app.py

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 hf_hub_download

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
from clean_refine import clean_mask,refine_mask

from huggingface_hub import HfApi, login
import os

# Set up Hugging Face authentication
HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("HF_TOKEN environment variable is not set")

login(token=HF_TOKEN)
hf_api = HfApi()

REPO_ID = "Pavan2k4/Building_area"
REPO_TYPE = "space"

@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






# save to dir func
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
        )
        st.success(f"File uploaded successfully to {repo_path}")
    except Exception as e:
        st.error(f"Error uploading file: {str(e)}")
        st.error("Detailed error information:")
        st.exception(e)


BASE_DIR = os.getcwd()

# Define subdirectories
UPLOAD_DIR = os.path.join(BASE_DIR, "uploaded_images")
MASK_DIR = os.path.join(BASE_DIR, "generated_masks")
PATCHES_DIR = os.path.join(BASE_DIR, "patches")
PRED_PATCHES_DIR = os.path.join(BASE_DIR, "pred_patches")
CSV_LOG_PATH = os.path.join(BASE_DIR, "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 = load_model()

def predict(image):
    with torch.no_grad():
        output = model(image.unsqueeze(0))
    return output.squeeze().cpu().numpy()

def split_image(image, patch_size=512):
    h, w, _ = image.shape
    patches = []
    for y in range(0, h, patch_size):
        for x in range(0, w, patch_size):
            patch = image[y:min(y+patch_size, h), x:min(x+patch_size, w)]
            patches.append((f"patch_{y}_{x}.png", patch))
    return patches

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)
    return merged

def process_large_image(model, image_path, patch_size=512):
    # Read the image
    img = cv2.imread(image_path)
    if img is None:
        raise ValueError(f"Failed to read image from {image_path}")

    h, w, _ = img.shape
    st.write(f"Processing image of size {w}x{h}")

    # Split the image into patches
    patches = split_image(img, patch_size)

    # Process each patch
    for filename, patch in patches:
        patch_pil = Image.fromarray(cv2.cvtColor(patch, cv2.COLOR_BGR2RGB))
        patch_transformed = transforms(patch_pil)
        prediction = predict(patch_transformed)
        mask = (prediction > 0.5).astype(np.uint8) * 255
        
        # Save the mask patch
        mask_filepath = os.path.join(PRED_PATCHES_DIR, filename)
        cv2.imwrite(mask_filepath, mask)

    # Merge the predicted patches
    merged_mask = merge(PRED_PATCHES_DIR, dest_image='merged_mask.png', image_shape=img.shape)

    return merged_mask

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])

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 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(f"Image saved to {filepath}")
            
            # Save image to Hugging Face repo----------------------------------------------------------------------------------------------------------------------------------
            # Save image to Hugging Face repo
            try:
                image_repo_path = f"images/{converted_filename}"
                save_to_hf_repo(converted_filepath, image_repo_path)
            except Exception as e:
                st.error(f"Error saving image to Hugging Face repo: {str(e)}")
            
            # 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))
                st.success(f'GeoTIFF converted to 8-bit image and saved as {converted_filename}')
                img = Image.open(converted_filepath)
            else:
                img = Image.open(filepath)
            
            st.image(img, caption='Uploaded Image', use_column_width=True)
            st.success(f'Image processed and saved as {converted_filename}')

            # 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:
                st.info('Large image detected. Using patch-based processing.')
                with st.spinner('Analyzing large image...'):
                    full_mask = process_large_image(model, converted_filepath)
            else:
                st.info('Small image detected. Processing whole image at once.')
                with st.spinner('Analyzing image...'):
                    img_transformed = transforms(img)
                    prediction = predict(img_transformed)
                    full_mask = (prediction > 0.5).astype(np.uint8) * 255
            full_mask = clean_mask(full_mask, morph_kernel_size=3, min_object_size=50)
            full_mask = refine_mask(full_mask, blur_kernel=5, edge_kernel=3, threshold_value=127)

            # Save the full mask
            mask_filename = f"mask_{timestamp}.png"
            mask_filepath = os.path.join(MASK_DIR, mask_filename)
            cv2.imwrite(mask_filepath, full_mask)
            st.session_state.mask_filename = mask_filename

            # Save mask to Hugging Face repo---------------------------------------------------------------------------------------------
            # Save mask to Hugging Face repo
            try:
                mask_repo_path = f"masks/{mask_filename}"
                save_to_hf_repo(mask_filepath, mask_repo_path)
            except Exception as e:
                st.error(f"Error saving mask to Hugging Face repo: {str(e)}")
            

            # Log image details
            log_image_details(timestamp, converted_filename, mask_filename)

            st.session_state.file_uploaded = True
            st.success("Image processed successfully")
            
        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.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.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()