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3cdac71 a57ce4a 570027d a57ce4a 570027d a57ce4a 6a3c00f a57ce4a 570027d 01af49e 570027d a57ce4a 6a3c00f 570027d 6a3c00f 3cdac71 01af49e a57ce4a 01af49e a57ce4a 570027d 01af49e 6a3c00f 570027d 01af49e 570027d 01af49e a57ce4a 01af49e 570027d 01af49e 570027d 01af49e 570027d 01af49e 570027d a57ce4a 05fba58 | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 | # import os
# import torch
# import torchvision.transforms as T
# import torchvision.transforms.functional as TF
# import numpy as np
# from PIL import Image
# from flask import Flask, render_template, request, send_file, abort
# app = Flask(__name__)
# device = "cuda" if torch.cuda.is_available() else "cpu"
# # Load model (assuming UNet is defined in unet.py)
# def load_model():
# try:
# from unet import UNet
# model = UNet().to(device)
# model_path = "unet_car_final.pth"
# if not os.path.exists(model_path):
# raise FileNotFoundError(f"Model file {model_path} not found")
# model.load_state_dict(torch.load(model_path, map_location=device))
# model.eval()
# return model
# except Exception as e:
# print(f"Error loading model: {e}")
# raise
# try:
# model = load_model()
# except Exception as e:
# print(f"Model loading failed: {e}")
# model = None
# # Image transforms
# img_transform = T.Compose([
# T.Resize((256, 256)),
# T.ToTensor(),
# T.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# ])
# TMP_FOLDER = "/tmp"
# os.makedirs(TMP_FOLDER, exist_ok=True)
# # Route to serve files from /tmp
# @app.route('/tmp/<filename>')
# def serve_tmp_file(filename):
# file_path = os.path.join(TMP_FOLDER, filename)
# if os.path.exists(file_path):
# return send_file(file_path)
# else:
# print(f"File not found: {file_path}")
# abort(404)
# @app.route("/", methods=["GET", "POST"])
# def index():
# orig = None
# mask = None
# overlay = None
# error = None
# # Check for existing input image
# img_path = os.path.join(TMP_FOLDER, "input.jpg")
# if os.path.exists(img_path):
# orig = "/tmp/input.jpg"
# print(f"Found existing image: {img_path}")
# if request.method == "POST":
# # Handle image upload
# if "image" in request.files:
# file = request.files["image"]
# if file.filename == "":
# error = "No file selected"
# print(error)
# return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
# try:
# # Save uploaded image to /tmp
# file.save(img_path)
# print(f"Image saved to: {img_path}")
# orig = "/tmp/input.jpg"
# # Clear previous results in /tmp
# for path in [os.path.join(TMP_FOLDER, "mask.png"), os.path.join(TMP_FOLDER, "overlay.png")]:
# if os.path.exists(path):
# os.remove(path)
# print(f"Removed: {path}")
# except Exception as e:
# error = f"Error uploading image: {str(e)}"
# print(f"Upload error: {e}")
# return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
# # Handle segmentation
# if "segment" in request.form:
# if not os.path.exists(img_path):
# error = "No image available for segmentation"
# print(f"Segmentation error: Image not found at {img_path}")
# return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
# try:
# if model is None:
# raise ValueError("Model not loaded")
# image = Image.open(img_path).convert("RGB")
# input_tensor = img_transform(image).unsqueeze(0).to(device)
# # Predict
# with torch.no_grad():
# output = model(input_tensor)
# pred_mask = torch.sigmoid(output)
# pred_mask = (pred_mask > 0.5).float()
# # Resize mask back to original image size
# mask_resized = TF.resize(
# TF.to_pil_image(pred_mask.squeeze().cpu()),
# size=image.size[::-1],
# interpolation=Image.NEAREST
# )
# # Save mask to /tmp
# mask_path = os.path.join(TMP_FOLDER, "mask.png")
# mask_resized.save(mask_path)
# print(f"Mask saved to: {mask_path}")
# # Create overlay
# mask_np = np.array(mask_resized)
# overlay = np.array(image).copy()
# overlay[mask_np > 128] = [255, 0, 0]
# overlay_img = Image.fromarray(overlay)
# overlay_path = os.path.join(TMP_FOLDER, "overlay.png")
# overlay_img.save(overlay_path)
# print(f"Overlay saved to: {overlay_path}")
# mask = "/tmp/mask.png"
# overlay = "/tmp/overlay.png"
# except Exception as e:
# error = f"Error during segmentation: {str(e)}"
# print(f"Segmentation error: {e}")
# return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
# return render_template("index.html", orig=orig, mask=mask, overlay=overlay, error=error)
# if __name__ == "__main__":
# app.run(debug=True)
import os
