| import gradio as gr
|
| import os
|
| import uuid
|
| import cv2
|
| from mask import FaceSwapper
|
|
|
|
|
| swapper = FaceSwapper(
|
| model_path="models/inswapper_128.onnx",
|
| gfpgan_model_path="gfpgan/weights/GFPGANv1.4.pth"
|
| )
|
|
|
|
|
| def swap_faces(source_img, target_img):
|
| try:
|
|
|
| source_path = f"temp_source_{uuid.uuid4().hex}.jpg"
|
| target_path = f"temp_target_{uuid.uuid4().hex}.jpg"
|
| output_path = f"img/result_{uuid.uuid4().hex}.jpg"
|
|
|
| cv2.imwrite(source_path, cv2.cvtColor(source_img, cv2.COLOR_RGB2BGR))
|
| cv2.imwrite(target_path, cv2.cvtColor(target_img, cv2.COLOR_RGB2BGR))
|
|
|
|
|
| result_path = swapper.merge_face_into_image(source_path, target_path, output_path)
|
|
|
|
|
| result_img = cv2.cvtColor(cv2.imread(result_path), cv2.COLOR_BGR2RGB)
|
|
|
|
|
| os.remove(source_path)
|
| os.remove(target_path)
|
|
|
| return result_img
|
| except Exception as e:
|
| return f"❌ حصل خطأ: {str(e)}"
|
|
|
|
|
| demo = gr.Interface(
|
| fn=swap_faces,
|
| inputs=[
|
| gr.Image(type="numpy", label="Source Image (الطفل)"),
|
| gr.Image(type="numpy", label="Target Image (المشهد)")
|
| ],
|
| outputs=gr.Image(type="numpy", label="النتيجة"),
|
| title="FaceSwap with GFPGAN",
|
| description="ارفع صورتين: (1) صورة الطفل (2) المشهد اللي عايز تدخله فيه. وهنرجعلك صورة face swap محسّنة بـ GFPGAN."
|
| )
|
|
|
| if __name__ == "__main__":
|
| demo.launch(server_name="127.0.0.1", server_port=7860)
|
|
|
|
|
| """import cv2
|
| import insightface
|
| import numpy as np
|
| import os
|
| from gfpgan import GFPGANer # pip install gfpgan"""
|
|
|
|
|
| """class FaceSwapper:
|
| def __init__(self,
|
| model_path="models/inswapper_128.onnx",
|
| gfpgan_model_path="gfpgan/weights/GFPGANv1.4.pth"):
|
| # ============ تحميل FaceAnalysis ============
|
| self.app = insightface.app.FaceAnalysis(name="buffalo_l", providers=['CPUExecutionProvider'])
|
| self.app.prepare(ctx_id=0, det_size=(640, 640))
|
|
|
| # ============ تحميل inswapper ============
|
| 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'])
|
|
|
| # ============ تحميل GFPGAN ============
|
| self.gfpganer = GFPGANer(
|
| model_path=gfpgan_model_path,
|
| upscale=1,
|
| arch="clean",
|
| channel_multiplier=2
|
| )
|
|
|
| # ============ دالة مساعدة لاختيار أكبر وجه ============
|
| @staticmethod
|
| def get_biggest_face(faces):
|
| return max(faces, key=lambda f: (f.bbox[2]-f.bbox[0]) * (f.bbox[3]-f.bbox[1]))
|
|
|
| # ============ دالة الدمج ============
|
| 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:
|
| #raise ValueError("❌ مش لاقي وش في الصورة!")
|
| 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)
|
|
|
| # استبدال الوجه
|
| swapped_img = self.swapper.get(target_img.copy(), target_face, source_face, paste_back=True)
|
|
|
| # قص الوجه من الصورة
|
| 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 بنفس حجم الوجه
|
| mask = 255 * np.ones(face_crop.shape, face_crop.dtype)
|
| mask = cv2.GaussianBlur(mask, (25, 25), 30)
|
|
|
| # مركز الوجه
|
| center = ((x1 + x2) // 2, (y1 + y2) // 2)
|
|
|
| # دمج الوجه في الصورة
|
| blended = cv2.seamlessClone(face_crop, swapped_img, mask, center, cv2.NORMAL_CLONE)
|
|
|
| # تحسين الصورة بالـ GFPGAN
|
| _, _, enhanced = self.gfpganer.enhance(blended, has_aligned=False, only_center_face=False, paste_back=True)
|
|
|
| final_img = enhanced # أو blended لو GFPGAN مش موجود
|
| return cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB)"""
|
|
|
