Labeling

# -*- coding: utf-8 -*-
"""
Created on Tue Jun 28 13:21:33 2022
 
@author: Ricore
"""
# Importing the libraries 
import cv2
import numpy as np
from matplotlib import pyplot as plt
 
def showimage(myimage, figsize=[10,10]):
    if (myimage.ndim>2):  #This only applies to RGB or RGBA images (e.g. not to Black and White images)
        myimage = myimage[:,:,::-1] #OpenCV follows BGR order, while matplotlib likely follows RGB order
 
    fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(myimage, cmap = 'gray', interpolation = 'bicubic')
    plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    plt.show()
 
 
def connected_component_label(path):
 
    # Getting the input image
    img = cv2.imread(path, 0)
    # Converting those pixels with values 1-127 to 0 and others to 1
    img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]
    # Applying cv2.connectedComponents() 
    num_labels, labels = cv2.connectedComponents(img)
 
    # Map component labels to hue val, 0-179 is the hue range in OpenCV
    label_hue = np.uint8(179*labels/np.max(labels))
    blank_ch = 255*np.ones_like(label_hue)
    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
 
    # Converting cvt to BGR
    labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
 
    # set bg label to black
    labeled_img[label_hue==0] = 0
 
 
    # Showing Original Image
    showimage(img)
 
    #Showing Image after Component Labeling
    plt.imshow(cv2.cvtColor(labeled_img, cv2.COLOR_BGR2RGB))
    plt.axis('off')
    plt.title("Image after Component Labeling")
    plt.show()
 
connected_component_label('face.jpg')
connected_component_label('crosses.jpg')
connected_component_label('shapes.png')