To segment the image using global thresholding, adaptive thresholding and Otsu's thresholding using python and OpenCV.
- Anaconda - Python 3.7
- OpenCV
Load the necessary packages.
Read the Image and convert to grayscale.
Use Global thresholding to segment the image.
Use Adaptive thresholding to segment the image.
Use Otsu's method to segment the image and display the results.
python import numpy as np import matplotlib.pyplot as plt import cv2
python image = cv2.imread('pandaa.jpg',1) image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB) image_gray = cv2.imread('pandaa.jpg',0)
python ret,thresh_img1=cv2.threshold(image_gray,86,255,cv2.THRESH_BINARY) ret,thresh_img2=cv2.threshold(image_gray,86,255,cv2.THRESH_BINARY_INV) ret,thresh_img3=cv2.threshold(image_gray,86,255,cv2.THRESH_TOZERO) ret,thresh_img4=cv2.threshold(image_gray,86,255,cv2.THRESH_TOZERO_INV) ret,thresh_img5=cv2.threshold(image_gray,100,255,cv2.THRESH_TRUNC)
python thresh_img7=cv2.adaptiveThreshold(image_gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2) thresh_img8=cv2.adaptiveThreshold(image_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
python ret,thresh_img6=cv2.threshold(image_gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
python titles=["Gray Image","Threshold Image (Binary)","Threshold Image (Binary Inverse)","Threshold Image (To Zero)" ,"Threshold Image (To Zero-Inverse)","Threshold Image (Truncate)","Otsu","Adaptive Threshold (Mean)","Adaptive Threshold (Gaussian)"] images=[image_gray,thresh_img1,thresh_img2,thresh_img3,thresh_img4,thresh_img5,thresh_img6,thresh_img7,thresh_img8] for i in range(0,9): plt.figure(figsize=(10,10)) plt.subplot(1,2,1) plt.title("Original Image") plt.imshow(image) plt.axis("off") plt.subplot(1,2,2) plt.title(titles[i]) plt.imshow(cv2.cvtColor(images[i],cv2.COLOR_BGR2RGB)) plt.axis("off") plt.show()
Thus the images are segmented using global thresholding, adaptive thresholding and optimum global thresholding using python and OpenCV.