Split immich api features into separate file
This commit is contained in:
Arnaud_Cayrol
parent
1870c956b0
commit
4b7b91c97f
3 changed files with 182 additions and 222 deletions
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@ -1,7 +1,6 @@
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# timelapse.py
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# image_processing.py
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import os
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import io
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import requests
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import concurrent.futures
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from datetime import datetime
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from dataclasses import dataclass
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@ -12,6 +11,7 @@ import dlib
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from tqdm import tqdm
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import logging
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from typing import Tuple
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from immich_api import get_assets_with_person, download_asset
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class TqdmLoggingHandler(logging.Handler):
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def __init__(self, level=logging.NOTSET):
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@ -32,8 +32,6 @@ formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s', datef
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tqdm_handler.setFormatter(formatter)
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logger.addHandler(tqdm_handler)
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face_predictor = None
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@dataclass
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class AppConfig:
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@ -50,71 +48,6 @@ class AppConfig:
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date_from: str
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date_to: str
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def draw_landmarks(image, landmarks, face_rect, output_path):
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"""
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Draws facial landmarks and face rectangle on the image and saves it.
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Args:
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image (numpy.ndarray): The input image in BGR format.
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landmarks (dict): Dictionary containing facial landmarks.
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face_rect (tuple): Face rectangle coordinates (x1, y1, x2, y2).
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output_path (str): Path to save the output image.
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"""
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# Create a copy of the image to draw on
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img = image.copy()
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# Draw face rectangle
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x1, y1, x2, y2 = face_rect
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cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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# Draw landmarks if available
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if landmarks is not None:
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for i, (x, y) in landmarks.items():
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cv2.circle(img, (int(x), int(y)), 2, (0, 0, 255), -1)
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# Create output directory if it doesn't exist
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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# Save the image
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cv2.imwrite(output_path, img)
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def validate_immich_connection(api_key, base_url):
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"""
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Validates that the provided Immich API key and base URL are working.
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Args:
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api_key (str): API key for authentication.
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base_url (str): Base URL of the API.
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Returns:
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tuple: (bool, str) - (is_valid, error_message)
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"""
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if not api_key or not base_url:
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return False, "API key and base URL are required."
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try:
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headers = {
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'Accept': 'application/json',
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'x-api-key': api_key,
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}
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# Try a simple ping to the server via the user endpoint
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url = f"{base_url}/server/about"
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response = requests.get(url, headers=headers, timeout=5)
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if response.status_code == 200:
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return True, "Connection successful."
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elif response.status_code == 401:
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return False, "Authentication failed. Invalid API key."
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else:
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return False, f"Server error: Status code {response.status_code}"
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except requests.exceptions.ConnectionError:
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return False, "Connection error. Check the base URL."
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except requests.exceptions.Timeout:
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return False, "Connection timed out. Server might be down."
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except Exception as e:
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return False, f"Unexpected error: {str(e)}"
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def initialize_worker(landmark_model_path: str) -> None:
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"""Initialize worker process with face predictor.
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@ -126,142 +59,6 @@ def initialize_worker(landmark_model_path: str) -> None:
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face_predictor = dlib.shape_predictor(landmark_model_path)
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def get_assets_with_person(api_key, base_url, person_id, date_from=None, date_to=None):
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"""
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Retrieve all image assets containing the specified person by querying the API.
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Args:
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api_key (str): API key for authentication.
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base_url (str): Base URL of the API.
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person_id (str): ID of the person to search for.
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date_from (str, optional): Start date in ISO format (YYYY-MM-DD).
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date_to (str, optional): End date in ISO format (YYYY-MM-DD).
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Returns:
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list: List of asset dictionaries.
