app.py

import gradio as gr
import numpy as np
import cv2
from pathlib import Path
from typing import Tuple, Optional
import os

from models.cloud_detector import CloudDetector
from models.change_detector import ChangeDetector
from utils.preprocessing import preprocess_image, mask_clouds
from utils.visualization import create_overlay, visualize_predictions
from utils.evaluation import calculate_metrics
from utils.metrics import compare_with_without_masking, calculate_change_statistics


# Initialize models
device = "cuda" if os.environ.get("CUDA_VISIBLE_DEVICES") else "cpu"
cloud_detector = CloudDetector(device=device)
change_detector = ChangeDetector(device=device)


def load_example_images():
    """Load example images from examples directory."""
    examples_dir = Path("examples")

    examples = []
    before_files = sorted(
        list((examples_dir / "before").glob("*.png")) +
        list((examples_dir / "before").glob("*.jpg"))
    )
    after_files = sorted(
        list((examples_dir / "after").glob("*.png")) +
        list((examples_dir / "after").glob("*.jpg"))
    )

    for before_file, after_file in zip(before_files, after_files):
        before = cv2.imread(str(before_file))
        after  = cv2.imread(str(after_file))

        if before is not None and after is not None:
            before = cv2.cvtColor(before, cv2.COLOR_BGR2RGB)
            after  = cv2.cvtColor(after,  cv2.COLOR_BGR2RGB)
            examples.append([before, after])

    return examples


def detect_clouds_in_image(
    image: np.ndarray,
    cloud_threshold: float = 0.5
) -> Tuple[np.ndarray, str]:
    """
    Detect clouds in a single image.

    Args:
        image: Input image (H, W, 3)
        cloud_threshold: Confidence threshold

    Returns:
        Tuple of (overlay_image, stats_text)
    """
    if image is None:
        return None, "Please upload an image."

    # Preprocess (normalise to float [0,1])
    preprocessed = preprocess_image(image, normalize=True)

    # Detect clouds — returns 2D mask and 2D confidence map
    cloud_mask, cloud_confidence = cloud_detector.detect_clouds(
        preprocessed,
        threshold=cloud_threshold
    )

    # Create visualization overlay on original image
    overlay = create_overlay(image, cloud_mask, alpha=0.5, color=(0, 0, 255))

    # Statistics — all values are now properly 2D arrays
    total_pixels  = int(cloud_mask.size)
    cloud_pixels  = int(np.sum(cloud_mask))
    cloud_pct     = 100.0 * cloud_pixels / total_pixels if total_pixels > 0 else 0.0
    mean_conf     = float(cloud_confidence.mean())
    max_conf      = float(cloud_confidence.max())
    min_conf      = float(cloud_confidence.min())

    stats_text = (
        f"Cloud Detection Results:\n"
        f"─────────────────────\n"
        f"Cloud Pixels:       {cloud_pixels}\n"
        f"Total Pixels:       {total_pixels}\n"
        f"Cloud Percentage:   {cloud_pct:.2f}%\n"
        f"Mean Confidence:    {mean_conf:.4f}\n"
        f"Max Confidence:     {max_conf:.4f}\n"
        f"Min Confidence:     {min_conf:.4f}"
    )

    return overlay, stats_text


def detect_changes(
    before_image: np.ndarray,
    after_image: np.ndarray,
    apply_cloud_masking: bool = True,
    cloud_threshold: float = 0.5,
    change_threshold: float = 0.5
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, str, str]:
    """
    Detect changes between two temporal images.

