1. POI area: area of interest
2. Edge padding
3. Numerical operations
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from PIL import Image
image1 = mpimg.imread('1.jpg')
image2 = mpimg.imread('2.jpg')
plt.imshow(image1)
plt.imshow(image2)
image addition
image3 = image1+image2
plt.imshow(image3)
image cropping
plt.imshow(image1[100:-100, 100:-100, :]);
image scaling
fx and fy represent the zoom factor
image4 = cv2.resize(image1, (0, 0), fx=3, fy=1)
plt.imshow(image4);
4. Image Thresholding
ret, dst = cv2.threshold(src, thresh, maxval, type)
- src: input image, only single channel image
- dst: output map
- thresh: threshold
- maxval: the value when the pixel value exceeds the threshold or is less than the threshold
- type: the type of binarization
- cv2.THRESH_BINARY takes maxval if it exceeds the threshold, otherwise takes 0
- cv2.THRESH_BINARY_INV Inversion of THRESH_BINARY
- cv2.THRESH_TRUNC is greater than the threshold setting threshold, the rest remain unchanged
- cv2.THRESH_TOZERO is greater than the threshold unchanged, and the rest are set to 0
- cv2.THRESH_TOZERO_INV Inversion of THRESH_TOZERO
image1_gray = image1[:,:,0]
res, thresh1 = cv2.threshold(image1_gray, 127, 255, cv2.THRESH_BINARY)
res, thresh2 = cv2.threshold(image1_gray, 127, 255, cv2.THRESH_BINARY_INV)
res, thresh3 = cv2.threshold(image1_gray, 127, 255, cv2.THRESH_TRUNC)
res, thresh4 = cv2.threshold(image1_gray, 127, 255, cv2.THRESH_TOZERO)
res, thresh5 = cv2.threshold(image1_gray, 127, 255, cv2.THRESH_TOZERO_INV)
titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [image1_gray, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
plt.subplot(2,3,i+1)
plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([])
plt.yticks([])
plt.show()
