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        "author": "Matt Yan",
        "author_email": "syan4@ualberta.ca",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "# whichimg\n![travis-badge](https://travis-ci.org/Madoshakalaka/whichimg.svg?branch=master)\n\nblazing fast template matching when possible images are all known. Handy tool for GUI scripting.\n\n\n## How to Use\n\nIt supports python 3.6 +\n\n`pip install whichimg`\n\n\n```python\nfrom whichimg import ImageTeller\n\nteller = ImageTeller([img1, img2, img3, img4]) # numpy arrays\n\nteller.tell(secret_img) # returns 0 or 1 or 2 or 3 or -1 for not found\n\n```\n\nif you're sure `secret_img` is one of the images, you can set keyword argument `surprises` to `False`. This will give you a minor performance gain.\n```python\nteller = ImageTeller([img1, img2, img3, img4], surprises=False)\n```\n\nThis is equivalent to the following naive approach\n\n```python\ndef naive_tell(images, sample_img):\n    for i, img in enumerate(images):\n        if np.array_equal(img, sample_img):\n            return i\n    return -1\n\nnaive_tell([img1, img2, img3, img4], secret_img)\n```\n\n\n\n## Note\n\nIt's **not** generally faster than the naive approach. I thought my approach was faster and spent a week writing this shit though. lmfao.\n\nThey're about equally fast on the `tests/fixtures` testing data I came up with (10x10 images). Through my rough testing, there could be a magnitude of performance gain when there are many possible images (>10) and when the images are larger (200x200 for example).",
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            "Say Thanks!": "https://github.com/Madoshakalaka/whichimg",
            "Source": "https://github.com/Madoshakalaka/whichimg"
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