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