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
import numpy as np
from PIL import Image
from flask import Flask, render_template, request, send_file, abort
app = Flask(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model (assuming UNet is defined in unet.py)
def load_model():
try:
from unet import UNet
model = UNet().to(device)
model_path = "unet_car_final.pth"
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file {model_path} not found")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
return model
except Exception as e:
print(f"Error loading model: {e}")
raise
try:
model = load_model()
except Exception as e:
print(f"Model loading failed: {e}")
model = None
# Image transforms
img_transform = T.Compose([
T.Resize((256, 256)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
TMP_FOLDER = "/tmp"
os.makedirs(TMP_FOLDER, exist_ok=True)
# Route to serve files from /tmp
@app.route('/tmp/<filename>')
def serve_tmp_file(filename):
file_path = os.path.join(TMP_FOLDER, filename)
if os.path.exists(file_path):
return send_file(file_path)
else:
print(f"File not found: {file_path}")
abort(404)
@app.route("/", methods=["GET", "POST"])
def index():
orig = None
mask = None
overlay = None
error = None
if request.method == "GET":
# Clear all relevant files in /tmp when a user accesses the root route
for filename in ["input.jpg", "mask.png", "overlay.png"]:
file_path = os.path.join(TMP_FOLDER, filename)
if os.path.exists(file_path):
try:
os.remove(file_path)
print(f"Cleared file: {file_path}")
except Exception as e:
print(f"Error clearing file {file_path}: {e}")
# Check for existing input image (will be None since we cleared /tmp/input.jpg)
img_path = os.path.join(TMP_FOLDER, "input.jpg")
if os.path.exists(img_path):
orig = "/tmp/input.jpg"
print(f"Found existing image: {img_path}")
if request.method == "POST":
# Handle image upload
if "image" in request.files:
file = request.files["image"]
if file.filename == "":
error = "No file selected"
print(error)
return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
try:
# Save uploaded image to /tmp
file.save(img_path)
print(f"Image saved to: {img_path}")
orig = "/tmp/input.jpg"
# Clear previous results in /tmp
for path in [os.path.join(TMP_FOLDER, "mask.png"), os.path.join(TMP_FOLDER, "overlay.png")]:
if os.path.exists(path):
os.remove(path)
print(f"Removed: {path}")
except Exception as e:
error = f"Error uploading image: {str(e)}"
print(f"Upload error: {e}")
return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
# Handle segmentation
if "segment" in request.form:
if not os.path.exists(img_path):
error = "No image available for segmentation"
print(f"Segmentation error: Image not found at {img_path}")
return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
try:
if model is None:
raise ValueError("Model not loaded")
image = Image.open(img_path).convert("RGB")
input_tensor = img_transform(image).unsqueeze(0).to(device)
# Predict
with torch.no_grad():
output = model(input_tensor)
pred_mask = torch.sigmoid(output)
pred_mask = (pred_mask > 0.5).float()
# Resize mask back to original image size
mask_resized = TF.resize(
TF.to_pil_image(pred_mask.squeeze().cpu()),
size=image.size[::-1],
interpolation=Image.NEAREST
)
# Save mask to /tmp
mask_path = os.path.join(TMP_FOLDER, "mask.png")
mask_resized.save(mask_path)
print(f"Mask saved to: {mask_path}")
# Create overlay
mask_np = np.array(mask_resized)
overlay = np.array(image).copy()
overlay[mask_np > 128] = [255, 0, 0]
overlay_img = Image.fromarray(overlay)
overlay_path = os.path.join(TMP_FOLDER, "overlay.png")
overlay_img.save(overlay_path)
print(f"Overlay saved to: {overlay_path}")
mask = "/tmp/mask.png"
overlay = "/tmp/overlay.png"
except Exception as e:
error = f"Error during segmentation: {str(e)}"
print(f"Segmentation error: {e}")
return render_template("index.html", error=error, orig=orig, mask=mask, overlay=overlay)
return render_template("index.html", orig=orig, mask=mask, overlay=overlay, error=error)
if __name__ == "__main__":
app.run(debug=True) |