Improved map image display

Added shapely library and updated image processing to improve GeoJSON polygon generation, fixing issues with excessive polygons and incorrect map display. The `utils/geospatial.py`, `app.py`, `.replit` and `pyproject.toml` files were modified.

Replit-Commit-Author: Agent
Replit-Commit-Session-Id: c7b687d7-8856-49d8-87a3-9d7f3f6499f6
Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/d7727d0d-3b25-49de-9476-c76c61abfa65/751a3a96-c7ad-42a9-a160-a9b0b9f7217c.jpg

.replit

@@ -2,7 +2,7 @@ modules = ["python-3.11"]
 
 [nix]
 channel = "stable-24_05"
-packages = ["freetype", "lcms2", "libGL", "libGLU", "libimagequant", "libjpeg", "libtiff", "libwebp", "libxcrypt", "openjpeg", "tcl", "tk", "zlib"]
+packages = ["freetype", "geos", "lcms2", "libGL", "libGLU", "libimagequant", "libjpeg", "libtiff", "libwebp", "libxcrypt", "openjpeg", "tcl", "tk", "zlib"]
 
 [deployment]
 deploymentTarget = "autoscale"

app.py

@@ -5,7 +5,7 @@ from flask import Flask, render_template, request, jsonify, send_from_directory
 import json
 from werkzeug.utils import secure_filename
 from utils.image_processing import process_image
-from utils.geo_processing import convert_to_geojson
+from utils.geospatial import process_image_to_geojson
 
 # Configure logging
 logging.basicConfig(level=logging.DEBUG)
@@ -58,8 +58,8 @@ def upload_file():
             # Process the image
             processed_image_path = process_image(file_path, PROCESSED_FOLDER)
             
-            # Convert processed image to GeoJSON
-            geojson_data = convert_to_geojson(processed_image_path)
+            # Convert processed image to GeoJSON using improved processing
+            geojson_data = process_image_to_geojson(processed_image_path)
             
             # Save GeoJSON to file
             geojson_filename = f"{uuid.uuid4().hex}.geojson"

pyproject.toml

@@ -12,5 +12,6 @@ dependencies = [
     "opencv-python>=4.11.0.86",
     "pillow>=11.2.1",
     "psycopg2-binary>=2.9.10",
+    "shapely>=2.1.0",
     "werkzeug>=3.1.3",
 ]

