import insightface import os import onnxruntime import cv2 import gfpgan import tempfile import time import gradio as gr class Predictor: def __init__(self): self.setup() def setup(self): os.makedirs('models', exist_ok=True) os.chdir('models') if not os.path.exists('GFPGANv1.4.pth'): os.system( 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth' ) if not os.path.exists('inswapper_128.onnx'): os.system( 'wget https://huggingface.co/ashleykleynhans/inswapper/resolve/main/inswapper_128.onnx' ) os.chdir('..') """Load the model into memory to make running multiple predictions efficient""" self.face_swapper = insightface.model_zoo.get_model('models/inswapper_128.onnx', providers=onnxruntime.get_available_providers()) self.face_enhancer = gfpgan.GFPGANer(model_path='models/GFPGANv1.4.pth', upscale=1) self.face_analyser = insightface.app.FaceAnalysis(name='buffalo_l') self.face_analyser.prepare(ctx_id=0, det_size=(640, 640)) def get_face(self, img_data): analysed = self.face_analyser.get(img_data) try: largest = max(analysed, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1])) return largest except: print("No face found") return None def predict(self, input_image, swap_image): """Run a single prediction on the model""" try: frame = cv2.imread(input_image.name) face = self.get_face(frame) source_face = self.get_face(cv2.imread(swap_image.name)) try: print(frame.shape, face.shape, source_face.shape) except: print("printing shapes failed.") result = self.face_swapper.get(frame, face, source_face, paste_back=True) _, _, result = self.face_enhancer.enhance( result, paste_back=True ) out_path = tempfile.mkdtemp() + f"/{str(int(time.time()))}.jpg" cv2.imwrite(out_path, result) return out_path except Exception as e: print(f"{e}") return None # Instantiate the Predictor class predictor = Predictor() title = "Swap Faces Using Our Model!!!" # Create Gradio Interface iface = gr.Interface( fn=predictor.predict, inputs=[ gr.inputs.Image(type="file", label="Target Image"), gr.inputs.Image(type="file", label="Swap Image") ], outputs=gr.outputs.Image(type="file", label="Result"), title=title, examples=[["input.jpg", "swap img.jpg"]]) # Launch the Gradio Interface iface.launch()