Add Gaussian smoothing and field boundary overlay

Features:
- gaussian_sigma: Control smoothing strength (default 1.5, 0 = no smoothing)
- show_field_boundary: Toggle field boundary overlay (default true)
- Light dashed rectangle with corner markers
- Added scipy dependency for gaussian_filter

app.py

@@ -12,6 +12,7 @@ import base64
 import logging
 from datetime import datetime, timedelta
 from typing import Optional
+from scipy.ndimage import gaussian_filter
 
 import numpy as np
 import matplotlib
@@ -69,6 +70,8 @@ class HeatmapRequest(BaseModel):
     center_lon: float
     field_size_hectares: float = 10.0
     index_type: str = "NDVI"  # Any index from vegetation_indices.py
+    gaussian_sigma: float = 1.5  # Gaussian smoothing strength (0 = no smoothing)
+    show_field_boundary: bool = True  # Whether to show field boundary
 
 
 class HeatmapResponse(BaseModel):
@@ -145,8 +148,13 @@ def parse_timestamp(ts_str: str) -> datetime:
     return datetime.fromisoformat(ts_str)
 
 
-def generate_heatmap_image(data: np.ndarray, index_type: str) -> tuple:
-    """Generate a heatmap image from index data."""
+def generate_heatmap_image(
+    data: np.ndarray, 
+    index_type: str,
+    gaussian_sigma: float = 1.5,
+    show_field_boundary: bool = True
+) -> tuple:
+    """Generate a heatmap image from index data with optional smoothing and field boundary."""
     
     # Handle NaN values
     valid_mask = ~np.isnan(data)
@@ -161,6 +169,10 @@ def generate_heatmap_image(data: np.ndarray, index_type: str) -> tuple:
     data_normalized = np.clip((data - min_val) / (max_val - min_val + 1e-8), 0, 1)
     data_normalized = np.nan_to_num(data_normalized, nan=0.5)
     
+    # Apply Gaussian smoothing if sigma > 0
+    if gaussian_sigma > 0:
+        data_normalized = gaussian_filter(data_normalized, sigma=gaussian_sigma)
+    
     # Create figure
     fig, ax = plt.subplots(figsize=(8, 8), dpi=100)
     
@@ -172,6 +184,34 @@ def generate_heatmap_image(data: np.ndarray, index_type: str) -> tuple:
     
     im = ax.imshow(data_normalized, cmap=cmap, interpolation='bilinear')
     
+    # Draw field boundary if enabled
+    if show_field_boundary:
+        h, w = data_normalized.shape
+        # Draw a light rectangular boundary with some padding
+        padding = 0.05  # 5% padding from edges
+        rect = plt.Rectangle(
+            (w * padding, h * padding),
+            w * (1 - 2 * padding),
+            h * (1 - 2 * padding),
+            fill=False,
+            edgecolor='white',
+            linewidth=2,
+            linestyle='--',
+            alpha=0.6
+        )
+        ax.add_patch(rect)
+        
+        # Add corner markers
+        corner_size = min(h, w) * 0.05
+        corners = [
+            (w * padding, h * padding),  # Top-left
+            (w * (1 - padding), h * padding),  # Top-right
+            (w * padding, h * (1 - padding)),  # Bottom-left
+            (w * (1 - padding), h * (1 - padding))  # Bottom-right
+        ]
+        for cx, cy in corners:
+            ax.plot(cx, cy, 'o', color='white', markersize=6, alpha=0.7)
+    
     # Add colorbar
     cbar = plt.colorbar(im, ax=ax, shrink=0.8, pad=0.02)
     cbar.set_label(f'{index_type} Value', fontsize=10)
@@ -299,8 +339,13 @@ async def generate_heatmap(request: HeatmapRequest):
         
         logger.info(f"Calculated {request.index_type}: min={np.nanmin(index_data):.4f}, max={np.nanmax(index_data):.4f}")
         
-        # Generate heatmap
-        img_base64, min_val, max_val, mean_val = generate_heatmap_image(index_data, request.index_type)
+        # Generate heatmap with Gaussian smoothing and optional field boundary
+        img_base64, min_val, max_val, mean_val = generate_heatmap_image(
+            index_data, 
+            request.index_type,
+            gaussian_sigma=request.gaussian_sigma,
+            show_field_boundary=request.show_field_boundary
+        )
         
         return HeatmapResponse(
             success=True,
@@ -325,7 +370,9 @@ async def generate_heatmap_image_direct(
     center_lat: float,
     center_lon: float,
     field_size_hectares: float = 10.0,
-    index_type: str = "NDVI"
+    index_type: str = "NDVI",
+    gaussian_sigma: float = 1.5,
+    show_field_boundary: bool = True
 ):
     """Generate and return heatmap as PNG image directly."""
     
@@ -333,7 +380,9 @@ async def generate_heatmap_image_direct(
         center_lat=center_lat,
         center_lon=center_lon,
         field_size_hectares=field_size_hectares,
-        index_type=index_type
+        index_type=index_type,
+        gaussian_sigma=gaussian_sigma,
+        show_field_boundary=show_field_boundary
     )
     
     response = await generate_heatmap(request)

requirements.txt

@@ -2,6 +2,7 @@ fastapi==0.104.1
 uvicorn[standard]==0.24.0
 pydantic==2.5.2
 numpy>=1.24.0
+scipy>=1.10.0
 matplotlib>=3.7.0
 Pillow>=9.0.0
 sentinelhub>=3.9.0