{"id":15253595,"url":"https://github.com/hansalemaos/tmplmatching","last_synced_at":"2026-01-03T17:49:31.890Z","repository":{"id":191444095,"uuid":"684825642","full_name":"hansalemaos/tmplmatching","owner":"hansalemaos","description":"Fast multitemplate matching with support for different sizes using numexpr / cupy / fftw / scipy / 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Fast multitemplate matching with support for different sizes using numexpr / cupy / fftw / scipy / multiprocessing\r\n\r\n## pip install tmplmatching\r\n\r\nTutorial (Brazilian Portugues)\r\n\r\n[![YT](https://i.ytimg.com/vi/KXYFTgS_A1Q/maxresdefault.jpg)](https://www.youtube.com/watch?v=KXYFTgS_A1Q)\r\n[https://www.youtube.com/watch?v=KXYFTgS_A1Q]()\r\n\r\n### Advantages:\r\n\r\n- Uses a tuned match_template algorithm from scipy (numexpr instead of numpy / different fftconvolve algorithms)\r\n- very fast fftconvolve function using pyfftw (alternatively cupy and scipy.signal.fftconvolve)\r\n- Multiprocessing using RawArray to speed it up / save memory \r\n- Template matching with multiple sizes \r\n- Results as a pandas DataFrame for further filtering\r\n- Adjustable accepted R, G, B difference \r\n- Finding the best matches among multiple matches  \r\n\r\n\r\n### Tested against Windows 10 / Python 3.10 / Anaconda - should work on MacOS / Linux\r\n\r\n## Selecting \r\n\r\n![pic1](https://github.com/hansalemaos/screenshots/blob/main/multitemplate/tag.png?raw=true)\r\n\r\n## Locating \r\n\r\n![pic2](https://github.com/hansalemaos/screenshots/blob/main/multitemplate/results.png?raw=true)\r\n\r\n```python\r\nimport cv2\r\nfrom tmplmatching import TemplateMatching\r\n\r\nif __name__ == \"__main__\": # important - since it uses multiprocessing, this line is mandatory!\r\n    ############################################################\r\n    # Step 1:\r\n    # Initialize TemplateMatching\r\n    self = TemplateMatching(screenshot_buffer=10, use_cupy=False)\r\n\r\n    ############################################################\r\n    # Step 2:\r\n    # Create a class list in ALPHABETICAL order, put one class per line and save the file:\r\n    # Here is an example:\r\n    \"\"\"\r\n    # social_media_icons.txt\r\n    facebookicon\r\n    tiktokicon\r\n    whatsappicon\r\n    youtubeicon\r\n    \"\"\"\r\n    # and save it as a TXT file\r\n    ############################################################\r\n    # Step 3:\r\n    # Start annotation tool (https://github.com/drainingsun/ybat - included in this package) which captures a screenshot, and shows the path to the screenshot (has to be done only once)\r\n    self.start_annotation_tool()\r\n    ############################################################\r\n    # Step 4:\r\n    # open the text file and the screenshot (or any other image) in the annotation tool, and select the items you want to find\r\n    ############################################################\r\n    # Step 5:\r\n    # Click on \"Save COCO\" when you are done\r\n    # Copy the path of the downloaded zip file and the path of the image\r\n    # \"C:\\Users\\hansc\\Downloads\\bboxes_coco.zip\"\r\n    ############################################################\r\n    # Step 6:\r\n    # Once the annotation is done, extract and resize items based on annotations (has to be done only once)\r\n    self.extract_images_from_annotations(\r\n         folder=r\"c:\\socialmediaicons_different_sizes\",  # The folder to save extracted templates\r\n         zipfile=r\"C:\\Users\\hansc\\Downloads\\bboxes_coco.zip\",  # Path to saved annotations\r\n         screenshot=r\"C:\\Users\\hansc\\AppData\\Local\\Temp\\tmp1ak8brdw.png\",  # The picture that you used in self.start_annotation_tool()\r\n         min_percentage=10,  # Defines variations for each template\r\n         max_percentage=200,\r\n         interpolation=cv2.INTER_LANCZOS4 # recommended for the best results\r\n     )\r\n    ############################################################\r\n    # Step 7:    \r\n    # Load template images with specified size ranges\r\n    self.load_needle_images(\r\n        folder=r\"c:\\socialmediaicons_different_sizes\",\r\n        min_percentage=25,\r\n        max_percentage=35,\r\n    )\r\n    ############################################################\r\n    # Step 8:   \r\n    df = self.take_screenshot_and_find_needles(\r\n        minimum_match=0.60,  # Minimum threshold for template matching - the lower you go, the longer the calculations takes\r\n        monitor=1,  # 0 for all monitors\r\n        percent=30,  # Resize screenshots before matching (results are recalculated to the original size)\r\n        processes=5,  # Number of CPUs used for multiprocessing\r\n        interpolation=cv2.INTER_LANCZOS4,  # Interpolation method for resizing the screenshot - Use cv2.INTER_LANCZOS4 for the best results (but a little slower than cv2.INTER_AREA)\r\n        max_diff_r=80,  # Maximal accepted RGB difference filter to reduce false positives\r\n        max_diff_g=80,\r\n        max_diff_b=80,\r\n        max_color_difference_factor=2, \r\n\r\n    )\r\n    \r\n    ima = self.draw_results(df)  # Pass the DataFrame to draw the results\r\n    cv2.imwrite(r\"c:\\socialmediaiconsmatching.png\", ima)\r\n    # --------------------------------------\r\n    # If you don't want to take a screenshot and want to pass an existing picture, use: \r\n    df2 = self.find_needles(\r\n        haystack=r\"C:\\Users\\hansc\\AppData\\Local\\Temp\\tmp1ak8brdw.png\", # accepts almost all image formats (base64, file path, urls, PIL, numpy, bytes ...)\r\n        percent=30,\r\n        minimum_match=0.7,\r\n        processes=4,\r\n        max_diff_r=30,\r\n        max_diff_g=30,\r\n        max_diff_b=30,\r\n        max_color_difference_factor=2,\r\n        interpolation=cv2.INTER_LANCZOS4,\r\n    )\r\n    ima2 = self.draw_results(\r\n        df2, haystack=r\"C:\\Users\\hansc\\AppData\\Local\\Temp\\tmp1ak8brdw.png\"\r\n    )  # Pass the DataFrame to draw the last results\r\n    cv2.imwrite(\"c:\\\\socialmediaiconsmatching2.png\", ima2)\r\n    \r\n                                                                                                                                                                                                                                              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