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import gradio as gr
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from PIL import Image
import torchvision.transforms as transforms
import torch
import sys
import matplotlib.pyplot as plt
"python test.py --model test --name selfie2anime --dataroot selfie2anime/testB --num_test 100 --model_suffix '_B' --no_dropout"
title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather"
description = ""
article = ""
def reset_interface():
return gr.update(value=None), gr.update(visible=False)
def resize_image(img):
# 将图片调整为256x256分辨率
return img.resize((256, 256), Image.BICUBIC)
def check_resolution(img):
# 获取图片分辨率
width, height = img.size
# 检查分辨率是否符合要求
if (width == 256 and height == 256) or (width == 64 and height == 64):
return True
else:
return False
def inference(img):
try:
# Debugging: Check if image is correctly received
if img is None:
print("No image received!")
return None
if check_resolution(img)==False:
img = resize_image(img)
import sys
sys.argv = ['--model', '--dataroot', './data/', '--num_test', '1', '--no_dropout']
# Load options and set them up
opt = TestOptions().parse()
opt.num_threads = 0
opt.batch_size = 1
opt.serial_batches = True
opt.no_flip = True
opt.display_id = -1
opt.name = ''
opt.model_suffix = '_B'
opt.num_test = 1
opt.no_dropout = True
# Create model and set it up
dataset = create_dataset(opt)
model = create_model(opt)
model.setup(opt)
if opt.eval:
model.eval()
# Convert PIL image to tensor
img_tensor = transforms.ToTensor()(img.convert('RGB')).unsqueeze(0)
img_tensor = img_tensor.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) # Move to GPU if available
# Prepare data for the model
data = {'A':img_tensor,'A_paths':'./data/'}
model.set_input(data)
model.test()
# Get the output visuals
img_out = model.get_current_visuals()
output_img_tensor = img_out.get('fake')
print(f'type of output_img_tensor: {type(img_out)}')
if output_img_tensor is None:
print("No output from model!")
return None
if isinstance(output_img_tensor, torch.Tensor):
# 将张量转换回PIL图像
output_img = output_img_tensor.squeeze(0).cpu().detach().numpy().transpose(1, 2, 0)
output_img = (output_img * 0.5 + 0.5) * 255 # 假设输出在[-1, 1]之间标准化
output_img = output_img.astype('uint8')
output_img = Image.fromarray(output_img)
print(f'type if output_img_tensor: {type(output_img_tensor)}')
return output_img
else:
print(f"意外的输出类型: {type(output_img_tensor)}")
return None
except Exception as e:
print(f"Error during inference: {e}")
return None
example_images = [
"img/1.png"
]
with gr.Blocks() as demo:
gr.Markdown(f"### {title}")
gr.Markdown(description)
with gr.Row():
with gr.Column():
img_input = gr.Image(type="pil", label="Upload an Image")
submit_btn = gr.Button("Submit...")
with gr.Column():
output = gr.Image(type="pil", label="Prediction Result")
submit_btn.click(fn=inference, inputs=img_input, outputs=output)
demo.load(reset_interface, None, output)
gr.Examples(
examples=example_images,
inputs=img_input,
)
demo.launch()
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