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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import geopandas as gpd\n",
"import rioxarray as rxr\n",
"import xarray as xr\n",
"import numpy as np\n",
"import os\n",
"import torch\n",
"from transformers import SegformerForSemanticSegmentation\n",
"from lib.utils import compute_mask, compute_vndvi, compute_vdi"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# # Read raster data\n",
"# raster_path = \"data/spain_2022-07-29.tif\"\n",
"# raster = rxr.open_rasterio(raster_path)\n",
"\n",
"# # Crop raster with GeoJSON geometry, if available\n",
"# geom_path = raster_path.replace(\".tif\", \".geojson\")\n",
"# if os.path.exists(geom_path):\n",
"# geom = gpd.read_file(geom_path)\n",
"# raster = raster.rio.clip(geom.geometry)\n",
"# raster.rio.to_raster(raster_path.replace(\".tif\", \"_cropped.tif\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_model(hf_path='links-ads/gaia-growseg'):\n",
" # logger.info(f'Loading GAIA GRowSeg on {device}...')\n",
" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
" model = SegformerForSemanticSegmentation.from_pretrained(\n",
" hf_path,\n",
" num_labels=1,\n",
" num_channels=3,\n",
" id2label={1: 'vine'},\n",
" label2id={'vine': 1},\n",
" token=os.getenv('hf_read_access_token')\n",
" )\n",
" return model.to(device).eval()\n",
"\n",
"# Load GAIA GRowSeg model\n",
"model = load_model()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2025-03-20 12:39:09.921\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mlib.utils\u001b[0m:\u001b[36msliding_window_avg_pooling\u001b[0m:\u001b[36m308\u001b[0m - \u001b[1mExtracting patches idx...\u001b[0m\n",
"100%|█████████████████████████████████████████████| 67848/67848 [00:03<00:00, 20745.29it/s]\n",
"\u001b[32m2025-03-20 12:39:14.795\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mlib.utils\u001b[0m:\u001b[36msliding_window_avg_pooling\u001b[0m:\u001b[36m308\u001b[0m - \u001b[1mExtracting patches idx...\u001b[0m\n",
"100%|█████████████████████████████████████████████| 67848/67848 [00:03<00:00, 19329.36it/s]\n",
"\u001b[32m2025-03-20 12:39:56.011\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mlib.utils\u001b[0m:\u001b[36msliding_window_avg_pooling\u001b[0m:\u001b[36m308\u001b[0m - \u001b[1mExtracting patches idx...\u001b[0m\n",
"100%|██████████████████████████████████████████████| 64758/64758 [00:20<00:00, 3203.45it/s]\n"
]
}
],
"source": [
"raster_path = \"data/italy_2022-06-13_cropped.tif\"\n",
"patch_size = 512\n",
"stride = 256\n",
"scaling_factor = 1.0\n",
"dilate_rows = False\n",
"window_size = 360\n",
"granularity = int(window_size/8)\n",
"\n",
"# raster_path = \"data/spain_2022-07-29_cropped.tif\"\n",
"# patch_size = 512\n",
"# stride = 256\n",
"# scaling_factor = 1.0\n",
"# dilate_rows = False\n",
"# window_size = 400\n",
"# granularity = int(window_size/8)\n",
"\n",
"# raster_path = \"data/portugal_2023-08-01.tif\"\n",
"# patch_size = 512\n",
"# stride = 256\n",
"# scaling_factor = 1.25\n",
"# dilate_rows = False\n",
"# window_size = 80\n",
"# granularity = int(window_size/8)\n",
"\n",
"raster = rxr.open_rasterio(raster_path)\n",
"\n",
"# Compute mask\n",
"mask_path = raster_path.replace(\".tif\", \"_mask.tif\")\n",
"if not os.path.exists(mask_path):\n",
" mask = compute_mask(\n",
" raster.to_numpy(),\n",
