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| import streamlit as st | |
| import sys | |
| sys.path.append('Utils') | |
| sys.path.append('model') | |
| import torch | |
| from model.CBAM.reunet_cbam import reunet_cbam | |
| import cv2 | |
| from PIL import Image | |
| from model.transform import transforms | |
| import numpy as np | |
| from model.unet import UNET | |
| from Utils.area import pixel_to_sqft, process_and_overlay_image | |
| import matplotlib.pyplot as plt | |
| import time | |
| import os | |
| import csv | |
| from datetime import datetime | |
| from Utils.split_merge import split, merge | |
| from Utils.convert import convert_gtiff_to_8bit | |
| import shutil | |
| # Define directories | |
| UPLOAD_DIR = "data/uploaded_images/" | |
| MASK_DIR = "data/generated_masks/" | |
| PATCHES_DIR = 'data/Patches/' | |
| PRED_PATCHES_DIR = 'data/Patch_pred/' | |
| CSV_LOG_PATH = "image_log.csv" | |
| # Create directories | |
| for directory in [UPLOAD_DIR, MASK_DIR, PATCHES_DIR, PRED_PATCHES_DIR]: | |
| os.makedirs(directory, exist_ok=True) | |
| # Load model | |
| model = reunet_cbam() | |
| model.load_state_dict(torch.load('latest.pth', map_location='cpu')['model_state_dict']) | |
| model.eval() | |
| def predict(image): | |
| with torch.no_grad(): | |
| output = model(image.unsqueeze(0)) | |
| return output.squeeze().cpu().numpy() | |
| def log_image_details(image_id, image_filename, mask_filename): | |
| file_exists = os.path.exists(CSV_LOG_PATH) | |
| current_time = datetime.now() | |
| date = current_time.strftime('%Y-%m-%d') | |
| time = current_time.strftime('%H:%M:%S') | |
| with open(CSV_LOG_PATH, mode='a', newline='') as file: | |
| writer = csv.writer(file) | |
| if not file_exists: | |
| writer.writerow(['S.No', 'Date', 'Time', 'Image ID', 'Image Filename', 'Mask Filename']) | |
| sno = sum(1 for row in open(CSV_LOG_PATH)) if file_exists else 1 | |
| writer.writerow([sno, date, time, image_id, image_filename, mask_filename]) | |
| def reset_state(): | |
| st.session_state.file_uploaded = False | |
| st.session_state.filename = None | |
| st.session_state.mask_filename = None | |
| st.session_state.tr_img = None | |
| if 'page' in st.session_state: | |
| del st.session_state.page | |
| def process_image(image, timestamp): | |
| filename = f"image_{timestamp}{os.path.splitext(image.name)[1]}" | |
| filepath = os.path.join(UPLOAD_DIR, filename) | |
| with open(filepath, "wb") as f: | |
| f.write(image.getvalue()) | |
| if filename.lower().endswith(('.tiff', '.tif')): | |
| st.info('Processing GeoTIFF image...') | |
| convert_gtiff_to_8bit(filepath) | |
| st.success('GeoTIFF converted to 8-bit image') | |
| return filename, filepath | |
| def predict_image(img_array, filename, timestamp): | |
| if img_array.shape[0] > 650 or img_array.shape[1] > 650: | |
| split(os.path.join(UPLOAD_DIR, filename), patch_size=256) | |
| with st.spinner('Analyzing...'): | |
| for patch_filename in os.listdir(PATCHES_DIR): | |
| if patch_filename.endswith(".png"): | |
| patch_path = os.path.join(PATCHES_DIR, patch_filename) | |
| patch_img = Image.open(patch_path) | |
| patch_tr_img = transforms(patch_img) | |
| prediction = predict(patch_tr_img) | |
| mask = (prediction > 0.5).astype(np.uint8) * 255 | |
| mask_filename = f"mask_{patch_filename}" | |
| mask_filepath = os.path.join(PRED_PATCHES_DIR, mask_filename) | |
| Image.fromarray(mask).save(mask_filepath) | |
| merged_mask_filename = f"mask_{timestamp}.png" | |
| merged_mask_filepath = os.path.join(MASK_DIR, merged_mask_filename) | |
| merge(PRED_PATCHES_DIR, merged_mask_filepath, img_array.shape) | |
| st.info('Cleaning up temporary files...') | |
