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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
# 这是修正后的predict函数，请用它替换掉您源代码中的旧版本
def predict(self, input_image, swap_image):
"""Run a single prediction on the model"""
# 【新增】检查输入是否为空
if input_image is None or swap_image is None:
raise gr.Error("请确保同时上传了目标图片和源图片。") # 使用Gradio的方式优雅地报错
try:
# 读取图片
target_img = cv2.imread(input_image.name)
swap_img = cv2.imread(swap_image.name)
# 分析人脸
target_face = self.get_face(target_img)
source_face = self.get_face(swap_img)
# 【关键修正】在执行换脸前，检查是否成功找到了人脸
if target_face is None:
raise gr.Error("在目标图片中未能检测到人脸，请更换图片后重试。")
if source_face is None:
raise gr.Error("在源图片中未能检测到人脸，请更换图片后重试。")
# 如果人脸都找到了，才执行核心换脸操作
result = self.face_swapper.get(target_img, target_face, source_face, paste_back=True)
# 增强画质
_, _, result = self.face_enhancer.enhance(
result,
paste_back=True
)
# 保存并返回结果
out_path = tempfile.mkdtemp() + f"/{str(int(time.time()))}.jpg"
cv2.imwrite(out_path, result)
return out_path
except Exception as e:
# 如果发生其他未知错误，也通过Gradio报错
print(f"An unexpected error occurred: {e}")
raise gr.Error(f"发生未知错误: {e}")
# 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()
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