face_analysis.py

import torch
import numpy as np
from facenet_pytorch import InceptionResnetV1
from sklearn.cluster import DBSCAN
import os
import shutil
import mediapipe as mp
import cv2

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = InceptionResnetV1(pretrained='vggface2').eval().to(device)

def get_face_embedding(face_img):
    face_tensor = torch.tensor(face_img).permute(2, 0, 1).unsqueeze(0).float() / 255
    face_tensor = (face_tensor - 0.5) / 0.5
    face_tensor = face_tensor.to(device)
    with torch.no_grad():
        embedding = model(face_tensor)
    return embedding.cpu().numpy().flatten()

def cluster_faces(embeddings):
    if len(embeddings) < 2:
        print("Not enough faces for clustering. Assigning all to one cluster.")
        return np.zeros(len(embeddings), dtype=int)

    X = np.stack(embeddings)
    dbscan = DBSCAN(eps=0.3, min_samples=5, metric='cosine')
    clusters = dbscan.fit_predict(X)

    if np.all(clusters == -1):
        print("DBSCAN assigned all to noise. Considering as one cluster.")
        return np.zeros(len(embeddings), dtype=int)

    return clusters

def organize_faces_by_person(embeddings_by_frame, clusters, aligned_faces_folder, organized_faces_folder):
    for (frame_num, embedding), cluster in zip(embeddings_by_frame.items(), clusters):
        person_folder = os.path.join(organized_faces_folder, f"person_{cluster}")
        os.makedirs(person_folder, exist_ok=True)
        src = os.path.join(aligned_faces_folder, f"frame_{frame_num}_face.jpg")
        dst = os.path.join(person_folder, f"frame_{frame_num}_face.jpg")
        shutil.copy(src, dst)


def draw_facial_landmarks(image, landmarks):
    mp_face_mesh = mp.solutions.face_mesh
    mp_drawing = mp.solutions.drawing_utils
    
    # Adjust these values to make the drawing thinner and less obtrusive
    connection_drawing_spec = mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=1)
    
    if landmarks:
        # Create a blank image with the same dimensions as the input image
        connections_layer = np.zeros_like(image)
        
        # Draw connections on the blank image
        mp_drawing.draw_landmarks(
            image=connections_layer,
            landmark_list=landmarks,
            connections=mp_face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=None,  # Set to None to avoid drawing points
            connection_drawing_spec=connection_drawing_spec)
        
        # Blend the connections layer with the original image
        alpha = 0.2
        image = cv2.addWeighted(image, 1, connections_layer, alpha, 0)
    
    return image