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"""
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headers = {
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'Content-Type': 'application/json',
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'Accept': 'application/json',
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'x-api-key': api_key,
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}
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url = f"{base_url}/search/metadata"
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all_assets = []
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payload = {
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"page": 1,
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"type": "IMAGE",
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"personIds": [person_id],
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"withArchived": False,
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"withDeleted": True,
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"withExif": True,
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"withPeople": True,
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"withStacked": True,
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}
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if date_from:
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payload["takenAfter"] = f"{date_from}T00:00:00.000Z"
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if date_to:
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payload["takenBefore"] = f"{date_to}T23:59:59.999Z"
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while payload["page"] is not None:
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response = requests.post(url, headers=headers, json=payload)
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if response.status_code != 200:
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logger.info(f"Error fetching page {payload['page']}: {response.status_code} - {response.text}")
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break
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data = response.json()
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if not data:
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break
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all_assets.extend(data['assets']['items'])
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logger.info(f"Fetched page {payload['page']} with {len(data['assets']['items'])} assets")
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payload["page"] = data['assets'].get('nextPage')
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return all_assets
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def download_asset(api_key, base_url, asset_id):
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"""
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Downloads the original image asset from the API.
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Args:
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api_key (str): API key for authentication.
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base_url (str): Base URL of the API.
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asset_id (str): The asset's ID.
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Returns:
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bytes: The content of the downloaded image.
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"""
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headers = {'x-api-key': api_key}
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response = requests.get(f'{base_url}/assets/{asset_id}/original', headers=headers)
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response.raise_for_status()
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return response.content
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def get_head_pose(landmarks, image):
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"""
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Estimates the head pose (pitch, yaw, roll) using facial landmarks.
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Based on https://learnopencv.com/head-pose-estimation-using-opencv-and-dlib/
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Args:
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landmarks (dict): Dictionary containing facial landmarks in numpy arrays.
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image (PIL.Image): The input image.
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Returns:
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tuple or None: (pitch, yaw, roll) in degrees if successful; otherwise None.
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"""
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# Get image size
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img_width, img_height = image.size
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image_points = np.array([
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landmarks['nose_tip'],
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landmarks['chin'],
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landmarks['left_eye'][0],
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landmarks['right_eye'][3],
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landmarks['left_mouth'],
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landmarks['right_mouth']
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], dtype="double")
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model_points = np.array([
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(0.0, 0.0, 0.0), # Nose tip
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(0.0, -330.0, -65.0), # Chin
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(-225.0, 170.0, -135.0), # Left eye left corner
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(225.0, 170.0, -135.0), # Right eye right corner
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(-150.0, -150.0, -125.0), # Left Mouth corner
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(150.0, -150.0, -125.0) # Right mouth corner
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])
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focal_length = img_width
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center = (img_width / 2, img_height / 2)
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camera_matrix = np.array(
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[[focal_length, 0, center[0]],
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[0, focal_length, center[1]],
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[0, 0, 1]], dtype="double"
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)
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dist_coeffs = np.zeros((4, 1))
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success, rotation_vector, translation_vector = cv2.solvePnP(
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model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE
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)
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if not success:
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logger.info("Head pose estimation failed in solvePnP.")
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return None
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rotation_matrix, _ = cv2.Rodrigues(rotation_vector)
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proj_matrix = np.hstack((rotation_matrix, translation_vector))
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_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(proj_matrix)
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pitch, yaw, roll = [float(angle) for angle in euler_angles]
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# Normalize angles to be between -180 and 180 degrees
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pitch = (pitch + 180) % 360 - 180
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yaw = (yaw + 180) % 360 - 180
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roll = (roll + 180) % 360 - 180
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# Adjust pitch to be between -90 and 90 degrees
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if pitch > 90:
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pitch = 180 - pitch
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elif pitch < -90:
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pitch = -180 - pitch
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return pitch, yaw, roll
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def detect_landmarks(image, face_data, face_resolution_threshold):
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"""
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Detects facial landmarks in the image, resizing if necessary for better detection.
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@ -376,6 +173,72 @@ def check_eye_visibility(left_eye, right_eye, ear_threshold=0.2) -> bool:
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return True
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def get_head_pose(landmarks, image):
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"""
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Estimates the head pose (pitch, yaw, roll) using facial landmarks.
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Based on https://learnopencv.com/head-pose-estimation-using-opencv-and-dlib/
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Args:
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landmarks (dict): Dictionary containing facial landmarks in numpy arrays.
