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

You can see that the last picture recognizes the shape