    Returns:
        Tuple of (before_overlay, after_overlay, change_mask_vis,
                  metrics_text, stats_text)
    """
    if before_image is None or after_image is None:
        empty = np.zeros((224, 224, 3), dtype=np.uint8)
        return empty, empty, empty, "Please upload both images.", ""

    # Resize both to the same size before processing
    TARGET = (512, 512)
    before_image = cv2.resize(before_image, TARGET, interpolation=cv2.INTER_LINEAR)
    after_image  = cv2.resize(after_image,  TARGET, interpolation=cv2.INTER_LINEAR)

    # Preprocess to float [0,1]
    before_preprocessed = preprocess_image(before_image, normalize=True)
    after_preprocessed  = preprocess_image(after_image,  normalize=True)

    cloud_mask = None

    if apply_cloud_masking:
        cloud_mask_before, _ = cloud_detector.detect_clouds(
            before_preprocessed, threshold=cloud_threshold
        )
        cloud_mask_after, _ = cloud_detector.detect_clouds(
            after_preprocessed, threshold=cloud_threshold
        )

        # Combined cloud mask (union of both)
        cloud_mask = np.logical_or(cloud_mask_before, cloud_mask_after).astype(np.uint8)

        before_masked = mask_clouds(before_preprocessed, cloud_mask, fill_value=0.0)
        after_masked  = mask_clouds(after_preprocessed,  cloud_mask, fill_value=0.0)
    else:
        before_masked = before_preprocessed
        after_masked  = after_preprocessed

    # Detect changes — now returns proper 2D arrays
    change_mask, change_confidence = change_detector.detect_changes(
        before_masked,
        after_masked,
        threshold=change_threshold
    )

    # Overlays on original images
    before_overlay = create_overlay(before_image, change_mask, alpha=0.5, color=(255, 0, 0))
    after_overlay  = create_overlay(after_image,  change_mask, alpha=0.5, color=(255, 0, 0))

    if cloud_mask is not None:
        cloud_overlay_before = create_overlay(before_image, cloud_mask, alpha=0.4, color=(0, 0, 255))
        cloud_overlay_after  = create_overlay(after_image,  cloud_mask, alpha=0.4, color=(0, 0, 255))
        before_overlay = (before_overlay * 0.5 + cloud_overlay_before * 0.5).astype(np.uint8)
        after_overlay  = (after_overlay  * 0.5 + cloud_overlay_after  * 0.5).astype(np.uint8)

    # Change mask visualisation (white = changed)
    change_mask_vis = (change_mask * 255).astype(np.uint8)
    change_mask_vis = cv2.cvtColor(change_mask_vis, cv2.COLOR_GRAY2RGB)

    # Statistics from 2D arrays — all values are valid now
    stats = calculate_change_statistics(change_mask, change_confidence)

    metrics_text = (
        f"Change Detection Metrics:\n"
        f"─────────────────────────\n"
        f"Mean Confidence:    {float(change_confidence.mean()):.4f}\n"
        f"Max Confidence:     {float(change_confidence.max()):.4f}\n"
        f"Min Confidence:     {float(change_confidence.min()):.4f}\n"
        f"Algorithm:          Siamese ViT\n"
        f"Cloud Masking:      {'Yes' if apply_cloud_masking else 'No'}"
    )

    # Safe access to change_confidence_mean
    if stats["changed_pixels"] > 0:
        change_conf_line = (
            f"Change Region Confidence: {stats['change_confidence_mean']:.4f}"
        )
    else:
        change_conf_line = "No changes detected above threshold"

    stats_text = (
        f"Change Statistics:\n"
        f"──────────────────\n"
        f"Total Pixels:       {stats['total_pixels']}\n"
        f"Changed Pixels:     {stats['changed_pixels']}\n"
        f"Unchanged Pixels:   {stats['unchanged_pixels']}\n"
        f"Change Percentage:  {stats['change_percentage']:.2f}%\n"
        f"Mean Confidence:    {stats['mean_confidence']:.4f}\n"
        f"Min Confidence:     {stats['min_confidence']:.4f}\n"
        f"Max Confidence:     {stats['max_confidence']:.4f}\n"
        f"{change_conf_line}"
    )

    return before_overlay, after_overlay, change_mask_vis, metrics_text, stats_text


def create_comparison_interface():
    """Create Gradio interface for change detection comparison."""