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image (PIL.Image): The input image.
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Returns:
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tuple or None: (pitch, yaw, roll) in degrees if successful; otherwise None.
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"""
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# Get image size
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img_width, img_height = image.size
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image_points = np.array([
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landmarks['nose_tip'],
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landmarks['chin'],
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landmarks['left_eye'][0],
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landmarks['right_eye'][3],
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landmarks['left_mouth'],
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landmarks['right_mouth']
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], dtype="double")
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model_points = np.array([
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(0.0, 0.0, 0.0), # Nose tip
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(0.0, -330.0, -65.0), # Chin
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(-225.0, 170.0, -135.0), # Left eye left corner
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(225.0, 170.0, -135.0), # Right eye right corner
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(-150.0, -150.0, -125.0), # Left Mouth corner
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(150.0, -150.0, -125.0) # Right mouth corner
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])
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focal_length = img_width
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center = (img_width / 2, img_height / 2)
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camera_matrix = np.array(
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[[focal_length, 0, center[0]],
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[0, focal_length, center[1]],
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[0, 0, 1]], dtype="double"
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)
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dist_coeffs = np.zeros((4, 1))
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success, rotation_vector, translation_vector = cv2.solvePnP(
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model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE
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)
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if not success:
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logger.info("Head pose estimation failed in solvePnP.")
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return None
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rotation_matrix, _ = cv2.Rodrigues(rotation_vector)
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proj_matrix = np.hstack((rotation_matrix, translation_vector))
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_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(proj_matrix)
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pitch, yaw, roll = [float(angle) for angle in euler_angles]
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# Normalize angles to be between -180 and 180 degrees
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pitch = (pitch + 180) % 360 - 180
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yaw = (yaw + 180) % 360 - 180
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roll = (roll + 180) % 360 - 180
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# Adjust pitch to be between -90 and 90 degrees
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if pitch > 90:
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pitch = 180 - pitch
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elif pitch < -90:
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pitch = -180 - pitch
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return pitch, yaw, roll
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def calculate_eye_alignment_transform(
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left_eye_center: np.ndarray,
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right_eye_center: np.ndarray,
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@ -479,19 +342,6 @@ def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold
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PIL.Image or None: The aligned face image if successful, None otherwise.
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"""
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try:
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# DEBUG : remove after testing
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face_img_width = face_data.get("imageWidth")
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face_img_height = face_data.get("imageHeight")
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img_width, img_height = image.size
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scale_x = img_width / face_img_width
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scale_y = img_height / face_img_height
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x1 = int(face_data.get("boundingBoxX1", 0) * scale_x)
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x2 = int(face_data.get("boundingBoxX2", 0) * scale_x)
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y1 = int(face_data.get("boundingBoxY1", 0) * scale_y)
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y2 = int(face_data.get("boundingBoxY2", 0) * scale_y)
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w = x2 - x1
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h = y2 - y1
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# Convert image to numpy array for OpenCV processing
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img_np = np.array(image)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
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@ -505,21 +355,18 @@ def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold
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# Check if both eyes are visible
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if not check_eye_visibility(landmarks['left_eye'], landmarks['right_eye']):
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logger.info("Eyes are too closed")
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# draw_landmarks(img_np, landmarks['all_landmarks'], (x1, y1, x2, y2), f"discarded/eyes_closed_{face_data.get('id')}.jpg")
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return None
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# Get head pose
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pose = get_head_pose(landmarks, image)
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if not pose:
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logger.info("Could not estimate head pose")
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draw_landmarks(img_np, landmarks['all_landmarks'], (x1, y1, x2, y2), "discarded/no_pose.jpg")
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return None
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# Check if head pose is within acceptable range
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pitch, yaw, roll = pose
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if abs(yaw) > pose_threshold:
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logger.info(f"Head pose exceeds threshold: pitch={pitch:.1f}°, yaw={yaw:.1f}°, roll={roll:.1f}°")
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draw_landmarks(img_np, landmarks['all_landmarks'], (x1, y1, x2, y2), f"discarded/pose_yaw_{yaw:.1f}.jpg")
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return None
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# Get eye positions
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112
immich_api.py
Normal file
112
immich_api.py
Normal file
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@ -0,0 +1,112 @@
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import requests
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import logging
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logger = logging.getLogger(__name__)
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def validate_immich_connection(api_key, base_url):
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"""
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Validates that the provided Immich API key and base URL are working.
