421 lines
14 KiB
Python
421 lines
14 KiB
Python
import os
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from typing import Optional, Dict, List
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import dlib
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import mediapipe as mp
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import numpy as np
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import requests
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from tqdm import tqdm
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class ModelDownload:
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"""Class for managing model downloads and verification"""
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MODEL_URLS = {
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# Mediapipe 468 landmark model
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"canonical_face_model": "https://raw.githubusercontent.com/google-ai-edge/mediapipe/master/mediapipe/modules/face_geometry/data/canonical_face_model.obj",
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# Dlib 68 landmark model
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"shape_predictor_68_face_landmarks": "https://huggingface.co/spaces/asdasdasdasd/Face-forgery-detection/resolve/ccfc24642e0210d4d885bc7b3dbc9a68ed948ad6/shape_predictor_68_face_landmarks.dat"
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}
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def __init__(self):
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"""Initialize model downloader"""
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self.chunk_size = 8192
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self.models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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def download_model(self, model_name: str, target_dir: Optional[str] = None) -> Optional[str]:
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"""
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Download model from predefined URL.
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Args:
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model_name: Name of the model to download
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target_dir: Optional target directory (defaults to models/{model_name})
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Returns:
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str: Path to downloaded model or None if failed
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"""
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try:
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if model_name not in self.MODEL_URLS:
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print(f"Unknown model: {model_name}")
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return None
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# Set target directory
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if target_dir is None:
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target_dir = os.path.join(self.models_dir, os.path.splitext(model_name)[0])
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# Create target directory if it doesn't exist
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os.makedirs(target_dir, exist_ok=True)
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# Set target file path
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url = self.MODEL_URLS[model_name]
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if not url: # Skip if URL is empty
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print(f"No URL defined for model: {model_name}")
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return None
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file_name = os.path.basename(url.split('?')[0]) # Remove URL parameters if any
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target_path = os.path.join(target_dir, file_name)
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# Check if file already exists
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if os.path.exists(target_path):
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print(f"Model {model_name} already exists at: {target_path}")
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return target_path
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print(f"Downloading {model_name} from: {url}")
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print(f"Target path: {target_path}")
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# Download file with progress bar
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response = requests.get(url, stream=True)
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total_size = int(response.headers.get('content-length', 0))
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with open(target_path, 'wb') as f:
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with tqdm(total=total_size, unit='B', unit_scale=True, desc=model_name) as pbar:
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for data in response.iter_content(chunk_size=self.chunk_size):
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f.write(data)
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pbar.update(len(data))
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print(f"Successfully downloaded {model_name} to: {target_path}")
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return target_path
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except Exception as e:
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print(f"Error downloading model {model_name}: {str(e)}")
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return None
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def get_model_path(self, model_name: str, target_dir: Optional[str] = None) -> Optional[str]:
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"""
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Get path to model, download if not exists.
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Args:
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model_name: Name of the model
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target_dir: Optional target directory
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Returns:
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str: Path to model or None if failed
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"""
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if target_dir is None:
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target_dir = os.path.join(self.models_dir, os.path.splitext(model_name)[0])
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# Check if model exists
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if os.path.exists(target_dir):
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expected_file = os.path.join(target_dir, os.path.basename(self.MODEL_URLS[model_name].split('?')[0]))
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if os.path.exists(expected_file):
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return expected_file
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# Download if not exists
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return self.download_model(model_name, target_dir)
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class ModelOBJ:
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"""Class for loading and parsing OBJ files with landmark transformation support"""
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_instance: Optional['ModelOBJ'] = None
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_vertices: List[List[float]] = []
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_uvs: List[List[float]] = []
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_faces: List[List[int]] = []
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_vertex_to_uv: Dict[int, int] = {}
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_landmarks: Optional[np.ndarray] = None
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_num_landmarks: int = 468
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_model_loaded: bool = False
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def __new__(cls):
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"""Implement singleton pattern"""
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if cls._instance is None:
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cls._instance = super(ModelOBJ, cls).__new__(cls)
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return cls._instance
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def __init__(self):
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"""Initialize ModelOBJ and load face model"""
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if not self._model_loaded:
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self.downloader = ModelDownload()
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self._load_obj()
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def _load_obj(self) -> None:
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"""Load and parse MediaPipe face model OBJ file"""
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try:
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# Get model path, download if needed
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model_path = self.downloader.get_model_path("canonical_face_model")
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if not model_path:
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raise FileNotFoundError("Failed to get canonical face model")
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print(f"Loading 3D model from: {model_path}")
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with open(model_path, 'r') as f:
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for line in f:
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if line.startswith('v '):
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# Parse vertex coordinates
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parts = line.strip().split()
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self._vertices.append([float(p) for p in parts[1:4]])
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elif line.startswith('vt '):
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# Parse UV coordinates (flip V coordinate)
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parts = line.strip().split()
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u = float(parts[1])
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v = 1.0 - float(parts[2]) # Flip V coordinate
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self._uvs.append([u, v])
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elif line.startswith('f '):
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# Parse face indices
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parts = line.strip().split()[1:]
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face_verts = []
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for part in parts:
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indices = part.split('/')
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if len(indices) >= 2:
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vertex_idx = int(indices[0]) - 1 # OBJ indices are 1-based
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uv_idx = int(indices[1]) - 1
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self._vertex_to_uv[vertex_idx] = uv_idx
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face_verts.append(vertex_idx)
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if len(face_verts) == 3: # Only store triangular faces
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self._faces.append(face_verts)
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# Initialize landmarks
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self._landmarks = self._initialize_landmarks()
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self._model_loaded = True
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print(f"Successfully loaded model data:")
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print(f"- Vertices: {len(self._vertices)}")
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print(f"- UVs: {len(self._uvs)}")
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print(f"- Faces: {len(self._faces)}")
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print(f"- Vertex to UV mappings: {len(self._vertex_to_uv)}")
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except Exception as e:
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print(f"Error loading OBJ file: {str(e)}")
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raise
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def _initialize_landmarks(self) -> np.ndarray:
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"""
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Initialize landmarks array from UV coordinates
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Returns:
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numpy.ndarray: Array of landmark coordinates in UV space
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"""
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landmarks = np.zeros((self._num_landmarks, 2), dtype=np.float32)
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uvs = np.array(self._uvs, dtype=np.float32)
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for vertex_idx, uv_idx in self._vertex_to_uv.items():
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if vertex_idx < self._num_landmarks:
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landmarks[vertex_idx] = uvs[uv_idx]
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return landmarks
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def get_faces(self) -> np.ndarray:
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"""
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Get face indices as numpy array
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Returns:
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numpy.ndarray: Array of face indices
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"""
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return np.array(self._faces, dtype=np.int32)
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def get_transformed_landmarks(self, x_scale: float = 1.0, y_translation: float = 0.0) -> np.ndarray:
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"""
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Get landmarks with applied transformations
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Args:
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x_scale: Horizontal scaling factor (0.5 to 1.0)
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y_translation: Vertical translation (-0.5 to 0.5)
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Returns:
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numpy.ndarray: Transformed landmarks
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"""
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if self._landmarks is None:
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raise RuntimeError("Landmarks not initialized")
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transformed = self._landmarks.copy()
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# Calculate center point
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center_x = (self._landmarks[:, 0].min() + self._landmarks[:, 0].max()) / 2
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# Calculate horizontal distance from center (normalized to 0-1)
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dx = np.abs(transformed[:, 0] - center_x)
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# Avoid division by zero
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if np.abs(center_x) < 1e-6: # Use small epsilon value
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influence = np.zeros_like(dx)
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else:
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influence = np.clip(dx / center_x, 0, 1)
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# Calculate scaled positions with horizontal influence
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scale_factor = 1.0 + (x_scale - 1.0) * influence
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transformed[:, 0] = center_x + (transformed[:, 0] - center_x) * scale_factor
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# Apply vertical translation
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transformed[:, 1] += y_translation
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return transformed
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@property
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def num_landmarks(self) -> int:
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"""
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Get the number of landmarks
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Returns:
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int: Number of landmarks
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"""
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return self._num_landmarks
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def _initialize_landmarks(self) -> np.ndarray:
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"""
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Initialize landmarks array from UV coordinates
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Returns:
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numpy.ndarray: Array of landmark coordinates in UV space
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"""
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landmarks = np.zeros((self._num_landmarks, 2), dtype=np.float32)
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uvs = np.array(self._uvs, dtype=np.float32)
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for vertex_idx, uv_idx in self._vertex_to_uv.items():
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if vertex_idx < self._num_landmarks:
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landmarks[vertex_idx] = uvs[uv_idx]
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return landmarks
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def get_faces(self) -> np.ndarray:
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"""
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Get face indices as numpy array
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Returns:
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numpy.ndarray: Array of face indices
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"""
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return np.array(self._faces, dtype=np.int32)
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def get_transformed_landmarks(self, x_scale: float = 1.0, y_translation: float = 0.0) -> np.ndarray:
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"""
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Get landmarks with applied transformations
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Args:
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x_scale: Horizontal scaling factor (0.5 to 1.0)
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y_translation: Vertical translation (-0.5 to 0.5)
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Returns:
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numpy.ndarray: Transformed landmarks
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"""
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if self._landmarks is None:
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raise RuntimeError("Landmarks not initialized")
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transformed = self._landmarks.copy()
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# Calculate center point
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center_x = (self._landmarks[:, 0].min() + self._landmarks[:, 0].max()) / 2
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# Calculate horizontal distance from center (normalized to 0-1)
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dx = np.abs(transformed[:, 0] - center_x)
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# Avoid division by zero
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if np.abs(center_x) < 1e-6: # Use small epsilon value
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influence = np.zeros_like(dx)
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else:
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influence = np.clip(dx / center_x, 0, 1)
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# Calculate scaled positions with horizontal influence
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scale_factor = 1.0 + (x_scale - 1.0) * influence
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transformed[:, 0] = center_x + (transformed[:, 0] - center_x) * scale_factor
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# Apply vertical translation
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transformed[:, 1] += y_translation
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return transformed
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class ModelDlib:
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"""Class for loading and managing Dlib face detection models using singleton pattern"""
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_instance: Optional['ModelDlib'] = None
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_face_detector: Optional[dlib.fhog_object_detector] = None
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_shape_predictor: Optional[dlib.shape_predictor] = None
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_model_loaded: bool = False
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def __new__(cls):
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"""Implement singleton pattern"""
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if cls._instance is None:
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cls._instance = super(ModelDlib, cls).__new__(cls)
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return cls._instance
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def __init__(self):
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"""Initialize Dlib model loader"""
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if not self._model_loaded:
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self.downloader = ModelDownload()
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self._initialize_models()
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def _initialize_models(self) -> None:
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"""Initialize Dlib face detector and shape predictor"""
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try:
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print("Initializing Dlib face detector...")
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self._face_detector = dlib.get_frontal_face_detector()
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# Get model path, download if needed
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model_path = self.downloader.get_model_path("shape_predictor_68_face_landmarks")
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if not model_path:
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raise FileNotFoundError("Failed to get shape predictor model")
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print(f"Loading Dlib shape predictor from: {model_path}")
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self._shape_predictor = dlib.shape_predictor(model_path)
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self._model_loaded = True
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print("Dlib models initialized successfully")
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except Exception as e:
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print(f"Error initializing Dlib models: {str(e)}")
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raise
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@property
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def face_detector(self) -> dlib.fhog_object_detector:
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"""Get Dlib face detector instance"""
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if not self._face_detector:
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self._initialize_models()
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return self._face_detector
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@property
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def shape_predictor(self) -> dlib.shape_predictor:
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"""Get Dlib shape predictor instance"""
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if not self._shape_predictor:
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self._initialize_models()
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return self._shape_predictor
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class ModelMediaPipe:
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"""Class for loading and managing MediaPipe face mesh models using singleton pattern"""
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_instance: Optional['ModelMediaPipe'] = None
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_face_mesh: Optional[mp.solutions.face_mesh.FaceMesh] = None
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_model_loaded: bool = False
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def __new__(cls):
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"""Implement singleton pattern"""
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if cls._instance is None:
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cls._instance = super(ModelMediaPipe, cls).__new__(cls)
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return cls._instance
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def __init__(self):
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"""Initialize MediaPipe model loader"""
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if not self._model_loaded:
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self._initialize_models()
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def _initialize_models(self) -> None:
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"""Initialize MediaPipe face mesh model"""
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try:
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print("Initializing MediaPipe Face Mesh...")
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self._face_mesh = mp.solutions.face_mesh.FaceMesh(
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static_image_mode=True,
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max_num_faces=1,
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refine_landmarks=True,
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min_detection_confidence=0.5
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)
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self._model_loaded = True
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print("MediaPipe Face Mesh initialized successfully")
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except Exception as e:
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print(f"Error initializing MediaPipe models: {str(e)}")
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raise
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@property
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def face_mesh(self) -> mp.solutions.face_mesh.FaceMesh:
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"""Get MediaPipe Face Mesh instance"""
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if not self._face_mesh:
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self._initialize_models()
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return self._face_mesh
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def __del__(self):
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"""Clean up resources"""
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if self._face_mesh:
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self._face_mesh.close() |