# From Selection and Masking Lecture import numpy as np import cv2 import scipy.linalg as la import matplotlib.pyplot as plt import sys import tqdm img_raw = cv2.imread("/work/2x2Rubiks_1.png") print(img_raw.shape, img_raw.dtype) img_rgb = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB) img_selected = img_rgb[850:1600, 100:750, :] img_masking = np.logical_and(np.logical_and(img_rgb[:, :, 0] > 200, img_rgb[:, :, 2] < 100), img_rgb[:, :, 1] < 100) plt.imshow(img_selected) plt.figure() plt.imshow(img_masking) plt.figure() img_new = img_rgb.copy() img_new[img_masking, :] = (0, 0, 0) plt.imshow(img_new) plt.figure() img_selected[:,:,0] = 0 plt.imshow(img_rgb) # dtypes # uint8: 0 - 255 total of 256 = 2^8 # float32: 0 - 1, fraction values, can store negative # bool: 0 or 1, indicates false or true

import numpy as np import cv2 import scipy.linalg as la import matplotlib.pyplot as plt import sys import tqdm img_bg = cv2.cvtColor(cv2.imread("/work/2x2Rubiks_BG.png"), cv2.COLOR_BGR2RGB) plt.figure() plt.imshow(img_bg) img_fg = cv2.cvtColor(cv2.imread("/work/2x2Rubiks_2.png"), cv2.COLOR_BGR2RGB) plt.figure() plt.imshow(img_fg) img_sub = np.sqrt(np.sum((img_fg.astype(np.float32) - img_bg.astype(np.float32))**2, -1)) plt.figure() plt.imshow(img_sub) img_new = img_fg.copy() img_new[img_sub < threshold, :] = (0, 0, 0) plt.figure(figsize=(30,15)) plt.imshow(img_new)