danvics/immich-automated-selfie-timelapse
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Simplified process parameters

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This commit is contained in:
Arnaud_Cayrol 2025-04-12 14:45:22 +02:00
parent 2bfc68c520
commit 9df9d7519c
2 changed files with 43 additions and 91 deletions
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86
main.py
View file

@ -2,13 +2,11 @@ import logging
import multiprocessing
import os
import threading
import uuid
from dataclasses import dataclass
from datetime import datetime
from typing import Callable, Dict, List, Optional, Tuple
from typing import Callable, Dict, List, Tuple
from flask import Flask, jsonify, render_template, request
from timelapse import ProcessConfig, process_faces, validate_immich_connection
from timelapse import process_faces, validate_immich_connection
# Configure logging
logger = logging.getLogger(__name__)
@ -27,16 +25,15 @@ class AppConfig:
"""Configuration for the application."""
api_key: str = os.environ.get("IMMICH_API_KEY", "")
base_url: str = os.environ.get("IMMICH_BASE_URL", "")
person_id: str = None
output_folder: str = "output"
landmark_model: str ="shape_predictor_68_face_landmarks.dat"
default_resize_size: int = 512
default_face_resolution_threshold: int = 128
default_pose_threshold: float = 25.0
default_left_eye_pos: Tuple[float, float] = (0.35, 0.4)
default_framerate: int = 24
default_date_format: str = "%Y-%m-%d"
landmark_model: str = "shape_predictor_68_face_landmarks.dat"
resize_size: int = 512
face_resolution_threshold: int = 128
pose_threshold: float = 25.0
left_eye_pos: Tuple[float, float] = (0.35, 0.4)
date_from: str = None
date_to: str = None
# Initialize Flask app
app = Flask(__name__)
@ -44,7 +41,7 @@ app = Flask(__name__)
# Global state
AVAILABLE_CORES = multiprocessing.cpu_count()
progress_info: Dict[str, any] = {"completed": 0, "total": 0, "status": "idle"}
processing_thread: Optional[threading.Thread] = None
processing_thread: threading.Thread = None
cancel_requested: bool = False
config = AppConfig()
@ -74,26 +71,12 @@ def check_output_folder() -> Tuple[bool, int]:
return len(files) == 0, len(files)
def background_process(
person_id: str,
resize_size: int,
face_resolution_threshold: int,
pose_threshold: float,
left_eye_pos: Tuple[float, float],
date_from: Optional[str] = None,
date_to: Optional[str] = None,
progress_callback: Optional[Callable] = None,
cancel_flag: Optional[Callable] = None
progress_callback: Callable = None,
cancel_flag: Callable = None
) -> List[str]:
"""Process faces in the background.
Args:
person_id: ID of the person to process
resize_size: Size to resize output images to
face_resolution_threshold: Minimum face resolution threshold
pose_threshold: Maximum allowed head pose deviation
left_eye_pos: Desired position of the left eye in output
date_from: Optional start date in YYYY-MM-DD format
date_to: Optional end date in YYYY-MM-DD format
progress_callback: Optional callback for progress updates
cancel_flag: Optional function to check for cancellation
@ -101,24 +84,8 @@ def background_process(
List of processed file paths
"""
try:
process_config = ProcessConfig(
api_key=config.api_key,
base_url=config.base_url,
person_id=person_id,
output_folder=config.output_folder,
resize_width=resize_size,
resize_height=resize_size,
min_face_width=face_resolution_threshold,
min_face_height=face_resolution_threshold,
pose_threshold=pose_threshold,
left_eye_pos=left_eye_pos,
landmark_model_path=config.landmark_model,
date_from=date_from,
date_to=date_to
)
return process_faces(
config=process_config,
config=config,
max_workers=1,
progress_callback=progress_callback,
cancel_flag=cancel_flag
@ -178,20 +145,17 @@ def index() -> str:
try:
cancel_requested = False
# Get form data with defaults
person_id = request.form["person_id"]
resize_size = int(request.form.get("resize_size", config.default_resize_size))
face_resolution_threshold = int(request.form.get("face_resolution_threshold",
config.default_face_resolution_threshold))
pose_threshold = float(request.form.get("pose_threshold", config.default_pose_threshold))
# Optional date ranges
date_from = request.form.get("date_from") or None
date_to = request.form.get("date_to") or None
# Get form data
config.person_id = request.form["person_id"]
config.resize_size = int(request.form.get("resize_size"))
config.face_resolution_threshold = int(request.form.get("face_resolution_threshold"))
config.pose_threshold = float(request.form.get("pose_threshold"))
config.date_from = request.form.get("date_from")
config.date_to = request.form.get("date_to")
# Video compilation options
compile_video = request.form.get("compile_video") == "on"
framerate = int(request.form.get("framerate", config.default_framerate))
framerate = int(request.form.get("framerate", 15))
# Reset progress info
progress_info.update({
@ -204,10 +168,8 @@ def index() -> str:
# Start processing
processing_thread = threading.Thread(
target=background_process,
args=(person_id, resize_size, face_resolution_threshold, pose_threshold,
config.default_left_eye_pos, config.output_folder, date_from, date_to,
update_progress, lambda: cancel_requested)
)
args=(update_progress, lambda: cancel_requested)
)
processing_thread.start()
result = "Processing started. Please wait and watch the progress bar below."

