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from pet_seg_core.model import UNet
from pet_seg_core.config import PetSegWebappConfig
from pet_seg_core.gdrive_utils import GDriveUtils
from torchvision import transforms as T
import torch
import gradio as gr
import numpy as np
import cv2
from dotenv import load_dotenv
load_dotenv()
device = torch.device("cpu")
if PetSegWebappConfig.DOWNLOAD_MODEL_WEIGTHS_FROM_GDRIVE:
GDriveUtils.download_file_from_gdrive(
PetSegWebappConfig.MODEL_WEIGHTS_GDRIVE_FILE_ID, PetSegWebappConfig.MODEL_WEIGHTS_LOCAL_PATH
)
model = UNet.load_from_checkpoint(PetSegWebappConfig.MODEL_WEIGHTS_LOCAL_PATH).to(device)
model.eval()
def segment_image(img):
img = T.ToTensor()(img).unsqueeze(0).to(device)
mask = model(img)
mask = torch.argmax(mask, dim = 1).squeeze().detach().cpu().numpy()
return mask
def overlay_mask(img, mask, alpha=0.5):
# Define color mapping
colors = {
0: [255, 0, 0], # Class 0 - Red
1: [0, 255, 0], # Class 1 - Green
2: [0, 0, 255] # Class 2 - Blue
# Add more colors for additional classes if needed
}
# Create a blank colored overlay image
overlay = np.zeros_like(img)
# Map each mask value to the corresponding color
for class_id, color in colors.items():
overlay[mask == class_id] = color
# Blend the overlay with the original image
output = cv2.addWeighted(img, 1 - alpha, overlay, alpha, 0)
return output
def transform(img):
mask=segment_image(img)
blended_img = overlay_mask(img, mask)
return blended_img
app = gr.Interface(
fn=transform,
inputs=gr.Image(label="Input Image"),
outputs=gr.Image(label="Image with Segmentation Overlay"),
title="Image Segmentation on Pet Images",
description="Segment image of a pet animal into three classes: background, pet, and boundary.",
examples=[
"example_images/img1.jpg",
"example_images/img2.jpg",
"example_images/img3.jpg"
]
) |