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import os
import io
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import numpy as np
from flask import Flask, request, render_template, jsonify
import base64
# ===========================
# CONFIGURATION
# ===========================
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
img_size = 128 # same as used during training
model_path = "model/nail_segmentation_unet.pt"
app = Flask(__name__)
# ===========================
# MODEL DEFINITION (MATCHES TRAINING)
# ===========================
class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, in_ch=3, out_ch=1):
super().__init__()
self.dconv_down1 = DoubleConv(in_ch, 32)
self.dconv_down2 = DoubleConv(32, 64)
self.dconv_down3 = DoubleConv(64, 128)
self.dconv_down4 = DoubleConv(128, 256)
self.maxpool = nn.MaxPool2d(2)
self.up3 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.up2 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.up1 = nn.ConvTranspose2d(64, 32, 2, stride=2)
self.dconv_up3 = DoubleConv(256, 128)
self.dconv_up2 = DoubleConv(128, 64)
self.dconv_up1 = DoubleConv(64, 32)
self.conv_last = nn.Conv2d(32, out_ch, 1)
def forward(self, x):
conv1 = self.dconv_down1(x)
x = self.maxpool(conv1)
conv2 = self.dconv_down2(x)
x = self.maxpool(conv2)
conv3 = self.dconv_down3(x)
x = self.maxpool(conv3)
x = self.dconv_down4(x)
x = self.up3(x)
x = torch.cat([x, conv3], dim=1)
x = self.dconv_up3(x)
x = self.up2(x)
x = torch.cat([x, conv2], dim=1)
x = self.dconv_up2(x)
x = self.up1(x)
x = torch.cat([x, conv1], dim=1)
x = self.dconv_up1(x)
x = self.conv_last(x)
x = torch.sigmoid(x)
return x
# ===========================
# LOAD TRAINED MODEL
# ===========================
model = UNet().to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# ===========================
# IMAGE TRANSFORM
# ===========================
transform = transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) # match training normalization
])
# ===========================
# UTILITY FUNCTION
# ===========================
def encode_image(pil_img):
buffer = io.BytesIO()
pil_img.save(buffer, format="PNG")
return base64.b64encode(buffer.getvalue()).decode('utf-8')
# ===========================
# ROUTES
# ===========================
@app.route("/", methods=["GET"])
def index():
return render_template("index.html")
@app.route("/process", methods=["POST"])
def process_image():
if "image" not in request.files:
return jsonify({"error": "No file part"}), 400
file = request.files["image"]
if file.filename == "":
return jsonify({"error": "No selected file"}), 400
try:
image_pil = Image.open(file.stream).convert("RGB")
input_img_tensor = transform(image_pil).unsqueeze(0).to(device)
with torch.no_grad():
pred_mask = model(input_img_tensor)[0]
# Convert mask tensor to binary mask
mask_np = pred_mask.squeeze().cpu().numpy()
mask_binary = (mask_np > 0.5).astype(np.uint8) * 255
# Resize mask to original image size
mask_pil = Image.fromarray(mask_binary).resize(image_pil.size, Image.NEAREST)
# Encode images for frontend display
original_b64 = encode_image(image_pil)
mask_b64 = encode_image(mask_pil)
return jsonify({
"original_image": original_b64,
"mask_image": mask_b64
})
except Exception as e:
return jsonify({"error": f"An error occurred: {str(e)}"}), 500
# ===========================
# RUN APP
# ===========================
if __name__ == "__main__":
app.run(debug=True)
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