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import cv2
import gradio as gr
import os
from PIL import Image, ImageEnhance
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
os.system("git clone https://github.com/xuebinqin/DIS")
os.system("mv DIS/IS-Net/* .")
from data_loader_cache import normalize, im_reader, im_preprocess
from models import *
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if not os.path.exists("saved_models"):
os.mkdir("saved_models")
os.system("mv isnet.pth saved_models/")
class GOSNormalize(object):
def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
self.mean = mean
self.std = std
def __call__(self, image):
image = normalize(image, self.mean, self.std)
return image
transform = transforms.Compose([GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0])])
def load_image(im_path, hypar):
im = im_reader(im_path)
im, im_shp = im_preprocess(im, hypar["cache_size"])
im = torch.divide(im, 255.0)
shape = torch.from_numpy(np.array(im_shp))
return transform(im).unsqueeze(0), shape.unsqueeze(0)
def build_model(hypar, device):
net = hypar["model"]
if hypar["model_digit"] == "half":
net.half()
for layer in net.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
net.to(device)
if hypar["restore_model"] != "":
net.load_state_dict(torch.load(hypar["model_path"] + "/" + hypar["restore_model"], map_location=device))
net.eval()
return net
def predict(net, inputs_val, shapes_val, hypar, device):
net.eval()
if hypar["model_digit"] == "full":
inputs_val = inputs_val.type(torch.FloatTensor)
else:
inputs_val = inputs_val.type(torch.HalfTensor)
inputs_val_v = Variable(inputs_val, requires_grad=False).to(device)
ds_val = net(inputs_val_v)[0]
pred_val = ds_val[0][0, :, :, :]
pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val, 0), (shapes_val[0][0], shapes_val[0][1]), mode='bilinear'))
ma = torch.max(pred_val)
mi = torch.min(pred_val)
pred_val = (pred_val - mi) / (ma - mi)
if device == 'cuda': torch.cuda.empty_cache()
return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
hypar = {}
hypar["model_path"] = "./saved_models"
hypar["restore_model"] = "isnet.pth"
hypar["interm_sup"] = False
hypar["model_digit"] = "full"
hypar["seed"] = 0
hypar["cache_size"] = [1024, 1024]
hypar["input_size"] = [1024, 1024]
hypar["crop_size"] = [1024, 1024]
hypar["model"] = ISNetDIS()
net = build_model(hypar, device)
def inference(image):
image_path = image
image_tensor, orig_size = load_image(image_path, hypar)
mask = predict(net, image_tensor, orig_size, hypar, device)
pil_mask = Image.fromarray(mask).convert('L')
im_rgb = Image.open(image).convert("RGB")
im_rgba = im_rgb.copy()
im_rgba.putalpha(pil_mask)
return [im_rgba, pil_mask]
# Functions Added From Team
def rotate_image(image, degrees):
img = Image.open(image).rotate(degrees)
return img
def resize_image(image, width, height):
img = Image.open(image).resize((width, height))
return img
def convert_to_grayscale(image):
img = Image.open(image).convert('L')
return img
def adjust_brightness(image, brightness_factor):
img = Image.open(image)
enhancer = ImageEnhance.Brightness(img)
img_enhanced = enhancer.enhance(brightness_factor)
return img_enhanced
# Custom CSS Added From Team
custom_css = """
body {
background-color: #f0f0f0;
}
.gradio-container {
max-width: 900px;
margin: auto;
background-color: #ffffff;
padding: 20px;
border-radius: 12px;
box-shadow: 0px 4px 16px rgba(0, 0, 0, 0.2);
}
button.lg {
background-color: #4CAF50;
color: white;
border: none;
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: 16px;
margin: 4px 2px;
transition-duration: 0.4s;
cursor: pointer;
border-radius: 8px;
}
button.lg:hover {
background-color: #45a049;
color: white;
}
"""
# Used Some Codes From Yang's Chatbot
with gr.Blocks(css=custom_css) as interface:
gr.Markdown(f"# {title}")
gr.Markdown("<h1 style='text-align: center;'>🚩 Image Processor with Brightness Adjustment 🚩</h1>")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type='filepath')
rotate_button = gr.Button("Rotate Image")
resize_button = gr.Button("Resize Image")
grayscale_button = gr.Button("Convert to Grayscale")
brightness_slider = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Adjust Brightness")
submit_button = gr.Button("Submit", variant="primary")
clear_button = gr.Button("Clear", variant="secondary")
with gr.Column():
output_image = gr.Image(label="Output Image")
mask_image = gr.Image(label="Mask")
# AI Generated: Use Gradio Blocks to organize the interface with buttons
rotate_button.click(rotate_image, inputs=[input_image, gr.Slider(minimum=0, maximum=360, step=1, value=90, label="Rotation Degrees")], outputs=output_image)
resize_button.click(resize_image, inputs=[input_image, gr.Number(value=512, label="Width"), gr.Number(value=512, label="Height")], outputs=output_image)
grayscale_button.click(convert_to_grayscale, inputs=input_image, outputs=output_image)
brightness_slider.change(adjust_brightness, inputs=[input_image, brightness_slider], outputs=output_image)
submit_button.click(inference, inputs=input_image, outputs=[output_image, mask_image])
clear_button.click(lambda: (None, None, None), inputs=None, outputs=[input_image, output_image, mask_image])
interface.launch(share=True)
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