forestaiUs/treeextraction-ndvi

1import os2import logging3import uuid4import numpy as np5from PIL import Image6import json7 8# Try to import GDAL, but provide fallback for environments without it9try:10    from osgeo import gdal, ogr, osr11    HAS_GDAL = True12except ImportError:13    logging.warning("GDAL not available. Using simplified GeoJSON conversion.")14    HAS_GDAL = False15 16def convert_to_geojson(image_path):17    """18    Convert a processed image to GeoJSON format.19    This function extracts features from the processed image and converts them20    to GeoJSON polygons or linestrings.21    22    Args:23        image_path (str): Path to the processed image24    25    Returns:26        dict: GeoJSON object27    """28    try:29        logging.info(f"Converting image to GeoJSON: {image_path}")30        31        # Open the image32        img = Image.open(image_path)33        img_array = np.array(img)34        35        # Create a simple GeoJSON structure36        geojson = {37            "type": "FeatureCollection",38            "features": []39        }40        41        # Extract contours from the image42        # In a real application, we would use OpenCV's findContours here43        # Since we're simulating it, we'll create a simplified process44        height, width = img_array.shape45        46        # Create a random bounding box as a demo47        # In a real application, this would be based on actual image analysis48        feature_id = 049        50        # Process the image to find contours51        # (For simplicity, we'll simulate finding features by looking at non-zero pixels)52        visited = np.zeros_like(img_array, dtype=bool)53        54        for y in range(0, height, 10):  # Step by 10 for performance55            for x in range(0, width, 10):  # Step by 10 for performance56                if img_array[y, x] > 0 and not visited[y, x]:57                    # Found a feature, trace its boundary58                    feature_id += 159                    60                    # Simplified feature extraction - in a real app this would be more sophisticated61                    # Here we'll just create a small polygon around the point62                    coords = []63                    size = min(20, min(width-x, height-y))64                    65                    # Create a simple polygon66                    polygon = [67                        [x, y],68                        [x + size, y],69                        [x + size, y + size],70                        [x, y + size],71                        [x, y]  # Close the polygon72                    ]73                    74                    # Convert pixel coordinates to approximate geo-coordinates75                    # In a real application, this would use proper geo-referencing76                    # Here we'll just normalize to [0,1] range and then to fake lat/long77                    geo_polygon = []78                    for px, py in polygon:79                        # Convert to fake geographic coordinates (for demo purposes)80                        lon = (px / width) * 0.1 - 74.0  # Fake longitude centered around New York81                        lat = (py / height) * 0.1 + 40.7  # Fake latitude centered around New York82                        geo_polygon.append([lon, lat])83                    84                    # Add the feature to GeoJSON85                    feature = {86                        "type": "Feature",87                        "id": feature_id,88                        "properties": {89                            "name": f"Feature {feature_id}",90                            "value": int(img_array[y, x])91                        },92                        "geometry": {93                            "type": "Polygon",94                            "coordinates": [geo_polygon]95                        }96                    }97                    98                    geojson["features"].append(feature)99                    100                    # Mark this area as visited101                    for cy in range(y, min(y + size, height)):102                        for cx in range(x, min(x + size, width)):103                            visited[cy, cx] = True104        105        logging.info(f"Converted image to GeoJSON with {feature_id} features")106        return geojson107        108    except Exception as e:109        logging.error(f"Error in GeoJSON conversion: {str(e)}")110        # Return a minimal valid GeoJSON if there's an error111        return {"type": "FeatureCollection", "features": []}112