pip install opencv-python
pip install numpy
pip install matplotlib
main.py
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import numpy as np import cv2 import matplotlib.pyplot as plt # read original image image = cv2.imread("c1.png") # convert to gray scale image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imwrite('gray.png', gray) # apply median filter for smoothing blurM = cv2.medianBlur(gray, 5) cv2.imwrite('blurM.png', blurM) # apply gaussian filter for smoothing blurG = cv2.GaussianBlur(gray, (9, 9), 0) cv2.imwrite('blurG.png', blurG) # histogram equalization histoNorm = cv2.equalizeHist(gray) cv2.imwrite('histoNorm.png', histoNorm) # create a CLAHE object for # Contrast Limited Adaptive Histogram Equalization (CLAHE) clahe = cv2.createCLAHE(clipLimit = 2.0, tileGridSize=(8, 8)) claheNorm = clahe.apply(gray) cv2.imwrite('claheNorm.png', claheNorm) # contrast stretching # Function to map each intensity level to output intensity level. def pixelVal(pix, r1, s1, r2, s2): if (0 <= pix and pix <= r1): return (s1 / r1) * pix elif (r1 < pix and pix <= r2): return ((s2 - s1) / (r2 - r1)) * (pix - r1) + s1 else: return ((255 - s2) / (255 - r2)) * (pix - r2) + s2 # Define parameters. r1 = 70 s1 = 0 r2 = 200 s2 = 255 # Vectorize the function to apply it to each value in the Numpy array. pixelVal_vec = np.vectorize(pixelVal) # Apply contrast stretching. contrast_stretched = pixelVal_vec(gray, r1, s1, r2, s2) contrast_stretched_blurM = pixelVal_vec(blurM, r1, s1, r2, s2) cv2.imwrite('contrast_stretch.png', contrast_stretched) cv2.imwrite('contrast_stretch_blurM.png', contrast_stretched_blurM) # edge detection using canny edge detector edge = cv2.Canny(gray, 100, 200) cv2.imwrite('edge.png', edge) edgeG = cv2.Canny(blurG, 100, 200) cv2.imwrite('edgeG.png', edgeG) edgeM = cv2.Canny(blurM, 100, 200) cv2.imwrite('edgeM.png', edgeM) |
