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https://github.com/KevinMidboe/immich.git
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619735fea0
* Refactor docker-compose to its own folder * Added FastAPI development environment * Added support for GPU in docker file * Added image classification * creating endpoint for smart Image info * added logo with white background on ios * Added endpoint and trigger for image tagging * Classify image and save into database * Update readme
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
from typing import Optional
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from pydantic import BaseModel
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import numpy as np
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from fastapi import FastAPI
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import tensorflow as tf
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from tensorflow.keras.applications import InceptionV3
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from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
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from tensorflow.keras.preprocessing import image
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IMG_SIZE = 299
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PREDICTION_MODEL = InceptionV3(weights='imagenet')
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def warm_up():
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img_path = f'./app/test.png'
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img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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PREDICTION_MODEL.predict(x)
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# Warm up model
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warm_up()
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app = FastAPI()
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class TagImagePayload(BaseModel):
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thumbnail_path: str
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@app.post("/tagImage")
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async def post_root(payload: TagImagePayload):
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imagePath = payload.thumbnail_path
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if imagePath[0] == '.':
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imagePath = imagePath[2:]
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img_path = f'./app/{imagePath}'
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img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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preds = PREDICTION_MODEL.predict(x)
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result = decode_predictions(preds, top=3)[0]
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payload = []
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for _, value, _ in result:
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payload.append(value)
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return payload
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