utils/geospatial.py

@@ -0,0 +1,271 @@
+"""
+Geospatial utilities for image processing and GeoJSON generation.
+This module adapts techniques from the geoai library for better polygon generation
+with simplified dependencies.
+"""
+
+import os
+import logging
+import uuid
+import numpy as np
+import cv2
+from PIL import Image
+import json
+from shapely.geometry import Polygon, MultiPolygon, mapping
+from shapely import ops
+
+def extract_contours(image_path, min_area=50, epsilon_factor=0.002):
+    """
+    Extract contours from an image and convert them to polygons.
+    Uses OpenCV's contour detection with douglas-peucker simplification.
+    
+    Args:
+        image_path (str): Path to the processed image
+        min_area (int): Minimum contour area to keep
+        epsilon_factor (float): Simplification factor for douglas-peucker algorithm
+        
+    Returns:
+        list: List of polygon objects
+    """
+    try:
+        # Read the image
+        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+        if img is None:
+            # Try using PIL if OpenCV fails
+            pil_img = Image.open(image_path).convert('L')
+            img = np.array(pil_img)
+            
+        # Apply threshold if needed
+        _, thresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
+        
+        # Find contours
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        polygons = []
+        for contour in contours:
+            # Filter small contours
+            area = cv2.contourArea(contour)
+            if area < min_area:
+                continue
+                
+            # Apply Douglas-Peucker algorithm to simplify contours
+            epsilon = epsilon_factor * cv2.arcLength(contour, True)
+            approx = cv2.approxPolyDP(contour, epsilon, True)
+            
+            # Convert to polygon
+            if len(approx) >= 3:  # At least 3 points needed for a polygon
+                polygon_points = []
+                for point in approx:
+                    x, y = point[0]
+                    polygon_points.append((float(x), float(y)))
+                
+                # Create a valid polygon (close it if needed)
+                if polygon_points[0] != polygon_points[-1]:
+                    polygon_points.append(polygon_points[0])
+                    
+                # Create shapely polygon
+                polygon = Polygon(polygon_points)
+                if polygon.is_valid:
+                    polygons.append(polygon)
+        
+        return polygons
+        
+    except Exception as e:
+        logging.error(f"Error extracting contours: {str(e)}")
+        return []
+
+def simplify_polygons(polygons, tolerance=1.0):
+    """
+    Apply polygon simplification to reduce the number of vertices.
+    
+    Args:
+        polygons (list): List of shapely Polygon objects
+        tolerance (float): Simplification tolerance
+        
+    Returns:
+        list: List of simplified polygons
+    """
+    simplified = []
+    for polygon in polygons:
+        # Apply simplification
+        simp = polygon.simplify(tolerance, preserve_topology=True)
+        if simp.is_valid and not simp.is_empty:
+            simplified.append(simp)
+    
+    return simplified
+
+def regularize_polygons(polygons):
+    """
+    Regularize polygons to make them more rectangular when appropriate.
+    
+    Args:
+        polygons (list): List of shapely Polygon objects
+        
+    Returns:
+        list: List of regularized polygons
+    """
+    regularized = []
+    for polygon in polygons:
+        try:
+            # Check if the polygon is roughly rectangular using a simple heuristic
+            bounds = polygon.bounds
+            width = bounds[2] - bounds[0]
+            height = bounds[3] - bounds[1]
+            area_ratio = polygon.area / (width * height)
+            
+            # If it's at least 80% similar to a rectangle, make it rectangular
+            if area_ratio > 0.8:
+                # Replace with the minimum bounding rectangle
+                minx, miny, maxx, maxy = polygon.bounds
+                regularized.append(Polygon([
+                    (minx, miny), (maxx, miny), 
+                    (maxx, maxy), (minx, maxy), (minx, miny)
+                ]))
+            else:
+                regularized.append(polygon)
+        except Exception as e:
+            logging.warning(f"Error regularizing polygon: {str(e)}")
+            regularized.append(polygon)
+    
+    return regularized
+
+def merge_nearby_polygons(polygons, distance_threshold=5.0):
+    """
+    Merge polygons that are close to each other to reduce the polygon count.
+    
+    Args:
+        polygons (list): List of shapely Polygon objects
+        distance_threshold (float): Distance threshold for merging
+        
+    Returns:
+        list: List of merged polygons
+    """
+    if not polygons:
+        return []
+        
+    # Buffer polygons slightly to create overlaps for nearby polygons
+    buffered = [polygon.buffer(distance_threshold) for polygon in polygons]
+    
+    # Union all buffered polygons