" model,\n",
" patch_size=patch_size,\n",
" stride=stride,\n",
" scaling_factor=scaling_factor,\n",
" rotate=False,\n",
" batch_size=16,\n",
" ) # mask is a HxW uint8 array in with 0=background, 255=vine, 1=nodata\n",
"\n",
" # Convert mask from grayscale to RGBA, with red pixels for vine\n",
" alpha = ((mask != 1)*255).astype(np.uint8)\n",
" mask_colored = np.stack([mask, np.zeros_like(mask), np.zeros_like(mask), alpha], axis=0) # now, mask is a 4xHxW uint8 array in with 0=background, 255=vine\n",
"\n",
" # Georef mask like raster\n",
" mask_raster = xr.DataArray(\n",
" mask_colored,\n",
" dims=('band', 'y', 'x'),\n",
" coords={'x': raster.x, 'y': raster.y, 'band': raster.band}\n",
" )\n",
" mask_raster.rio.write_crs(raster.rio.crs, inplace=True) # Copy CRS\n",
" mask_raster.rio.write_transform(raster.rio.transform(), inplace=True) # Copy affine transform\n",
"\n",
" # Save mask\n",
" mask_raster.rio.to_raster(raster_path.replace(\".tif\", \"_mask.tif\"), compress='lzw')\n",
"else:\n",
" mask = rxr.open_rasterio(mask_path).sel(band=1).squeeze().to_numpy()\n",
"\n",
"# Compute vNDVI\n",
"vndvi_rows_path = raster_path.replace(\".tif\", \"_vndvi_rows.tif\")\n",
"vndvi_interrows_path = raster_path.replace(\".tif\", \"_vndvi_interrows.tif\")\n",
"if not os.path.exists(vndvi_rows_path) or not os.path.exists(vndvi_interrows_path):\n",
" vndvi_rows, vndvi_interrows = compute_vndvi(\n",
" raster.to_numpy(),\n",
" mask,\n",
" dilate_rows=dilate_rows,\n",
" window_size=window_size,\n",
" granularity=granularity,\n",
" ) # vNDVI is RGBA\n",
"\n",
" # Georef vNDVI like raster\n",
" vndvi_rows_raster = xr.DataArray(\n",
" vndvi_rows.transpose(2, 0, 1),\n",
" dims=('band', 'y', 'x'),\n",
" coords={'x': raster.x, 'y': raster.y, 'band': raster.band}\n",
" )\n",
" vndvi_rows_raster.rio.write_crs(raster.rio.crs, inplace=True)\n",
" vndvi_rows_raster.rio.write_transform(raster.rio.transform(), inplace=True)\n",
"\n",
" vndvi_interrows_raster = xr.DataArray(\n",
" vndvi_interrows.transpose(2, 0, 1),\n",
" dims=('band', 'y', 'x'),\n",
" coords={'x': raster.x, 'y': raster.y, 'band': raster.band}\n",
" )\n",
" vndvi_interrows_raster.rio.write_crs(raster.rio.crs, inplace=True)\n",
" vndvi_interrows_raster.rio.write_transform(raster.rio.transform(), inplace=True)\n",
"\n",
" # Save vNDVI\n",
" vndvi_rows_raster.rio.to_raster(raster_path.replace(\".tif\", \"_vndvi_rows.tif\"), compress='lzw')\n",
" vndvi_interrows_raster.rio.to_raster(raster_path.replace(\".tif\", \"_vndvi_interrows.tif\"), compress='lzw')\n",
"\n",
"# Compute VDI\n",
"vdi_path = raster_path.replace(\".tif\", \"_vdi.tif\")\n",
"if not os.path.exists(vdi_path):\n",
" vdi = compute_vdi(\n",
" raster.to_numpy(),\n",
" mask,\n",
" window_size=window_size,\n",
" granularity=granularity,\n",
" ) # VDI is RGBA\n",
"\n",
" # Georef VDI like raster\n",
" vdi_raster = xr.DataArray(\n",
" vdi.transpose(2, 0, 1),\n",
" dims=('band', 'y', 'x'),\n",
" coords={'x': raster.x, 'y': raster.y, 'band': raster.band}\n",
" )\n",
" vdi_raster.rio.write_crs(raster.rio.crs, inplace=True)\n",
" vdi_raster.rio.write_transform(raster.rio.transform(), inplace=True)\n",
"\n",
" # Save results\n",
" vdi_raster.rio.to_raster(raster_path.replace(\".tif\", \"_vdi.tif\"), compress='lzw')\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2025-03-20 12:40:30.816\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m76\u001b[0m - \u001b[1mReprojecting rasters to EPSG:4326 with NODATA value 0...\u001b[0m\n",
