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