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import torch
from torch import nn
import torchvision
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import pandas as pd
import segmentation_models_pytorch as smp
import gradio as gr
num_classes = 2
model_unet_path = "unet_model.pth"
model_fpn_path = "fpn_model.pth"
model_deeplab_path = "deeplabv3_model.pth"
image_path = "leaf11.jpg"
# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
print(f"Using {device} device")
model_unet = smp.Unet(
encoder_name="resnet18", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
encoder_weights=None, # use `imagenet` pre-trained weights for encoder initialization
in_channels=3, # model input channels (1 for gray-scale images, 3 for RGB, etc.)
classes=num_classes, # model output channels (number of classes in your dataset)
)
model_fpn = smp.FPN(
encoder_name="resnet18", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
encoder_weights=None, # use `imagenet` pre-trained weights for encoder initialization
in_channels=3, # model input channels (1 for gray-scale images, 3 for RGB, etc.)
classes=num_classes, # model output channels (number of classes in your dataset)
)
model_deeplab = smp.DeepLabV3(
encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
encoder_weights=None, # use `imagenet` pre-trained weights for encoder initialization
in_channels=3, # model input channels (1 for gray-scale images, 3 for RGB, etc.)
classes=num_classes, # model output channels (number of classes in your dataset)
)
def pred_one_image(inp,option):
one_image = np.array(inp.resize((256, 256)).convert("RGB"))
# convert to other format HWC -> CHW
one_image = np.moveaxis(one_image, -1, 0)
# mask = np.expand_dims(mask, 0)
one_image = torch.tensor(one_image).float()
one_image = one_image.unsqueeze(0)
one_image = one_image.to(device)
if option == "unet":
model_load = model_unet
elif option == "fpn":
model_load = model_fpn
elif option == "deeplab":
model_load = model_deeplab
model_load.eval()
with torch.no_grad():
output = model_load(one_image)
# print(output.shape)
predictions = torch.argmax(output, dim=1) # 获取预测的类别标签图像
pred_array = (predictions[0].cpu().numpy()/2*255).astype(np.uint8)
# print(pred_array.shape)
pred_img = Image.fromarray(pred_array)
# pred_img.save("pred.png")
# print(predictions.shape)
return pred_img
model_unet.load_state_dict(torch.load(model_unet_path,map_location=torch.device('cpu')))
model_fpn.load_state_dict(torch.load(model_fpn_path,map_location=torch.device('cpu')))
model_deeplab.load_state_dict(torch.load(model_deeplab_path,map_location=torch.device('cpu')))
dropdown = gr.Dropdown(["unet", "fpn","deeplab"])
interface = gr.Interface(fn=pred_one_image,
inputs=[gr.Image(type="pil"),dropdown],
outputs=gr.Image(type="pil"),
examples=[["leaf11.jpg",'unet']],)
interface.launch(debug=False)
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