added code
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46
ocr.py
Executable file
46
ocr.py
Executable file
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import os
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import cv2
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import easyocr
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def perform_ocr(image_path):
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# Read the image using OpenCV
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img = cv2.imread(image_path)
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# Initialize the OCR reader
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reader = easyocr.Reader(['en']) # Use English language
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# Perform text extraction
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result = reader.readtext(img)
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# Extract the text from the result
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text = "\n".join([entry[1] for entry in result])
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return text
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def main():
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screenshot_dir = 'screenshots'
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output_dir = 'ocr_results'
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# Create output directory if it doesn't exist
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# Process all PNG files in the screenshots directory
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for filename in os.listdir(screenshot_dir):
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if filename.endswith('.png'):
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image_path = os.path.join(screenshot_dir, filename)
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# Perform OCR
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extracted_text = perform_ocr(image_path)
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# Save the extracted text
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output_filename = os.path.splitext(filename)[0] + '.txt'
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output_path = os.path.join(output_dir, output_filename)
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(extracted_text)
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print(f"Processed {filename}, saved result to {output_filename}")
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if __name__ == "__main__":
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main()
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58
screenshot.py
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58
screenshot.py
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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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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 Mean Squared Error between the two images
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mse = np.mean((gray1 - gray2) ** 2)
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# Normalize the difference (assuming max pixel value is 255)
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diff_percent = (mse / (255.0 ** 2)) * 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 > 4.0:
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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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59
updatedscreenshot.py
Executable file
59
updatedscreenshot.py
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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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