48
timelapse.py
View file

@ -11,7 +11,7 @@ import cv2
import dlib
from tqdm import tqdm
import logging
from typing import Tuple
class TqdmLoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
@ -36,21 +36,19 @@ face_predictor = None
@dataclass
class ProcessConfig:
"""
Dataclass to hold configuration parameters for processing assets.
"""
class AppConfig:
"""Configuration for the application."""
api_key: str
base_url: str
person_id: str
output_folder: str = "output"
resize_width: int = 512
resize_height: int = 512
min_face_width: int = 128
min_face_height: int = 128
pose_threshold: float = 25
left_eye_pos: tuple = (0.35, 0.4)
landmark_model_path: str = "shape_predictor_68_face_landmarks.dat"
output_folder: str
landmark_model: str
resize_size: int
face_resolution_threshold: int
pose_threshold: float
left_eye_pos: Tuple[float, float]
date_from: str
date_to: str
def draw_landmarks(image, landmarks, face_rect, output_path):
"""
@ -486,7 +484,7 @@ def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold
return None
def process_asset_worker(asset, config: ProcessConfig):
def process_asset_worker(asset, config: AppConfig):
"""
Worker function to process a single asset.
@ -495,14 +493,13 @@ def process_asset_worker(asset, config: ProcessConfig):
Args:
asset (dict): The asset metadata.
config (ProcessConfig): Configuration parameters.
config (AppConfig): Configuration parameters.
Returns:
str or None: The file path of the saved image if processing is successful; otherwise None.
"""
try:
asset_id = asset['id']
image_bytes = download_asset(config.api_key, config.base_url, asset_id)
image = Image.open(io.BytesIO(image_bytes))
image = ImageOps.exif_transpose(image)
@ -514,12 +511,11 @@ def process_asset_worker(asset, config: ProcessConfig):
matching_person = next((p for p in asset.get('people', []) if p.get('id') == config.person_id), None)
face_data = matching_person.get('faces', [])[0]
aligned_face = crop_and_align_face(
image,
face_data,
resize_size=config.resize_width,
face_resolution_threshold=config.min_face_width,
resize_size=config.resize_size,
face_resolution_threshold=config.face_resolution_threshold,
pose_threshold=config.pose_threshold,
left_eye_pos=config.left_eye_pos
)
@ -527,15 +523,13 @@ def process_asset_worker(asset, config: ProcessConfig):
if aligned_face is None:
return None
os.makedirs(config.output_folder, exist_ok=True)
dt = datetime.fromisoformat(asset['fileCreatedAt'].replace("Z", "+00:00"))
timestamp = dt.strftime("%Y%m%d_%H%M%S")
filename = os.path.join(config.output_folder, f"{timestamp}.jpg")
aligned_face.save(filename)
return filename
def process_faces(config: ProcessConfig, max_workers=1, progress_callback=None, date_from=None, date_to=None,
cancel_flag=None):
def process_faces(config: AppConfig, max_workers=1, progress_callback=None, cancel_flag=None):
"""
Processes assets containing the person and saves aligned face images.
@ -543,23 +537,19 @@ def process_faces(config: ProcessConfig, max_workers=1, progress_callback=None,
concurrently download, crop, and align faces.
Args:
config (ProcessConfig): Configuration parameters.
config (AppConfig): Configuration parameters.
max_workers (int): Number of worker processes.
progress_callback (callable, optional): A callback function for progress updates.
date_from (str, optional): Start date for filtering assets.
date_to (str, optional): End date for filtering assets.
cancel_flag (callable, optional): A function that returns True if processing should be cancelled.
Returns:
list: A list of file paths of the saved images.
"""
os.makedirs(config.output_folder, exist_ok=True)
if cancel_flag and cancel_flag():
logger.info("Processing was cancelled.")
return []
assets = get_assets_with_person(config.api_key, config.base_url, config.person_id, date_from, date_to)
assets = get_assets_with_person(config.api_key, config.base_url, config.person_id, config.date_from, config.date_to)
logger.info(f"Found {len(assets)} assets containing the person.")
total_assets = len(assets)
@ -568,7 +558,7 @@ def process_faces(config: ProcessConfig, max_workers=1, progress_callback=None,
processed_files = []
completed_count = 0
initializer_args = (config.landmark_model_path,)
initializer_args = (config.landmark_model)
with concurrent.futures.ProcessPoolExecutor(
max_workers=max_workers,
initializer=initialize_worker,