import streamlit as st import sys import os import shutil import time from datetime import datetime import csv import cv2 import numpy as np from PIL import Image import torch from huggingface_hub import HfApi # Adjust import paths as needed sys.path.append('Utils') sys.path.append('model') from model.CBAM.reunet_cbam import reunet_cbam from model.transform import transforms from model.unet import UNET from Utils.area import pixel_to_sqft, process_and_overlay_image from Utils.convert import read_pansharpened_rgb # Initialize Hugging Face API hf_api = HfApi() # Get the token from secrets HF_TOKEN = st.secrets.get("HF_TOKEN") if not HF_TOKEN: st.error("HF_TOKEN not found in secrets. Please set it in your Space's Configuration > Secrets.") st.stop() # Your Space ID (this should match exactly with your Hugging Face Space URL) REPO_ID = "Pavan2k4/Building_area" REPO_TYPE = "space" # Define subdirectories UPLOAD_DIR = "uploaded_images" MASK_DIR = "generated_masks" PATCHES_DIR = "patches" PRED_PATCHES_DIR = "pred_patches" 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) def split(image, destination = PATCHES_DIR, patch_size = 650): img = cv2.imread(image) h,w,_ = img.shape for y in range(0, h, patch_size): for x in range(0, w, patch_size): patch = img[y:y+patch_size, x:x+patch_size] patch_filename = f"patch_{y}_{x}.png" patch_path = os.path.join(destination, patch_filename) cv2.imwrite(patch_path, patch) def merge(patch_folder , dest_image = 'out.png', image_shape = None): merged = np.zeros(image_shape[:-1] + (3,), dtype=np.uint8) for filename in os.listdir(patch_folder): if filename.endswith(".png"): patch_path = os.path.join(patch_folder, filename) patch = cv2.imread(patch_path) patch_height, patch_width, _ = patch.shape # Extract patch coordinates from filename parts = filename.split("_") x, y = None, None for part in parts: if part.endswith(".png"): x = int(part.split(".")[0]) elif part.isdigit(): y = int(part) if x is None or y is None: raise ValueError(f"Invalid filename: {filename}") # Check if patch fits within image boundaries if x + patch_width > image_shape[1] or y + patch_height > image_shape[0]: # Adjust patch position to fit within image boundaries if x + patch_width > image_shape[1]: x = image_shape[1] - patch_width if y + patch_height > image_shape[0]: y = image_shape[0] - patch_height # Merge patch into the main image merged[y:y+patch_height, x:x+patch_width, :] = patch cv2.imwrite(dest_image, merged) # Load model @st.cache_resource def load_model(): model = reunet_cbam() model.load_state_dict(torch.load('latest.pth', map_location='cpu')['model_state_dict']) model.eval() return model model = load_model() def predict(image): with torch.no_grad(): output = model(image.unsqueeze(0)) return output.squeeze().cpu().numpy() def save_to_hf_repo(local_path, repo_path): try: hf_api.upload_file( path_or_fileobj=local_path, path_in_repo=repo_path, repo_id=REPO_ID, repo_type=REPO_TYPE, token=HF_TOKEN ) except Exception as e: st.error(f"Error uploading file: {str(e)}") st.error("Detailed error information:") st.exception(e) 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']) # Get the next S.No if file_exists: with open(CSV_LOG_PATH, mode='r') as f: reader = csv.reader(f) sno = sum(1 for row in reader) else: sno = 1 writer.writerow([sno, date, time, image_id, image_filename, mask_filename]) # Save CSV to Hugging Face repo save_to_hf_repo(CSV_LOG_PATH, 'image_log.csv') def upload_page(): if 'file_uploaded' not in st.session_state: st.session_state.file_uploaded = False if 'filename' not in st.session_state: st.session_state.filename = None if 'mask_filename' not in st.session_state: st.session_state.mask_filename = None image = st.file_uploader('Choose a satellite image', type=['jpg', 'png', 'jpeg', 'tiff', 'tif']) if st.button('Back to Upload'): st.session_state.page = 'upload' st.session_state.file_uploaded = False st.session_state.filename = None st.session_state.mask_filename = None st.rerun() if image is not None and not st.session_state.file_uploaded: try: bytes_data = image.getvalue() timestamp = int(time.time()) original_filename = image.name file_extension = os.path.splitext(original_filename)[1].lower() if file_extension in ['.tiff', '.tif']: filename = f"image_{timestamp}.tif" converted_filename = f"image_{timestamp}_converted.png" else: filename = f"image_{timestamp}.png" converted_filename = filename filepath = os.path.join(UPLOAD_DIR, filename) converted_filepath = os.path.join(UPLOAD_DIR, converted_filename) with open(filepath, "wb") as f: f.write(bytes_data) st.success('File uploaded and saved') # Save image to Hugging Face repo save_to_hf_repo(filepath, f'uploaded_images/{filename}') # Check if the uploaded file is a GeoTIFF if file_extension in ['.tiff', '.tif']: st.info('Processing GeoTIFF image...') rgb_image = read_pansharpened_rgb(filepath) cv2.imwrite(converted_filepath, cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)) img = Image.open(converted_filepath) else: img = Image.open(filepath) st.image(img, caption='Uploaded Image', use_column_width=True) # Store the full path of the converted image st.session_state.filename = converted_filename # Convert image to numpy array img_array = np.array(img) # Check if image shape is more than 650x650 if img_array.shape[0] > 650 or img_array.shape[1] > 650: # Split image into patches split(converted_filepath, patch_size=512) # Display buffer while analyzing with st.spinner('Analyzing...'): # Predict on each patch 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) # Merge predicted patches merged_mask_filename = f"mask_{timestamp}.png" merged_mask_path = os.path.join(MASK_DIR, merged_mask_filename) merge(PRED_PATCHES_DIR, merged_mask_path, img_array.shape) # Save merged mask st.session_state.mask_filename = merged_mask_filename # Clean up temporary patch files # Clean up temporary patch files st.info('Cleaning up temporary files...') for filename in os.listdir(PATCHES_DIR): file_path = os.path.join(PATCHES_DIR, filename) if os.path.isfile(file_path): os.remove(file_path) for filename in os.listdir(PRED_PATCHES_DIR): file_path = os.path.join(PRED_PATCHES_DIR, filename) if os.path.isfile(file_path): os.remove(file_path) else: # Predict on whole image st.session_state.tr_img = transforms(img) prediction = predict(st.session_state.tr_img) mask = (prediction > 0.5).astype(np.uint8) * 255 mask_filename = f"mask_{timestamp}.png" mask_filepath = os.path.join(MASK_DIR, mask_filename) Image.fromarray(mask).save(mask_filepath) st.session_state.mask_filename = mask_filename st.success('Mask generated and saved') # Save mask to Hugging Face repo mask_filepath = os.path.join(MASK_DIR, st.session_state.mask_filename) save_to_hf_repo(mask_filepath, f'generated_masks/{st.session_state.mask_filename}') # Log image details log_image_details(timestamp, converted_filename, st.session_state.mask_filename) st.session_state.file_uploaded = True except Exception as e: st.error(f"An error occurred: {str(e)}") st.error("Please check the logs for more details.") print(f"Error in upload_page: {str(e)}") # This will appear in the Streamlit logs 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'): st.session_state.page = 'upload' st.session_state.file_uploaded = False st.session_state.filename = None st.session_state.mask_filename = None st.experimental_rerun() return col1, col2 = st.columns(2) # Display original image original_img_path = os.path.join(UPLOAD_DIR, st.session_state.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}") # Display predicted mask mask_path = os.path.join(MASK_DIR, st.session_state.mask_filename) 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)") # Display overlayed image 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) # Ensure mask is binary _, mask_np = cv2.threshold(mask_np, 127, 255, cv2.THRESH_BINARY) # Resize mask to match original image size if necessary if original_np.shape[:2] != mask_np.shape[:2]: mask_np = cv2.resize(mask_np, (original_np.shape[1], original_np.shape[0])) # Process and overlay image 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'): st.session_state.page = 'upload' st.session_state.file_uploaded = False st.session_state.filename = None st.session_state.mask_filename = None st.experimental_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()