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import copy |
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import os |
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from dataclasses import dataclass |
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from typing import List, Union |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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import insightface |
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from modules.face_restoration import FaceRestoration |
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from modules.upscaler import UpscalerData |
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from scripts.logger import logger |
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import warnings |
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np.warnings = warnings |
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np.warnings.filterwarnings('ignore') |
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providers = ["CPUExecutionProvider"] |
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models_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models") |
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insightface_path = os.path.join(models_path, "insightface") |
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insightface_models_path = os.path.join(insightface_path, "models") |
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swapper_path = os.path.join(models_path, "roop") |
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@dataclass |
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class UpscaleOptions: |
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do_restore_first: bool = True |
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scale: int = 1 |
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upscaler: UpscalerData = None |
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upscale_visibility: float = 0.5 |
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face_restorer: FaceRestoration = None |
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restorer_visibility: float = 0.5 |
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def cosine_distance(vector1: np.ndarray, vector2: np.ndarray) -> float: |
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vec1 = vector1.flatten() |
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vec2 = vector2.flatten() |
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dot_product = np.dot(vec1, vec2) |
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norm1 = np.linalg.norm(vec1) |
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norm2 = np.linalg.norm(vec2) |
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cosine_distance = 1 - (dot_product / (norm1 * norm2)) |
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return cosine_distance |
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def cosine_similarity(test_vec: np.ndarray, source_vecs: List[np.ndarray]) -> float: |
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cos_dist = sum(cosine_distance(test_vec, source_vec) for source_vec in source_vecs) |
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average_cos_dist = cos_dist / len(source_vecs) |
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return average_cos_dist |
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FS_MODEL = None |
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CURRENT_FS_MODEL_PATH = None |
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ANALYSIS_MODEL = None |
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def getAnalysisModel(): |
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global ANALYSIS_MODEL |
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if ANALYSIS_MODEL is None: |
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ANALYSIS_MODEL = insightface.app.FaceAnalysis( |
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name="buffalo_l", providers=providers, root=insightface_path |
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) |
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return ANALYSIS_MODEL |
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def getFaceSwapModel(model_path: str): |
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global FS_MODEL |
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global CURRENT_FS_MODEL_PATH |
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if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path: |
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CURRENT_FS_MODEL_PATH = model_path |
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FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers) |
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return FS_MODEL |
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def upscale_image(image: Image, upscale_options: UpscaleOptions): |
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result_image = image |
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if upscale_options.do_restore_first: |
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if upscale_options.face_restorer is not None: |
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original_image = result_image.copy() |
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logger.info("Restoring face with %s", upscale_options.face_restorer.name()) |
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numpy_image = np.array(result_image) |
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numpy_image = upscale_options.face_restorer.restore(numpy_image) |
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restored_image = Image.fromarray(numpy_image) |
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result_image = Image.blend( |
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original_image, restored_image, upscale_options.restorer_visibility |
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) |
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if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None": |
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original_image = result_image.copy() |
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logger.info( |
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"Upscaling with %s scale = %s", |
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upscale_options.upscaler.name, |
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upscale_options.scale, |
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) |
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result_image = upscale_options.upscaler.scaler.upscale( |
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original_image, upscale_options.scale, upscale_options.upscaler.data_path |
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) |
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if upscale_options.scale == 1: |
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result_image = Image.blend( |
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original_image, result_image, upscale_options.upscale_visibility |
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) |
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else: |
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if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None": |
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original_image = result_image.copy() |
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logger.info( |
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"Upscaling with %s scale = %s", |
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upscale_options.upscaler.name, |
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upscale_options.scale, |
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) |
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result_image = upscale_options.upscaler.scaler.upscale( |
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image, upscale_options.scale, upscale_options.upscaler.data_path |
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) |
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if upscale_options.scale == 1: |
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result_image = Image.blend( |
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original_image, result_image, upscale_options.upscale_visibility |
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) |
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if upscale_options.face_restorer is not None: |
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original_image = result_image.copy() |
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logger.info("Restoring face with %s", upscale_options.face_restorer.name()) |
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numpy_image = np.array(result_image) |
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numpy_image = upscale_options.face_restorer.restore(numpy_image) |
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restored_image = Image.fromarray(numpy_image) |
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result_image = Image.blend( |
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original_image, restored_image, upscale_options.restorer_visibility |
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) |
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return result_image |
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def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640)): |
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face_analyser = copy.deepcopy(getAnalysisModel()) |
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buffalo_path = os.path.join(insightface_models_path, "buffalo_l.zip") |
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if os.path.exists(buffalo_path): |
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os.remove(buffalo_path) |
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face_analyser.prepare(ctx_id=0, det_size=det_size) |
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face = face_analyser.get(img_data) |
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320: |
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det_size_half = (det_size[0] // 2, det_size[1] // 2) |
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return get_face_single(img_data, face_index=face_index, det_size=det_size_half) |
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try: |
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return sorted(face, key=lambda x: x.bbox[0])[face_index] |
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except IndexError: |
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return None |
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def swap_face( |
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source_img: Image.Image, |
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target_img: Image.Image, |
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model: Union[str, None] = None, |
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source_faces_index: List[int] = [0], |
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faces_index: List[int] = [0], |
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upscale_options: Union[UpscaleOptions, None] = None, |
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): |
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result_image = target_img |
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if model is not None: |
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if isinstance(source_img, str): |
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import base64, io |
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if 'base64,' in source_img: |
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base64_data = source_img.split('base64,')[-1] |
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img_bytes = base64.b64decode(base64_data) |
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else: |
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img_bytes = base64.b64decode(source_img) |
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source_img = Image.open(io.BytesIO(img_bytes)) |
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source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR) |
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target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) |
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source_face = get_face_single(source_img, face_index=source_faces_index[0]) |
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if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index): |
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logger.info(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.') |
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elif source_face is not None: |
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result = target_img |
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model_path = model_path = os.path.join(swapper_path, model) |
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face_swapper = getFaceSwapModel(model_path) |
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source_face_idx = 0 |
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for face_num in faces_index: |
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if len(source_faces_index) > 1 and source_face_idx > 0: |
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source_face = get_face_single(source_img, face_index=source_faces_index[source_face_idx]) |
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source_face_idx += 1 |
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if source_face is not None: |
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target_face = get_face_single(target_img, face_index=face_num) |
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if target_face is not None: |
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result = face_swapper.get(result, target_face, source_face) |
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else: |
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logger.info(f"No target face found for {face_num}") |
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else: |
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logger.info(f"No source face found for face number {source_face_idx}.") |
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result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB)) |
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if upscale_options is not None: |
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result_image = upscale_image(result_image, upscale_options) |
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else: |
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logger.info("No source face(s) found") |
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return result_image |
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