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76479e1 e68f6e0 76479e1 65cfbb9 | 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 | import torch
import torch.nn as nn
import segmentation_models_pytorch as smp
from torchvision import transforms
from PIL import Image
import numpy as np
import base64
import io
import cv2
from flask import Flask, request, jsonify, send_from_directory
import os
app = Flask(__name__, static_folder="static")
# ── Model — exact same as training ────────────────────────────
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = smp.Unet(
encoder_name="efficientnet-b3",
encoder_weights=None, # no pretrained needed at inference
in_channels=3,
classes=1,
activation=None, # raw logits, same as training
)
MODEL_PATH = "best_model.pth"
if os.path.exists(MODEL_PATH):
model.load_state_dict(
torch.load(MODEL_PATH, map_location=device, weights_only=True)
)
print(f"✅ Model loaded — device: {device}")
else:
print("⚠️ best_model.pth not found — running in demo mode")
model.to(device)
model.eval()
# ── Preprocessing — matches val_transform from training ───────
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
def preprocess(pil_img, patch_size=256):
"""Resize to nearest multiple of patch_size, normalize, tensorize."""
img = np.array(pil_img.convert("RGB"))
h, w = img.shape[:2]
# Pad to multiple of patch_size
new_h = ((h + patch_size - 1) // patch_size) * patch_size
new_w = ((w + patch_size - 1) // patch_size) * patch_size
padded = np.zeros((new_h, new_w, 3), dtype=np.float32)
padded[:h, :w] = img
# Normalize
padded = padded / 255.0
padded = (padded - mean) / std
tensor = torch.tensor(padded).permute(2, 0, 1).float().unsqueeze(0)
return tensor, (h, w)
def run_inference(pil_img, patch_size=256):
"""Run patch-based inference matching training patch extraction."""
img = np.array(pil_img.convert("RGB"))
h, w = img.shape[:2]
# Pad to multiple of patch_size
new_h = ((h + patch_size - 1) // patch_size) * patch_size
new_w = ((w + patch_size - 1) // patch_size) * patch_size
padded = np.zeros((new_h, new_w, 3), dtype=np.uint8)
padded[:h, :w] = img
full_mask = np.zeros((new_h, new_w), dtype=np.float32)
for i in range(0, new_h, patch_size):
for j in range(0, new_w, patch_size):
patch = (
padded[i : i + patch_size, j : j + patch_size].astype(np.float32)
/ 255.0
)
patch = (patch - mean) / std
tensor = (
torch.tensor(patch).permute(2, 0, 1).float().unsqueeze(0).to(device)
)
with torch.no_grad():
out = model(tensor)
prob = torch.sigmoid(out).squeeze().cpu().numpy()
full_mask[i : i + patch_size, j : j + patch_size] = prob
return full_mask[:h, :w], img
# ── Zoning + illegal detection (from your notebook) ───────────
def create_zoning_mask(shape):
h, w = shape
zoning = np.zeros((h, w), dtype=np.uint8)
zoning[:, w // 2 :] = 1
return zoning
def detect_illegal_buildings(binary_mask, zoning_mask):
num_labels, labels = cv2.connectedComponents(binary_mask.astype(np.uint8))
illegal, legal = [], []
for label in range(1, num_labels):
building_pixels = labels == label
if (building_pixels & (zoning_mask == 1)).any():
illegal.append(label)
else:
legal.append(label)
return illegal, legal, labels
def to_base64(arr_or_img):
if isinstance(arr_or_img, np.ndarray):
img = Image.fromarray(arr_or_img.astype(np.uint8))
else:
img = arr_or_img
buf = io.BytesIO()
img.save(buf, format="PNG")
return base64.b64encode(buf.getvalue()).decode()
# ── Routes ────────────────────────────────────────────────────
@app.route("/")
def index():
return send_from_directory("static", "index.html")
@app.route("/predict", methods=["POST"])
def predict():
if "image" not in request.files:
return jsonify({"error": "No image provided"}), 400
file = request.files["image"]
pil_img = Image.open(file.stream).convert("RGB")
# Run patch-based segmentation
prob_mask, orig_rgb = run_inference(pil_img, patch_size=256)
binary_mask = (prob_mask > 0.5).astype(np.uint8)
# Zoning-based illegal detection
zoning_mask = create_zoning_mask(binary_mask.shape)
illegal, legal, labels = detect_illegal_buildings(binary_mask, zoning_mask)
# Build overlay: illegal=red, legal=green
overlay = orig_rgb.copy()
for lbl in illegal:
overlay[labels == lbl] = [255, 0, 0]
for lbl in legal:
overlay[labels == lbl] = [0, 200, 100]
total = len(illegal) + len(legal)
illegal_pct = round(float(binary_mask.mean() * 100), 2)
verdict = "ILLEGAL CONSTRUCTION DETECTED" if illegal else "NO VIOLATION DETECTED"
return jsonify(
{
"verdict": verdict,
"illegal_count": len(illegal),
"legal_count": len(legal),
"total_count": total,
"illegal_percent": illegal_pct,
"device": str(device),
"original": to_base64(orig_rgb),
"mask": to_base64((prob_mask * 255).astype(np.uint8)),
"overlay": to_base64(overlay),
}
)
if __name__ == "__main__":
print(f"Running on: {device}")
app.run(host="0.0.0.0",debug=True, port=7860)
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