roop/face_analyser.py

import threading
from typing import Any, Optional, List
import insightface
import numpy as np
import onnxruntime
import cv2
from roop.typing import Frame, Face
FACE_ANALYSER = None
# THREAD_LOCK = threading.Lock()
import utils
def encode_execution_providers(execution_providers: List[str]) -> List[str]:
    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]

def decode_execution_providers(execution_providers: List[str]) -> List[str]:
    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]

def clear_face_analyser() -> Any:
    global FACE_ANALYSER
    FACE_ANALYSER = None

def get_one_face(frame: Frame, bboxes, kpss, position, face_analyser_model) -> Optional[Face]:
    many_faces = get_many_faces(frame, bboxes, kpss, face_analyser_model)
    if many_faces:
        try:
            return many_faces[position]
        except IndexError:
            return many_faces[-1]
    return None

def get_one_template_face(frame: Frame, bboxes, kpss, position, face_analyser_model, targetface_manual) -> Optional[Face]:
    
    if targetface_manual:
        bboxes = np.array([targetface_manual[0][position]['bbox']], dtype=np.float32)
        kpss = np.array([targetface_manual[0][position]['kps']], dtype=np.float32)
    many_faces = get_many_template_faces(frame, bboxes, kpss, face_analyser_model)
    if many_faces:
        if not targetface_manual:
            targetface_manual.append(many_faces)
            try:
                return targetface_manual[0][position]
            except IndexError:
                return targetface_manual[0][-1]
        else:
            try:
                return many_faces[position]
            except IndexError:
                return many_faces[-1]
    return None
            

def get_many_faces(frame: Frame, bboxes, kpss, face_analyser_model) -> Optional[List[Face]]:
    # Kiểm tra và in kích thước của frame
    if frame.shape[0] <= 0 or frame.shape[1] <= 0:
        raise ValueError("Invalid frame dimensions")
    # try:
        # Thêm kiểm tra trước khi resize
    
    height, width = frame.shape[:2]
    new_width, new_height = 640, 640
    if width <= 0 or height <= 0:
        raise ValueError("Invalid frame dimensions for resizing")
    
    faces = face_analyser_model.get_points(bboxes, kpss, frame)
    # draw_face = face_analyser_model.draw_on(frame, faces)
    # import uuid
    # unique = uuid.uuid4() 
    # cv2.imwrite(f"draw_face_{unique}.jpg", draw_face)
    return faces
    # except ValueError as e:
    #     print(f"Error in get_many_faces: {e}")
    #     return None

def get_many_template_faces(frame: Frame, bboxes, kpss, face_analyser_model) -> Optional[List[Face]]:
    # Kiểm tra và in kích thước của frame
    if frame.shape[0] <= 0 or frame.shape[1] <= 0:
        raise ValueError("Invalid frame dimensions")
    # try:
        # Thêm kiểm tra trước khi resize
    
    height, width = frame.shape[:2]
    new_width, new_height = 640, 640
    if width <= 0 or height <= 0:
        raise ValueError("Invalid frame dimensions for resizing")
    
    faces = face_analyser_model.get_points(bboxes, kpss, frame)
    # draw_face = face_analyser_model.draw_on(frame, faces)
    # import uuid
    # unique = uuid.uuid4() 
    # cv2.imwrite(f"draw_face_{unique}.jpg", draw_face)
    return faces
    # except ValueError as e:
    #     print(f"Error in get_many_faces: {e}")
    #     return None

def get_many_faces_detect(frame: Frame, bboxes, kpss, face_analyser_model) -> Optional[List[Face]]:
    # Kiểm tra và in kích thước của frame
    if frame.shape[0] <= 0 or frame.shape[1] <= 0:
        raise ValueError("Invalid frame dimensions")
    # try:
        # Thêm kiểm tra trước khi resize
    
    height, width = frame.shape[:2]
    new_width, new_height = 640, 640
    if width <= 0 or height <= 0:
        raise ValueError("Invalid frame dimensions for resizing")
    
    faces = face_analyser_model.get(bboxes, kpss, frame)
    # draw_face = utils.globals.FACE_ANALYSER.draw_on(frame, faces)
    # import uuid
    # unique = uuid.uuid4() 
    # cv2.imwrite(f"draw_face_{unique}.jpg", draw_face)
    return faces
    # except ValueError as e:
    #     print(f"Error in get_many_faces: {e}")
    #     return None

def find_similar_face(frame: Frame, targetface_bbox, kpss_targetface, reference_face: Face, face_analyser_model) -> Optional[Face]:
    many_faces = get_many_faces(frame, targetface_bbox, kpss_targetface, face_analyser_model)
    if many_faces:
        for face in many_faces:
            if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
                distance = np.sum(np.square(face.normed_embedding - reference_face.normed_embedding))
                if distance < 0.95:
                    return face
    return None