import os
import logging
import uuid
import numpy as np
from PIL import Image
import json

# Try to import GDAL, but provide fallback for environments without it
try:
    from osgeo import gdal, ogr, osr
    HAS_GDAL = True
except ImportError:
    logging.warning("GDAL not available. Using simplified GeoJSON conversion.")
    HAS_GDAL = False

def convert_to_geojson(image_path):
    """
    Convert a processed image to GeoJSON format.
    This function extracts features from the processed image and converts them
    to GeoJSON polygons or linestrings.
    
    Args:
        image_path (str): Path to the processed image
    
    Returns:
        dict: GeoJSON object
    """
    try:
        logging.info(f"Converting image to GeoJSON: {image_path}")
        
        # Open the image
        img = Image.open(image_path)
        img_array = np.array(img)
        
        # Create a simple GeoJSON structure
        geojson = {
            "type": "FeatureCollection",
            "features": []
        }
        
        # Extract contours from the image
        # In a real application, we would use OpenCV's findContours here
        # Since we're simulating it, we'll create a simplified process
        height, width = img_array.shape
        
        # Create a random bounding box as a demo
        # In a real application, this would be based on actual image analysis
        feature_id = 0
        
        # Process the image to find contours
        # (For simplicity, we'll simulate finding features by looking at non-zero pixels)
        visited = np.zeros_like(img_array, dtype=bool)
        
        for y in range(0, height, 10):  # Step by 10 for performance
            for x in range(0, width, 10):  # Step by 10 for performance
                if img_array[y, x] > 0 and not visited[y, x]:
                    # Found a feature, trace its boundary
                    feature_id += 1
                    
                    # Simplified feature extraction - in a real app this would be more sophisticated
                    # Here we'll just create a small polygon around the point
                    coords = []
                    size = min(20, min(width-x, height-y))
                    
                    # Create a simple polygon
                    polygon = [
                        [x, y],
                        [x + size, y],
                        [x + size, y + size],
                        [x, y + size],
                        [x, y]  # Close the polygon
                    ]
                    
                    # Convert pixel coordinates to approximate geo-coordinates
                    # In a real application, this would use proper geo-referencing
                    # Here we'll just normalize to [0,1] range and then to fake lat/long
                    geo_polygon = []
                    for px, py in polygon:
                        # Convert to fake geographic coordinates (for demo purposes)
                        lon = (px / width) * 0.1 - 74.0  # Fake longitude centered around New York
                        lat = (py / height) * 0.1 + 40.7  # Fake latitude centered around New York
                        geo_polygon.append([lon, lat])
                    
                    # Add the feature to GeoJSON
                    feature = {
                        "type": "Feature",
                        "id": feature_id,
                        "properties": {
                            "name": f"Feature {feature_id}",
                            "value": int(img_array[y, x])
                        },
                        "geometry": {
                            "type": "Polygon",
                            "coordinates": [geo_polygon]
                        }
                    }
                    
                    geojson["features"].append(feature)
                    
                    # Mark this area as visited
                    for cy in range(y, min(y + size, height)):
                        for cx in range(x, min(x + size, width)):
                            visited[cy, cx] = True
        
        logging.info(f"Converted image to GeoJSON with {feature_id} features")
        return geojson
        
    except Exception as e:
        logging.error(f"Error in GeoJSON conversion: {str(e)}")
        # Return a minimal valid GeoJSON if there's an error
        return {"type": "FeatureCollection", "features": []}