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Args:
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api_key (str): API key for authentication.
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base_url (str): Base URL of the API.
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Returns:
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tuple: (bool, str) - (is_valid, error_message)
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"""
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if not api_key or not base_url:
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return False, "API key and base URL are required."
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try:
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headers = {
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'Accept': 'application/json',
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'x-api-key': api_key,
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}
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# Try a simple ping to the server via the user endpoint
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url = f"{base_url}/server/about"
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response = requests.get(url, headers=headers, timeout=5)
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if response.status_code == 200:
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return True, "Connection successful."
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elif response.status_code == 401:
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return False, "Authentication failed. Invalid API key."
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else:
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return False, f"Server error: Status code {response.status_code}"
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except requests.exceptions.ConnectionError:
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return False, "Connection error. Check the base URL."
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except requests.exceptions.Timeout:
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return False, "Connection timed out. Server might be down."
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except Exception as e:
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return False, f"Unexpected error: {str(e)}"
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def get_assets_with_person(api_key, base_url, person_id, date_from=None, date_to=None):
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"""
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Retrieve all image assets containing the specified person by querying the API.
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Args:
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api_key (str): API key for authentication.
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base_url (str): Base URL of the API.
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person_id (str): ID of the person to search for.
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date_from (str, optional): Start date in ISO format (YYYY-MM-DD).
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date_to (str, optional): End date in ISO format (YYYY-MM-DD).
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Returns:
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list: List of asset dictionaries.
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"""
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headers = {
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'Content-Type': 'application/json',
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'Accept': 'application/json',
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'x-api-key': api_key,
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}
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url = f"{base_url}/search/metadata"
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all_assets = []
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payload = {
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"page": 1,
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"type": "IMAGE",
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"personIds": [person_id],
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"withArchived": False,
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"withDeleted": True,
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"withExif": True,
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"withPeople": True,
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"withStacked": True,
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}
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if date_from:
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payload["takenAfter"] = f"{date_from}T00:00:00.000Z"
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if date_to:
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payload["takenBefore"] = f"{date_to}T23:59:59.999Z"
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while payload["page"] is not None:
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response = requests.post(url, headers=headers, json=payload)
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if response.status_code != 200:
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logger.info(f"Error fetching page {payload['page']}: {response.status_code} - {response.text}")
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break
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data = response.json()
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if not data:
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break
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all_assets.extend(data['assets']['items'])
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logger.info(f"Fetched page {payload['page']} with {len(data['assets']['items'])} assets")
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payload["page"] = data['assets'].get('nextPage')
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return all_assets
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def download_asset(api_key, base_url, asset_id):
|
||||
"""
|
||||
Downloads the original image asset from the API.
|
||||
|
||||
Args:
|
||||
api_key (str): API key for authentication.
|
||||
base_url (str): Base URL of the API.
|
||||
asset_id (str): The asset's ID.
|
||||
|
||||
Returns:
|
||||
bytes: The content of the downloaded image.
|
||||
"""
|
||||
headers = {'x-api-key': api_key}
|
||||
response = requests.get(f'{base_url}/assets/{asset_id}/original', headers=headers)
|
||||
response.raise_for_status()
|
||||
return response.content
|
||||
3
main.py
3
main.py
|
|
@ -6,7 +6,8 @@ from dataclasses import dataclass
|
|||
from typing import Callable, Dict, List, Tuple
|
||||
|
||||
from flask import Flask, jsonify, render_template, request
|
||||
from timelapse import process_faces, validate_immich_connection
|
||||
from image_processing import process_faces
|
||||
from immich_api import validate_immich_connection
|
||||
|
||||
# Configure logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
|
|||
Loading…
Reference in a new issue