import streamlit as st import geopandas as gpd import leafmap.foliumap as leafmap from PIL import Image import rasterio from rasterio.windows import Window from stqdm import stqdm import io import zipfile import os import albumentations as albu import segmentation_models_pytorch as smp from albumentations.pytorch.transforms import ToTensorV2 from shapely.geometry import shape from shapely.ops import unary_union from rasterio.features import shapes import torch import numpy as np DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") ENCODER = 'se_resnext50_32x4d' ENCODER_WEIGHTS = 'imagenet' #model @st.cache_resource def load_model(): model = torch.load('deeplabv3 v15.pth', map_location=DEVICE) model.eval().float() return model best_model = load_model() def to_tensor(x, **kwargs): return x.astype('float32') # Preprocessing preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS) def get_preprocessing(tile_size): _transform = [ albu.PadIfNeeded(min_height=tile_size, min_width=tile_size, always_apply=True), albu.Lambda(image=preprocessing_fn), albu.Lambda(image=to_tensor, mask=to_tensor), ToTensorV2(), ] return albu.Compose(_transform) def extract_tiles(map_file, model, tile_size=512, overlap=0, batch_size=4, threshold=0.6): preprocess = get_preprocessing(tile_size) tiles = [] with rasterio.open(map_file) as src: height = src.height width = src.width effective_tile_size = tile_size - overlap for y in stqdm(range(0, height, effective_tile_size)): for x in range(0, width, effective_tile_size): batch_images = [] batch_metas = [] for i in range(batch_size): curr_y = y + (i * effective_tile_size) if curr_y >= height: break window = Window(x, curr_y, tile_size, tile_size) out_image = src.read(window=window) if out_image.shape[0] == 1: out_image = np.repeat(out_image, 3, axis=0) elif out_image.shape[0] != 3: raise ValueError("The number of channels in the image is not supported") out_image = np.transpose(out_image, (1, 2, 0)) tile_image = Image.fromarray(out_image.astype(np.uint8)) out_meta = src.meta.copy() out_meta.update({ "driver": "GTiff", "height": tile_size, "width": tile_size, "transform": rasterio.windows.transform(window, src.transform) }) tile_image = np.array(tile_image) preprocessed_tile = preprocess(image=tile_image)['image'] batch_images.append(preprocessed_tile) batch_metas.append(out_meta) if not batch_images: break batch_tensor = torch.cat([img.unsqueeze(0).to(DEVICE) for img in batch_images], dim=0) with torch.no_grad(): batch_masks = model(batch_tensor) batch_masks = torch.sigmoid(batch_masks) batch_masks = (batch_masks > threshold).float() for j, mask_tensor in enumerate(batch_masks): mask_resized = torch.nn.functional.interpolate(mask_tensor.unsqueeze(0), size=(tile_size, tile_size), mode='bilinear', align_corners=False).squeeze(0) mask_array = mask_resized.squeeze().cpu().numpy() if mask_array.any() == 1: tiles.append([mask_array, batch_metas[j]]) return tiles def create_vector_mask(tiles, output_path): all_polygons = [] for mask_array, meta in tiles: # Ensure mask is binary mask_array = (mask_array > 0).astype(np.uint8) # Get shapes from the mask mask_shapes = list(shapes(mask_array, mask=mask_array, transform=meta['transform'])) # Convert shapes to Shapely polygons polygons = [shape(geom) for geom, value in mask_shapes if value == 1] all_polygons.extend(polygons) # Perform union of all polygons union_polygon = unary_union(all_polygons) # Create a GeoDataFrame gdf = gpd.GeoDataFrame({'geometry': [union_polygon]}, crs=meta['crs']) # Save to file gdf.to_file(output_path) # Calculate area in square meters area_m2 = gdf.to_crs(epsg=3857).area.sum() return gdf, area_m2 def display_map(shapefile_path, tif_path): st.title("Map with Shape and TIFF Overlay") # Load the shapefile mask = gpd.read_file(shapefile_path) # Check and reproject the mask to EPSG:3857 if needed if mask.crs is None or mask.crs.to_string() != 'EPSG:3857': mask = mask.to_crs('EPSG:3857') # Get the bounds of the shapefile to center the map bounds = mask.total_bounds # [minx, miny, maxx, maxy] center = [(bounds[1] + bounds[3]) / 2, (bounds[0] + bounds[2]) / 2] # Create a leafmap centered on the shapefile bounds m = leafmap.Map( center=[center[1], center[0]], # leafmap uses [latitude, longitude] zoom=10, crs='EPSG3857' ) # Add the mask layer to the map m.add_gdf(mask, layer_name="Shapefile Mask") # Add the TIFF image to the map as RGB m.add_raster(tif_path, layer_name="Satellite Image", rgb=True, opacity=0.9) # Display the map in Streamlit m.to_streamlit() def main(): st.title("PV Segmentor") uploaded_file = st.file_uploader("Choose a TIF file", type="tif") if uploaded_file is not None: st.write("File uploaded successfully!") resolution = st.radio( "Selext Processing resolution:", (512, 1024), index=0 ) overlap = st.slider( 'Select the value of overlap', min_value=50, max_value=150, value=100, step=25 ) threshold = st.slider( 'Select the value of the threshold', min_value=0.1, max_value=0.9, value=0.6, step=0.01 ) st.write('You selected:',resolution) st.write('Selected overlap value:', overlap) st.write('Selected threshold value:', threshold) if st.button("Process File"): st.write("Processing...") with open("temp.tif", "wb") as f: f.write(uploaded_file.getbuffer()) best_model.float() tiles = extract_tiles("temp.tif", best_model, tile_size=resolution, overlap=overlap, batch_size=4, threshold=threshold) st.write("Processing complete!") output_path = "output_mask.shp" result_gdf, area_m2 = create_vector_mask(tiles, output_path) st.write("Vector mask created successfully!") st.write(f"Total area occupied by PV panels: {area_m2:.4f} m^2") # Offer the shapefile for download shp_files = [f for f in os.listdir() if f.startswith("output_mask") and f.endswith((".shp", ".shx", ".dbf", ".prj"))] with io.BytesIO() as zip_buffer: with zipfile.ZipFile(zip_buffer, 'a', zipfile.ZIP_DEFLATED, False) as zip_file: for file in shp_files: zip_file.write(file) zip_buffer.seek(0) st.download_button( label="Download shapefile", data=zip_buffer, file_name="output_mask.zip", mime="application/zip" ) # Display the map with the predicted shapefile display_map("output_mask.shp", "temp.tif") # Clean up temporary files #os.remove("temp.tif") #for file in shp_files: # os.remove(file) if __name__ == "__main__": main()