    with gr.Blocks(title="Satellite Change Detector") as demo:
        gr.Markdown(
            """
            # Satellite Change Detection System

            Detect changes in Sentinel-2 satellite imagery using Vision Transformer models.
            Compare results with and without cloud masking.
            """
        )

        with gr.Tabs():
            # ── Cloud Detection Tab ──────────────────────────────────────────
            with gr.Tab("Cloud Detection"):
                gr.Markdown("### Detect and visualize clouds in satellite imagery")

                with gr.Row():
                    with gr.Column():
                        cloud_input = gr.Image(label="Input Image", type="numpy")
                        cloud_threshold = gr.Slider(
                            0, 1, value=0.5, step=0.01,
                            label="Cloud Detection Threshold"
                        )
                        cloud_detect_btn = gr.Button("Detect Clouds")

                    with gr.Column():
                        cloud_overlay_output = gr.Image(label="Cloud Detection Result")
                        cloud_stats_output = gr.Textbox(label="Statistics", lines=8)

                cloud_detect_btn.click(
                    detect_clouds_in_image,
                    inputs=[cloud_input, cloud_threshold],
                    outputs=[cloud_overlay_output, cloud_stats_output]
                )

            # ── Change Detection Tab ─────────────────────────────────────────
            with gr.Tab("Change Detection"):
                gr.Markdown("### Detect changes between two temporal satellite images")

                with gr.Row():
                    with gr.Column():
                        before_img = gr.Image(label="Before Image", type="numpy")
                        after_img  = gr.Image(label="After Image",  type="numpy")

                    with gr.Column():
                        gr.Markdown("### Settings")
                        apply_masking = gr.Checkbox(
                            value=True,
                            label="Apply Cloud Masking"
                        )
                        cloud_thresh = gr.Slider(
                            0, 1, value=0.5, step=0.01,
                            label="Cloud Threshold"
                        )
                        change_thresh = gr.Slider(
                            0, 1, value=0.5, step=0.01,
                            label="Change Threshold"
                        )
                        detect_btn = gr.Button("Detect Changes", size="lg")

                with gr.Row():
                    before_overlay_output = gr.Image(label="Before with Changes")
                    after_overlay_output  = gr.Image(label="After with Changes")

                with gr.Row():
                    change_mask_output = gr.Image(label="Change Mask")
                    metrics_output     = gr.Textbox(label="Metrics", lines=8)

                stats_output = gr.Textbox(label="Change Statistics", lines=10)

                detect_btn.click(
                    detect_changes,
                    inputs=[before_img, after_img, apply_masking, cloud_thresh, change_thresh],
                    outputs=[
                        before_overlay_output,
                        after_overlay_output,
                        change_mask_output,
                        metrics_output,
                        stats_output
                    ]
                )

            # ── Examples Tab ─────────────────────────────────────────────────
            with gr.Tab("Examples"):
                gr.Markdown("### Pre-loaded example images")

                examples = load_example_images()

                if examples:
                    for idx, (before, after) in enumerate(examples[:3]):
                        with gr.Row():
                            gr.Image(value=before, label=f"Example {idx+1}: Before")
                            gr.Image(value=after,  label=f"Example {idx+1}: After")
                else:
                    gr.Markdown(
                        "No example images found in `examples/` directory.\n"
                        "Run `python setup_oscd.py` to download OSCD samples."
                    )

        gr.Markdown(
            """
            ## About

            This application uses Vision Transformer (ViT) models for:
            - **Cloud Detection**: Identifies and masks cloud cover in satellite imagery
            - **Change Detection**: Detects land cover changes between multi-temporal observations

            Models are fine-tuned on Sentinel-2 satellite data.
            """
        )

    return demo


if __name__ == "__main__":
    demo = create_comparison_interface()
    demo.launch(share=True)