+    union = ops.unary_union(buffered)
+    
+    # Convert the result to a list of polygons
+    if isinstance(union, Polygon):
+        return [union]
+    elif isinstance(union, MultiPolygon):
+        return list(union.geoms)
+    else:
+        return []
+
+def convert_to_geojson_with_transform(polygons, image_height, image_width, 
+                                    min_lat=None, min_lon=None, max_lat=None, max_lon=None):
+    """
+    Convert polygons to GeoJSON with proper geographic transformation.
+    
+    Args:
+        polygons (list): List of shapely Polygon objects
+        image_height (int): Height of the source image
+        image_width (int): Width of the source image
+        min_lat (float, optional): Minimum latitude for geographic bounds
+        min_lon (float, optional): Minimum longitude for geographic bounds
+        max_lat (float, optional): Maximum latitude for geographic bounds
+        max_lon (float, optional): Maximum longitude for geographic bounds
+        
+    Returns:
+        dict: GeoJSON object
+    """
+    # Set default geographic bounds if not provided
+    if None in (min_lon, min_lat, max_lon, max_lat):
+        # Default to somewhere neutral (center of Atlantic Ocean)
+        min_lon, min_lat = -30.0, 0.0
+        max_lon, max_lat = -20.0, 10.0
+    
+    # Create a GeoJSON feature collection
+    geojson = {
+        "type": "FeatureCollection",
+        "features": []
+    }
+    
+    # Function to transform pixel coordinates to geographic coordinates
+    def transform_point(x, y):
+        # Linear interpolation
+        lon = min_lon + (x / image_width) * (max_lon - min_lon)
+        # Invert y-axis for geographic coordinates
+        lat = max_lat - (y / image_height) * (max_lat - min_lat)
+        return lon, lat
+    
+    # Convert each polygon to a GeoJSON feature
+    for i, polygon in enumerate(polygons):
+        # Extract coordinates
+        coords = list(polygon.exterior.coords)
+        
+        # Transform coordinates to geographic space
+        geo_coords = [transform_point(x, y) for x, y in coords]
+        
+        # Create GeoJSON geometry
+        geometry = {
+            "type": "Polygon",
+            "coordinates": [geo_coords]
+        }
+        
+        # Create GeoJSON feature
+        feature = {
+            "type": "Feature",
+            "id": i + 1,
+            "properties": {
+                "name": f"Feature {i+1}"
+            },
+            "geometry": geometry
+        }
+        
+        geojson["features"].append(feature)
+    
+    return geojson
+
+def process_image_to_geojson(image_path):
+    """
+    Complete pipeline to convert an image to a simplified GeoJSON.
+    
+    Args:
+        image_path (str): Path to the processed image
+        
+    Returns:
+        dict: GeoJSON object
+    """
+    try:
+        # Open image to get dimensions
+        img = Image.open(image_path)
+        width, height = img.size
+        
+        # Extract contours from the image
+        polygons = extract_contours(image_path)
+        logging.info(f"Extracted {len(polygons)} initial polygons")
+        
+        if not polygons:
+            logging.warning("No polygons found in the image")
+            return {"type": "FeatureCollection", "features": []}
+        
+        # Simplify polygons to reduce vertex count
+        polygons = simplify_polygons(polygons, tolerance=2.0)
+        logging.info(f"After simplification: {len(polygons)} polygons")
+        
+        # Regularize appropriate polygons
+        polygons = regularize_polygons(polygons)
+        
+        # Merge nearby polygons to reduce count
+        polygons = merge_nearby_polygons(polygons)
+        logging.info(f"After merging: {len(polygons)} polygons")
+        
+        # Convert to GeoJSON with proper transformation
+        geojson = convert_to_geojson_with_transform(
+            polygons, height, width,
+            # Use generic bounds as we don't have real georeferencing
+            min_lat=40.0, min_lon=-75.0,
+            max_lat=42.0, max_lon=-73.0
+        )
+        
+        return geojson
+        
+    except Exception as e:
+        logging.error(f"Error in GeoJSON processing: {str(e)}")
+        return {"type": "FeatureCollection", "features": []}

uv.lock

@@ -412,6 +412,7 @@ dependencies = [
     { name = "opencv-python" },
     { name = "pillow" },
     { name = "psycopg2-binary" },
+    { name = "shapely" },
     { name = "werkzeug" },
 ]
 
@@ -425,9 +426,53 @@ requires-dist = [
     { name = "opencv-python", specifier = ">=4.11.0.86" },
     { name = "pillow", specifier = ">=11.2.1" },
     { name = "psycopg2-binary", specifier = ">=2.9.10" },
+    { name = "shapely", specifier = ">=2.1.0" },
     { name = "werkzeug", specifier = ">=3.1.3" },
 ]
 
+[[package]]
+name = "shapely"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/fe/3b0d2f828ffaceadcdcb51b75b9c62d98e62dd95ce575278de35f24a1c20/shapely-2.1.0.tar.gz", hash = "sha256:2cbe90e86fa8fc3ca8af6ffb00a77b246b918c7cf28677b7c21489b678f6b02e", size = 313617 }
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