import numpy as np import cv2 import scipy.linalg as la import matplotlib.pyplot as plt import sys import tqdm # Good resources: # https://docs.opencv.org/3.4/dd/d1a/group__imgproc__feature.html#ga04723e007ed888ddf11d9ba04e2232de # https://docs.opencv.org/3.4/da/d22/tutorial_py_canny.html # https://theailearner.com/tag/hysteresis-thresholding/

img = cv2.cvtColor(cv2.imread("/work/2x2Rubiks_2.png"), cv2.COLOR_BGR2GRAY)[400:850:4, 300:750:4] fig, ax = plt.subplots(2, 3, figsize=(7*3,7*2)) ax[0,0].imshow(img, cmap='gray') # https://docs.opencv.org/3.4/d4/d86/group__imgproc__filter.html#gacea54f142e81b6758cb6f375ce782c8d imgf = img.astype(np.float32)/255 Gx = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32)) Gy = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32).T) Gmag = np.sqrt(Gx**2 + Gy**2) ax[0,1].imshow(Gmag, cmap='gray') ax[0,2].imshow(Gmag > (thresh/100), cmap='gray') gx_ax = ax[1,0].imshow(Gx) fig.colorbar(gx_ax, ax=ax[1,0], orientation='vertical') gy_ax = ax[1,1].imshow(Gy) fig.colorbar(gy_ax, ax=ax[1,1], orientation='vertical') img_noise = (255*np.clip(imgf + (noise/255)*np.random.rand(*imgf.shape), 0, 1)).astype(np.uint8) fig, ax = plt.subplots(2, 3, figsize=(7*3,7*2)) ax[0,0].imshow(img_noise, cmap='gray') imgf = img_noise.astype(np.float32)/255 Gx = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32)) Gy = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32).T) Gmag = np.sqrt(Gx**2 + Gy**2) ax[0,1].imshow(Gmag, cmap='gray') ax[0,2].imshow(Gmag > (thresh/100), cmap='gray') gx_ax = ax[1,0].imshow(Gx) fig.colorbar(gx_ax, ax=ax[1,0], orientation='vertical') gy_ax = ax[1,1].imshow(Gy) fig.colorbar(gy_ax, ax=ax[1,1], orientation='vertical') print(sigma/100) img_filt = cv2.GaussianBlur(img_noise, (5, 5), (sigma/10)) fig, ax = plt.subplots(2, 3, figsize=(7*3,7*2)) ax[0,0].imshow(img_filt, cmap='gray') imgf = img_filt.astype(np.float32)/255 Gx = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32)) Gy = cv2.filter2D(imgf, cv2.CV_32F, np.array([[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]], dtype=np.float32).T) Gmag = np.sqrt(Gx**2 + Gy**2) ax[0,1].imshow(Gmag, cmap='gray') ax[0,2].imshow(Gmag > (thresh/100), cmap='gray') gx_ax = ax[1,0].imshow(Gx) fig.colorbar(gx_ax, ax=ax[1,0], orientation='vertical') gy_ax = ax[1,1].imshow(Gy) fig.colorbar(gy_ax, ax=ax[1,1], orientation='vertical')

img = cv2.cvtColor(cv2.imread("/work/2x2Rubiks_2.png"), cv2.COLOR_BGR2GRAY)[400:850:4, 300:750:4] fig, ax = plt.subplots(1, 3, figsize=(7*3,11)) ax[0].imshow(img, cmap='gray') # https://docs.opencv.org/3.4/d4/d86/group__imgproc__filter.html#gacea54f142e81b6758cb6f375ce782c8d imgf = img.astype(np.float32)/255 Gx = cv2.Sobel(imgf, cv2.CV_32F, 1, 0) Gy = cv2.Sobel(imgf, cv2.CV_32F, 0, 1) ax[1].imshow(np.sqrt(Gx**2 + Gy**2), cmap='gray') ax[2].imshow(cv2.Canny(img, thresh1, thresh2), cmap='gray')