58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
import os
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from flask import Flask, request, render_template
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from timelapse import process_faces
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app = Flask(__name__)
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# Critical parameters provided via environment variables
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API_KEY = os.environ.get("IMMICH_API_KEY", "")
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BASE_URL = os.environ.get("IMMICH_BASE_URL", "")
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OUTPUT_FOLDER = os.environ.get("OUTPUT_FOLDER", "output")
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# model paths
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FACE_DETECT_MODEL = "mmod_human_face_detector.dat"
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LANDMARK_MODEL = "shape_predictor_68_face_landmarks.dat"
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@app.route("/", methods=["GET", "POST"])
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def index():
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result = None
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error = None
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if request.method == "POST":
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try:
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api_key = API_KEY
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base_url = BASE_URL
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output_folder = OUTPUT_FOLDER
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person_id = request.form["person_id"]
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padding_percent = float(request.form.get("padding_percent", 0.3))
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resize_size = int(request.form.get("resize_size", 512))
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face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128))
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pose_threshold = float(request.form.get("pose_threshold", 25))
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desired_left_eye_x = float(request.form.get("desired_left_eye_x", 0.35))
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desired_left_eye_y = float(request.form.get("desired_left_eye_y", 0.45))
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max_workers = int(request.form.get("max_workers", 4))
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processed_files = process_faces(
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api_key=api_key,
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base_url=base_url,
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person_id=person_id,
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output_folder=output_folder,
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padding_percent=padding_percent,
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resize_width=resize_size,
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resize_height=resize_size,
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min_face_width=face_resolution_threshold,
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min_face_height=face_resolution_threshold,
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pose_threshold=pose_threshold,
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desired_left_eye=(desired_left_eye_x, desired_left_eye_y),
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max_workers=max_workers,
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face_detect_model_path=FACE_DETECT_MODEL,
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landmark_model_path=LANDMARK_MODEL
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)
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result = f"Finished processing. {len(processed_files)} images saved in '{output_folder}'."
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except Exception as e:
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error = f"Error processing request: {e}"
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return render_template("index.html", result=result, error=error)
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000)
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