60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
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import time
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import numpy as np
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from PIL import Image
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import mss
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import cv2
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import os
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from skimage.metrics import structural_similarity as ssim
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def take_screenshot():
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with mss.mss() as sct:
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monitor = sct.monitors[0]
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screenshot = sct.grab(monitor)
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img = Image.frombytes("RGB", screenshot.size, screenshot.rgb)
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return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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def compare_images(img1, img2):
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if img1 is None or img2 is None:
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return 100.0
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# Convert images to grayscale
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gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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# Compute the Structural Similarity Index (SSIM) between the two images
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score, _ = ssim(gray1, gray2, full=True)
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# The SSIM score is a value between -1 and 1, where 1 means identical image
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# We'll convert it to a percentage difference
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diff_percent = (1 - score) * 100
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return diff_percent
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def main():
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last_saved_image = None
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screenshot_count = 0
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if not os.path.exists('screenshots'):
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os.makedirs('screenshots')
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while True:
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current_image = take_screenshot()
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diff_percent = compare_images(current_image, last_saved_image)
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if diff_percent > 15.0: # Lowered threshold for increased sensitivity
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screenshot_count += 1
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filename = f'screenshots/screenshot_{screenshot_count}.png'
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cv2.imwrite(filename, current_image)
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print(f"Saved {filename} (Difference: {diff_percent:.2f}%)")
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last_saved_image = current_image
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else:
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print(f"Screenshot not saved (Difference: {diff_percent:.2f}%)")
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time.sleep(5)
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if __name__ == "__main__":
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main()
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