| import cv2
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| import insightface
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| import numpy as np
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| import os
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| from gfpgan import GFPGANer
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| from basicsr.archs.rrdbnet_arch import RRDBNet
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| from realesrgan import RealESRGANer
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|
|
|
|
| class FaceSwapper:
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| def __init__(self,
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| model_path="models/inswapper_128.onnx",
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| gfpgan_model_path="gfpgan/weights/GFPGANv1.4.pth",
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| realesrgan_model_path="models/RealESRGAN_x2plus.pth"):
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|
|
|
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| self.app = insightface.app.FaceAnalysis(name="buffalo_l", providers=['CPUExecutionProvider'])
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| self.app.prepare(ctx_id=0, det_size=(640, 640))
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|
|
|
|
| if not os.path.exists(model_path):
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| raise FileNotFoundError(f"❌ الموديل مش موجود في: {model_path}")
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| self.swapper = insightface.model_zoo.get_model(model_path, providers=['CPUExecutionProvider'])
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|
|
|
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| self.gfpganer = GFPGANer(
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| model_path=gfpgan_model_path,
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| upscale=2,
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| arch="clean",
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| channel_multiplier=2
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| )
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|
|
|
|
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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| num_block=23, num_grow_ch=32, scale=2)
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| self.realesrganer = RealESRGANer(
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| scale=2,
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| model_path=realesrgan_model_path,
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| model=model,
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| tile=0,
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| tile_pad=10,
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| pre_pad=0,
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| half=False
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| )
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|
|
| @staticmethod
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| def get_biggest_face(faces):
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| return max(faces, key=lambda f: (f.bbox[2]-f.bbox[0]) * (f.bbox[3]-f.bbox[1]))
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|
|
| def merge_face_into_image(self, source_img_path, target_img_path, output_path):
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| source_img = cv2.imread(source_img_path)
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| target_img = cv2.imread(target_img_path)
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|
|
| if source_img is None or target_img is None:
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| raise ValueError("❌ مشكلة في قراءة الصور")
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|
|
| source_faces = self.app.get(source_img)
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| target_faces = self.app.get(target_img)
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|
|
| if not source_faces or not target_faces:
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| print("⚠️ No faces detected, returning target image as-is.")
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| cv2.imwrite(output_path, target_img)
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| return output_path
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|
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| source_face = self.get_biggest_face(source_faces)
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| target_face = self.get_biggest_face(target_faces)
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|
|
| swapped_img = self.swapper.get(target_img.copy(), target_face, source_face, paste_back=True)
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|
|
|
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| x1, y1, x2, y2 = target_face.bbox.astype(int)
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| x1, y1 = max(0, x1), max(0, y1)
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| x2, y2 = min(swapped_img.shape[1], x2), min(swapped_img.shape[0], y2)
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| face_crop = swapped_img[y1:y2, x1:x2]
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|
|
| if face_crop.size == 0:
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| raise ValueError("❌ الوجه المقطوع فاضي (bbox مش مظبوط)")
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|
|
|
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| mask = 255 * np.ones(face_crop.shape, face_crop.dtype)
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| mask = cv2.GaussianBlur(mask, (51, 51), 40)
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| center = ((x1 + x2) // 2, (y1 + y2) // 2)
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|
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| blended = cv2.seamlessClone(face_crop, swapped_img, mask, center, cv2.MIXED_CLONE)
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|
|
|
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| _, _, gfpgan_img = self.gfpganer.enhance(blended, has_aligned=False, only_center_face=False, paste_back=True)
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|
|
|
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| sr_img, _ = self.realesrganer.enhance(gfpgan_img, outscale=1)
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|
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| cv2.imwrite(output_path, sr_img, [cv2.IMWRITE_PNG_COMPRESSION, 0])
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| return output_path
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|
|
