87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
import os
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import multiprocessing
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import threading
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from flask import Flask, request, render_template, jsonify
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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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LEFT_EYE_POS = (0.35, 0.45)
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AVAILABLE_CORES = multiprocessing.cpu_count()
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# Global progress dictionary – only one job at a time is assumed here
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progress_info = {"completed": 0, "total": 0, "status": "idle"}
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def update_progress(current, total):
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progress_info["completed"] = current
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progress_info["total"] = total
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progress_info["status"] = "running" if current < total else "done"
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def background_process(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers):
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try:
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progress_info["status"] = "running"
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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=LEFT_EYE_POS,
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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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progress_callback=update_progress
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)
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except Exception as e:
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progress_info["status"] = f"error: {e}"
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else:
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progress_info["status"] = "done"
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@app.route("/progress")
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def progress():
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return jsonify(progress_info)
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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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# Create max_workers_options as a list from 1 to AVAILABLE_CORES
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max_workers_options = list(range(1, AVAILABLE_CORES + 1))
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if request.method == "POST":
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try:
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person_id = request.form["person_id"]
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padding_percent = float(request.form.get("padding_percent", 30)) / 100
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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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max_workers = int(request.form.get("max_workers", 1)) # default is 1
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# Reset progress info before starting
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progress_info["completed"] = 0
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progress_info["total"] = 0
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progress_info["status"] = "idle"
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# Start the processing in a background thread
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threading.Thread(
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target=background_process,
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args=(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers)
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).start()
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result = "Processing started. Please wait and watch the progress bar below."
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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, max_workers_options=max_workers_options)
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000)
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