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| import os | |
| from glob import glob | |
| import re | |
| import numpy as np | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| %matplotlib inline | |
| # get properly sorted images | |
| data_path = r'E:\bg-sugar' | |
| image_paths = sorted(glob(f"{data_path}/*.png"), key=lambda x:float(re.findall("(\d+)",x)[0])) | |
| def get_mask(frame1, frame2, kernel=np.array((9,9), dtype=np.uint8)): | |
| """ Obtains image mask | |
| Inputs: | |
| frame1 - Grayscale frame at time t | |
| frame2 - Grayscale frame at time t + 1 | |
| kernel - (NxN) array for Morphological Operations | |
| Outputs: | |
| mask - Thresholded mask for moving pixels | |
| """ | |
| frame_diff = cv2.subtract(frame2, frame1) | |
| # blur the frame difference | |
| frame_diff = cv2.medianBlur(frame_diff, 3) | |
| mask = cv2.adaptiveThreshold(frame_diff, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\ | |
| cv2.THRESH_BINARY_INV, 11, 3) | |
| mask = cv2.medianBlur(mask, 3) | |
| # morphological operations | |
| mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1) | |
| return mask | |
| frame = 181 | |
| for idx in range(frame-60, frame): | |
| img1_rgb = cv2.cvtColor(cv2.imread(image_paths[idx]), cv2.COLOR_BGR2RGB) | |
| img2_rgb = cv2.cvtColor(cv2.imread(image_paths[idx+1]), cv2.COLOR_BGR2RGB) | |
| # convert to grayscale | |
| img1 = cv2.cvtColor(img1_rgb, cv2.COLOR_RGB2GRAY) | |
| img2 = cv2.cvtColor(img2_rgb, cv2.COLOR_RGB2GRAY) | |
| # compute grayscale image difference | |
| grayscale_diff = cv2.subtract(img2, img1) | |
| kernel = np.array((9,9), dtype=np.uint8) | |
| mask = get_mask(img1, img2, kernel) | |
| fig, ax = plt.subplots(1, 3, figsize=(25, 25)) | |
| ax[0].imshow(img2_rgb) | |
| ax[0].set_title(f'Frame {idx+1}') | |
| ax[1].imshow(grayscale_diff*50) # scale the frame difference to show the noise | |
| ax[1].set_title(f'Frame Difference {idx+1}-{idx}') | |
| ax[2].imshow(mask, cmap='gray') | |
| ax[2].set_title("Motion Mask") |
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