| for dir in [PATCHES_DIR, PRED_PATCHES_DIR]: | |
| shutil.rmtree(dir) | |
| os.makedirs(dir) | |
| st.success('Temporary files cleaned up') | |
| else: | |
| tr_img = transforms(Image.open(os.path.join(UPLOAD_DIR, filename))) | |
| prediction = predict(tr_img) | |
| mask = (prediction > 0.5).astype(np.uint8) * 255 | |
| merged_mask_filename = f"mask_{timestamp}.png" | |
| merged_mask_filepath = os.path.join(MASK_DIR, merged_mask_filename) | |
| Image.fromarray(mask).save(merged_mask_filepath) | |
| return merged_mask_filepath | |
| def upload_page(): | |
| if 'file_uploaded' not in st.session_state: | |
| st.session_state.file_uploaded = False | |
| image = st.file_uploader('Choose a satellite image', type=['jpg', 'png', 'jpeg', 'tiff', 'tif']) | |
| if image is not None: | |
| reset_state() | |
| timestamp = int(time.time()) | |
| filename, filepath = process_image(image, timestamp) | |
| img = Image.open(filepath) | |
| st.image(img, caption='Uploaded Image', use_column_width=True) | |
| st.success(f'Image saved as {filename}') | |
| st.session_state.filename = filename | |
| img_array = np.array(img) | |
| mask_filepath = predict_image(img_array, filename, timestamp) | |
| st.session_state.mask_filename = mask_filepath | |
| log_image_details(timestamp, filename, os.path.basename(mask_filepath)) | |
| st.session_state.file_uploaded = True | |
| if st.session_state.file_uploaded and st.button('View result'): | |
| if st.session_state.filename is None: | |
| st.error("Please upload an image before viewing the result.") | |
| else: | |
| st.success('Image analyzed') | |
| st.session_state.page = 'result' | |
| st.rerun() | |
| def result_page(): | |
| st.title('Analysis Result') | |
| if 'filename' not in st.session_state or 'mask_filename' not in st.session_state: | |
| st.error("No image or mask file found. Please upload and process an image first.") | |
| if st.button('Back to Upload'): | |
| reset_state() | |
| st.rerun() | |
| return | |
| col1, col2 = st.columns(2) | |
| original_img_path = os.path.join(UPLOAD_DIR, st.session_state.filename) | |
| mask_path = st.session_state.mask_filename | |
| if os.path.exists(original_img_path): | |
| original_img = Image.open(original_img_path) | |
| col1.image(original_img, caption='Original Image', use_column_width=True) | |
| else: | |
| col1.error(f"Original image file not found: {original_img_path}") | |
| if os.path.exists(mask_path): | |
| mask = Image.open(mask_path) | |
| col2.image(mask, caption='Predicted Mask', use_column_width=True) | |
| else: | |
| col2.error(f"Predicted mask file not found: {mask_path}") | |
| st.subheader("Overlay with Area of Buildings (sqft)") | |
| if os.path.exists(original_img_path) and os.path.exists(mask_path): | |
| original_np = cv2.imread(original_img_path) | |
| mask_np = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) | |
| _, mask_np = cv2.threshold(mask_np, 127, 255, cv2.THRESH_BINARY) | |
| if original_np.shape[:2] != mask_np.shape[:2]: | |
| mask_np = cv2.resize(mask_np, (original_np.shape[1], original_np.shape[0])) | |
| overlay_img = process_and_overlay_image(original_np, mask_np, 'output.png') | |
| st.image(overlay_img, caption='Overlay Image', use_column_width=True) | |
| else: | |
| st.error("Image or mask file not found for overlay.") | |
| if st.button('Back to Upload'): | |
| reset_state() | |
| st.rerun() | |
| def main(): | |
| st.title('Building area estimation') | |
| if 'page' not in st.session_state: | |
| st.session_state.page = 'upload' | |
| if st.session_state.page == 'upload': | |
| upload_page() | |
| elif st.session_state.page == 'result': | |
| result_page() | |
| if __name__ == '__main__': | |
| main() |