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import gradio as gr
from PIL import Image
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision
import numpy as np
import yaml
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = torch.load('Models/haze_detection.pt', map_location=device)
model = model.to(device)
def load_img (filename):
img = Image.open(filename).convert("RGB")
img_tensor = pil_to_tensor(img)
return img_tensor
def process_img(image):
img = np.array(image)
img = img / 255.
img = img.astype(np.float32)
y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device)
with torch.no_grad():
result = model(y)
restored_img = result.squeeze().permute(1,2,0).clamp_(0, 1).cpu().detach().numpy()
restored_img = np.clip(restored_img, 0. , 1.)
restored_img = (restored_img * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(restored_img)
title = "Efficient Hazy Vehicle Detection ✏️[] 🤗"
description = ''' ## [Efficient Hazy Vehicle Detection](https://github.com/cidautai)
[Paula Garrido Mellado](https://github.com/paugar5)
Fundación Cidaut
> **Disclaimer:** please remember this is not a product, thus, you will notice some limitations.
**This demo expects an image with some degradations.**
Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K).
<br>
'''
examples = [['examples/dusttornado.jpg'],
['examples/foggy.jpg'],
['examples/haze.jpg'],
["examples/mist.jpg"],
["examples/rain_storm.jpg"]
["examples/sand_storm.jpg"]
["examples/snow_storm.jpg"]]
css = """
.image-frame img, .image-container img {
width: auto;
height: auto;
max-width: none;
}
"""
demo = gr.Interface(
fn = process_img,
inputs = [
gr.Image(type = 'pil', label = 'input')
],
outputs = [gr.Image(type='pil', label = 'output')],
title = title,
description = description,
examples = examples,
css = css
)
if __name__ == '__main__':
demo.launch()
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