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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!!!"
with gr.Blocks() as iface:
gr.Markdown(title)
with gr.Row():
with gr.Column():
input_image = gr.Image(type="file", label="Target Image")
swap_image = gr.Image(type="file", label="Swap Image")
submit_btn = gr.Button("Swap Faces")
with gr.Column():
output_image = gr.Image(type="file", label="Result")
submit_btn.click(
fn=predictor.predict,
inputs=[input_image, swap_image],
outputs=output_image
)
gr.Markdown("""
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""")
# Launch the Gradio Interface
iface.launch() |