"\u001b[32m2025-03-20 12:40:52.371\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m84\u001b[0m - \u001b[1mCreating RGB raster overlay...\u001b[0m\n",
"\u001b[32m2025-03-20 12:40:52.373\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mcreate_image_overlay\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mCreating overlay: 'Orthoimage'...\u001b[0m\n",
"\u001b[32m2025-03-20 12:40:58.801\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m86\u001b[0m - \u001b[1mCreating mask overlay...\u001b[0m\n",
"\u001b[32m2025-03-20 12:40:58.806\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mcreate_image_overlay\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mCreating overlay: 'Mask'...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:05.006\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m88\u001b[0m - \u001b[1mCreating vNDVI rows overlay...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:05.008\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mcreate_image_overlay\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mCreating overlay: 'vNDVI Rows'...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:10.988\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mCreating vNDVI interrows overlay...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:10.990\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mcreate_image_overlay\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mCreating overlay: 'vNDVI Interrows'...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:16.558\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m92\u001b[0m - \u001b[1mCreating VDI overlay...\u001b[0m\n",
"\u001b[32m2025-03-20 12:41:16.560\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mcreate_image_overlay\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mCreating overlay: 'VDI'...\u001b[0m\n"
]
}
],
"source": [
"import folium\n",
"from loguru import logger\n",
"\n",
"def create_map(location=[41.9099533, 12.3711879], zoom_start=5, crs=3857, max_zoom=23):\n",
" \"\"\"Create a folium map with OpenStreetMap tiles and optional Esri.WorldImagery basemap.\"\"\"\n",
" if isinstance(crs, int):\n",
" crs = f\"EPSG{crs}\"\n",
" assert crs in [\"EPSG3857\"], f\"Only EPSG:3857 supported for now. Got {crs}.\"\n",
" \n",
" m = folium.Map(\n",
" location=location,\n",
" zoom_start=zoom_start,\n",
" crs=crs,\n",
" max_zoom=max_zoom,\n",
" tiles=\"OpenStreetMap\", # Esri.WorldImagery\n",
" attributionControl=False,\n",
" prefer_canvas=True,\n",
" )\n",
"\n",
" # Add Esri.WorldImagery as optional basemap (radio button)\n",
" folium.TileLayer(\n",
" tiles=\"Esri.WorldImagery\",\n",
" show=False,\n",
" overlay=False,\n",
" control=True,\n",
" ).add_to(m)\n",
"\n",
" return m\n",
"\n",
"def create_image_overlay(raster_path_or_array, name=\"Raster\", opacity=1.0, to_crs=4326, show=True):\n",
" \"\"\" Create a folium image overlay from a raster filepath or xarray.DataArray. \"\"\"\n",
" if isinstance(raster_path_or_array, str):\n",
" # Open the raster and its metadata\n",
" logger.info(f\"Opening raster: {raster_path_or_array!r}...\")\n",
" r = rxr.open_rasterio(raster_path_or_array)\n",
" else:\n",
" r = raster_path_or_array\n",
