Spaces:
Sleeping
Sleeping
File size: 2,904 Bytes
db0960e 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 061386e 744e6f4 db0960e 744e6f4 061386e 744e6f4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import streamlit as st
import torch
import torch.nn as nn
import timm
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import torchvision.transforms as T
import matplotlib.pyplot as plt
from huggingface_hub import hf_hub_download
# ========== Model Definition ==========
class MobileViTSegmentation(nn.Module):
def __init__(self, encoder_name='mobilevit_s', pretrained=True):
super().__init__()
self.backbone = timm.create_model(encoder_name, features_only=True, pretrained=pretrained)
self.encoder_channels = self.backbone.feature_info.channels()
self.decoder = nn.Sequential(
nn.Conv2d(self.encoder_channels[-1], 128, kernel_size=3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(128, 64, kernel_size=3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(64, 32, kernel_size=3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(32, 1, kernel_size=1),
nn.Sigmoid()
)
def forward(self, x):
feats = self.backbone(x)
out = self.decoder(feats[-1])
out = nn.functional.interpolate(out, size=(x.shape[2], x.shape[3]), mode='bilinear', align_corners=False)
return out
# ========== Load Model ==========
@st.cache_resource
def load_model():
checkpoint_path = hf_hub_download(repo_id="svsaurav95/ToothSegmentation", filename="mobilevit_teeth_segmentation.pth")
model = MobileViTSegmentation(pretrained=False)
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
model.eval()
return model
model = load_model()
# ========== Image Transformation ==========
transform = T.Compose([
T.Resize((256, 256)),
T.ToTensor()
])
# ========== Streamlit UI ==========
st.title("Tooth Segmentation using MobileViT")
uploaded_file = st.file_uploader("Upload a mouth image with visible teeth", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file).convert("RGB")
input_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
pred_mask = model(input_tensor)[0, 0].numpy()
# Post-processing
pred_mask = (pred_mask > 0.7).astype(np.uint8) * 255
pred_mask = Image.fromarray(pred_mask).resize(image.size)
# Create overlay
overlay = Image.new("RGBA", image.size, (0, 0, 255, 100)) # Blue translucent
base = image.convert("RGBA")
pred_mask_rgba = Image.new("L", image.size, 0)
pred_mask_rgba.paste(255, mask=pred_mask)
final = Image.composite(overlay, base, pred_mask_rgba)
# Side-by-side display
col1, col2 = st.columns(2)
with col1:
st.image(image, caption="Original Image", use_container_width=True)
with col2:
st.image(final, caption="Tooth Segmentation Overlay", use_container_width=True)
|