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https://github.com/osmarks/meme-search-engine.git
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osmarks
7cb42e028f
I decided I wanted to integrate the experimental OCR thing better, so I rewrote in Go and also integrated the thumbnailer. However, Go is a bad langauge and I only used it out of spite. It turned out to have a very hard-to-fix memory leak due to some unclear interaction between libvips and both sets of bindings I tried, so I had Claude-3 transpile it to Rust then spent a while fixing the several mistakes it made and making tweaks. The new Rust version works, although I need to actually do something with the OCR data and make the index queryable concurrently.
101 lines
4.3 KiB
Python
101 lines
4.3 KiB
Python
import pyjson5
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import re
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import asyncio
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import aiohttp
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from PIL import Image
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import time
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import io
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CALLBACK_REGEX = re.compile(r">AF_initDataCallback\(({key: 'ds:1'.*?)\);</script>")
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def encode_img(img):
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image_bytes = io.BytesIO()
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img.save(image_bytes, format="PNG", compress_level=6)
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return image_bytes.getvalue()
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def rationalize_coords_format1(image_w, image_h, center_x_fraction, center_y_fraction, width_fraction, height_fraction, mysterious):
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return {
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"x": round((center_x_fraction - width_fraction / 2) * image_w),
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"y": round((center_y_fraction - height_fraction / 2) * image_h),
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"w": round(width_fraction * image_w),
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"h": round(height_fraction * image_h)
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}
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async def scan_image_chunk(sess, image):
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timestamp = int(time.time() * 1000)
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url = f"https://lens.google.com/v3/upload?stcs={timestamp}"
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headers = {"User-Agent": "Mozilla/5.0 (Linux; Android 13; RMX3771) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.6167.144 Mobile Safari/537.36"}
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cookies = {"SOCS": "CAESEwgDEgk0ODE3Nzk3MjQaAmVuIAEaBgiA_LyaBg"}
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# send data to inscrutable undocumented Google service
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# https://github.com/AuroraWright/owocr/blob/master/owocr/ocr.py#L193
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async with aiohttp.ClientSession() as sess:
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data = aiohttp.FormData()
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data.add_field(
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"encoded_image",
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encode_img(image),
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filename="ocr" + str(timestamp) + ".png",
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content_type="image/png"
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)
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async with sess.post(url, headers=headers, cookies=cookies, data=data, timeout=10) as res:
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body = await res.text()
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# I really worry about Google sometimes. This is not a sensible format.
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match = CALLBACK_REGEX.search(body)
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if match == None:
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raise ValueError("Invalid callback")
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lens_object = pyjson5.loads(match.group(1))
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if "errorHasStatus" in lens_object:
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raise RuntimeError("Lens failed")
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text_segments = []
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text_regions = []
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root = lens_object["data"]
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# I don't know why Google did this.
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# Text segments are in one place and their locations are in another, using a very strange coordinate system.
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# At least I don't need whatever is contained in the base64 parts (which I assume are protobufs).
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# TODO: on a few images, this seems to not work for some reason.
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try:
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text_segments = root[3][4][0][0]
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text_regions = [ rationalize_coords_format1(image.width, image.height, *x[1]) for x in root[2][3][0] if x[11].startswith("text:") ]
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except (KeyError, IndexError):
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# https://github.com/dimdenGD/chrome-lens-ocr/blob/main/src/core.js#L316 has code for a fallback text segment read mode.
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# In testing, this proved unnecessary (quirks of the HTTP request? I don't know), and this only happens on textless images.
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return [], []
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return text_segments, text_regions
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MAX_SCAN_DIM = 1000 # not actually true but close enough
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def chunk_image(image: Image):
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chunks = []
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# Cut image down in X axis (I'm assuming images aren't too wide to scan in downscaled form because merging text horizontally would be annoying)
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if image.width > MAX_SCAN_DIM:
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image = image.resize((MAX_SCAN_DIM, round(image.height * (image.width / MAX_SCAN_DIM))), Image.LANCZOS)
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for y in range(0, image.height, MAX_SCAN_DIM):
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chunks.append(image.crop((0, y, image.width, min(y + MAX_SCAN_DIM, image.height))))
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return chunks
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async def scan_chunks(sess: aiohttp.ClientSession, chunks: [Image]):
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# If text happens to be split across the cut line it won't get read.
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# This is because doing overlap read areas would be really annoying.
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text = ""
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regions = []
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for chunk in chunks:
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new_segments, new_regions = await scan_image_chunk(sess, chunk)
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for segment in new_segments:
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text += segment + "\n"
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for i, (segment, region) in enumerate(zip(new_segments, new_regions)):
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regions.append({ **region, "y": region["y"] + (MAX_SCAN_DIM * i), "text": segment })
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return text, regions
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async def scan_image(sess: aiohttp.ClientSession, image: Image):
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return await scan_chunks(sess, chunk_image(image))
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if __name__ == "__main__":
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async def main():
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async with aiohttp.ClientSession() as sess:
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print(await scan_image(sess, Image.open("/data/public/memes-or-something/linear-algebra-chess.png")))
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asyncio.run(main()) |