danvics/immich-automated-selfie-timelapse
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Zoom out a bit on final image, disable /progress route spam

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Arnaud_Cayrol 2025-04-11 20:43:15 +02:00
parent 32c74cc627
commit 24a4727dd1
2 changed files with 11 additions and 11 deletions
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15
main.py
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@ -4,6 +4,7 @@ import threading
import subprocess import subprocess
from flask import Flask, request, render_template, jsonify, redirect, url_for from flask import Flask, request, render_template, jsonify, redirect, url_for
from timelapse import process_faces, ProcessConfig, validate_immich_connection from timelapse import process_faces, ProcessConfig, validate_immich_connection
import logging
app = Flask(__name__) app = Flask(__name__)
@ -24,6 +25,13 @@ processing_thread = None
# Global flag to signal cancellation # Global flag to signal cancellation
cancel_requested = False cancel_requested = False
class ProgressRouteFilter(logging.Filter):
def filter(self, record):
# Filter out logs containing the progress route
return "/progress" not in record.getMessage()
log = logging.getLogger('werkzeug')
log.addFilter(ProgressRouteFilter())
def update_progress(current, total): def update_progress(current, total):
""" """
@ -215,13 +223,8 @@ def index():
resize_size = int(request.form.get("resize_size", 512)) resize_size = int(request.form.get("resize_size", 512))
face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128)) face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128))
pose_threshold = float(request.form.get("pose_threshold", 25)) pose_threshold = float(request.form.get("pose_threshold", 25))
# Parse left_eye_pos tuple from form
left_eye_x = float(request.form.get("left_eye_x", 0.35))
left_eye_y = float(request.form.get("left_eye_y", 0.45))
left_eye_pos = (left_eye_x, left_eye_y)
max_workers = int(request.form.get("max_workers", 1)) max_workers = int(request.form.get("max_workers", 1))
left_eye_pos = (0.4, 0.4)
# Date ranges are optional # Date ranges are optional
date_from = request.form.get("date_from") or None date_from = request.form.get("date_from") or None

7
timelapse.py
View file

@ -50,7 +50,7 @@ class ProcessConfig:
min_face_width: int = 128 min_face_width: int = 128
min_face_height: int = 128 min_face_height: int = 128
pose_threshold: float = 25 pose_threshold: float = 25
left_eye_pos: tuple = (0.35, 0.45) left_eye_pos: tuple = (0.4, 0.4)
landmark_model_path: str = "shape_predictor_68_face_landmarks.dat" landmark_model_path: str = "shape_predictor_68_face_landmarks.dat"
@ -275,17 +275,14 @@ def align_face(image, desired_face_width, desired_face_height, left_eye_pos, pos
img_width, img_height = image.size img_width, img_height = image.size
face_width = img_width / (1 + 2 * padding_percent) face_width = img_width / (1 + 2 * padding_percent)
face_height = img_height / (1 + 2 * padding_percent) face_height = img_height / (1 + 2 * padding_percent)
# Calculate the face rectangle coordinates
x1 = int((img_width - face_width) / 2) x1 = int((img_width - face_width) / 2)
x2 = int(x1 + face_width) x2 = int(x1 + face_width)
y1 = int((img_height - face_height) / 2) y1 = int((img_height - face_height) / 2)
y2 = int(y1 + face_height) y2 = int(y1 + face_height)
# Resize the image for landmark detection
optimal_size = 256 # optimal image resolution for landmark detection (between 200-400px) optimal_size = 256 # optimal image resolution for landmark detection (between 200-400px)
scale_factor = optimal_size / face_width scale_factor = optimal_size / face_width
# Resize the image for landmark detection
resized_width = int(img_width * scale_factor) resized_width = int(img_width * scale_factor)
resized_height = int(img_height * scale_factor) resized_height = int(img_height * scale_factor)
resized_gray = cv2.resize(gray, (resized_width, resized_height)) resized_gray = cv2.resize(gray, (resized_width, resized_height))