45 Countour and its functions.py

"""
Created on Mon Jan 24 18:09:28 2022

@author: 
    
                ---Contours---
                
Contours can be explained simply as a curve joining all the continuous points 
(along the boundary), having same color or intensity. 

The contours are a useful tool for shape analysis and object detection and recognition

For better accuracy, use binary images and also apply edge detection before 
findig countours.

findCountour function manipulate original imge so copy it before proceeding.
findContour is like finding white object from black background.
so you must turn image in white and background is black.

We have to find and draw contours as per the requirement
    
"""

import cv2
import numpy as np

img = cv2.imread("D:\\music\\shapes.png")
img = cv2.resize(img,(600,700))
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

ret,thresh = cv2.threshold(img1,220,255,cv2.THRESH_BINARY_INV)

#findcontour(img,contour_retrival_mode,method)
cnts,hier = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#Here cnts is a list of contours. ANd each contour is an array with x, y cordinate   
#hier variable called hierarchy and it contain image information.
print("Number of contour==",cnts,"\ntotal contour==",len(cnts))
print("Hierarchy==\n",hier)

#img = cv2.drawContours(img, cnts, -1, (113,45,67), 4)
# loop over the contours
for c in cnts:
    # compute the center of the contour
    #an image moment is a certain particular weighted average (moment) of the image pixels
    M = cv2.moments(c)
    print("M==",M)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    # draw the contour and center of the shape on the image
    cv2.drawContours(img, [c], -1, (0, 255, 0), 2)
    cv2.circle(img, (cX, cY), 7, (255, 255, 255), -1)
    cv2.putText(img, "center", (cX - 20, cY - 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
    

#Outputs

cv2.imshow("img", img)
cv2.imshow("Gray_img", img1)
cv2.imshow("thresh==",thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()