" nodata = r.rio.nodata or 0\n",
" if r.rio.crs.to_epsg() != to_crs:\n",
" logger.info(f\"Reprojecting raster to EPSG:{to_crs} with NODATA value {nodata}...\")\n",
" r = r.rio.reproject(to_crs, nodata=nodata) # nodata default: 255\n",
" r = r.transpose(\"y\", \"x\", \"band\")\n",
" bounds = r.rio.bounds() # (left, bottom, right, top)\n",
"\n",
" # Create a folium image overlay\n",
" logger.info(f\"Creating overlay: {name!r}...\")\n",
" overlay = folium.raster_layers.ImageOverlay(\n",
" image=r.to_numpy(),\n",
" name=name,\n",
" bounds=[[bounds[1], bounds[0]], [bounds[3], bounds[2]]], # format for folium: ((bottom,left),(top,right))\n",
" opacity=opacity,\n",
" interactive=True,\n",
" cross_origin=False,\n",
" zindex=1,\n",
" show=show,\n",
" )\n",
"\n",
" return overlay\n",
"\n",
"# Define paths\n",
"raster_path = \"data/portugal_2023-08-01.tif\"\n",
"mask_path = raster_path.replace('.tif', '_mask.tif')\n",
"vndvi_rows_path = raster_path.replace('.tif', '_vndvi_rows.tif')\n",
"vndvi_interrows_path = raster_path.replace('.tif', '_vndvi_interrows.tif')\n",
"vdi_path = raster_path.replace('.tif', '_vdi.tif')\n",
"\n",
"# Load rasters\n",
"raster = rxr.open_rasterio(raster_path)\n",
"mask_raster = rxr.open_rasterio(mask_path)\n",
"vndvi_rows_raster = rxr.open_rasterio(vndvi_rows_path)\n",
"vndvi_interrows_raster = rxr.open_rasterio(vndvi_interrows_path)\n",
"vdi_raster = rxr.open_rasterio(vdi_path)\n",
"\n",
"# Reproject all rasters to EPSG:4326\n",
"if raster.rio.crs.to_epsg() != 4326:\n",
" logger.info(f\"Reprojecting rasters to EPSG:4326 with NODATA value 0...\")\n",
" raster = raster.rio.reproject(\"EPSG:4326\", nodata=0) # nodata default: 255\n",
" mask_raster = mask_raster.rio.reproject(\"EPSG:4326\", nodata=0)\n",
" vndvi_rows_raster = vndvi_rows_raster.rio.reproject(\"EPSG:4326\", nodata=0)\n",
" vndvi_interrows_raster = vndvi_interrows_raster.rio.reproject(\"EPSG:4326\", nodata=0)\n",
" vdi_raster = vdi_raster.rio.reproject(\"EPSG:4326\", nodata=0)\n",
"\n",
"# Create overlays\n",
"logger.info(f'Creating RGB raster overlay...')\n",
"raster_overlay = create_image_overlay(raster, name=\"Orthoimage\", opacity=1.0, show=True)\n",
"logger.info(f'Creating mask overlay...')\n",
"mask_overlay = create_image_overlay(mask_raster, name=\"Mask\", opacity=1.0, show=False)\n",
"logger.info(f'Creating vNDVI rows overlay...')\n",
"vndvi_rows_overlay = create_image_overlay(vndvi_rows_raster, name=\"vNDVI Rows\", opacity=1.0, show=False)\n",
"logger.info(f'Creating vNDVI interrows overlay...')\n",
"vndvi_interrows_overlay = create_image_overlay(vndvi_interrows_raster, name=\"vNDVI Interrows\", opacity=1.0, show=False)\n",
"logger.info(f'Creating VDI overlay...')\n",
"vdi_overlay = create_image_overlay(vdi_raster, name=\"VDI\", opacity=1.0, show=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"m = create_map()\n",
"raster_overlay.add_to(m)\n",
"mask_overlay.add_to(m)\n",
"vndvi_rows_overlay.add_to(m)\n",
"vndvi_interrows_overlay.add_to(m)\n",
"vdi_overlay.add_to(m)\n",
"\n",
"# Add layer control\n",
"folium.LayerControl().add_to(m)\n",
"\n",
"# Fit map to bounds\n",
"m.fit_bounds(raster_overlay.get_bounds())\n",
"\n",
"# Save map\n",
"map_path = raster_path.replace('.tif', '.html')\n",
"m.save(map_path)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat_minor": 2
}
|