import streamlit as st import torch import numpy as np from PIL import Image import rasterio from rasterio.windows import Window from tqdm.auto import tqdm import io import zipfile import os import albumentations as albu import segmentation_models_pytorch as smp from albumentations.pytorch.transforms import ToTensorV2 import geopandas as gpd from shapely.geometry import shape from shapely.ops import unary_union from rasterio.features import shapes DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") ENCODER = 'se_resnext50_32x4d' ENCODER_WEIGHTS = 'imagenet' # Load and prepare the 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(): _transform = [ albu.Resize(512, 512), albu.Lambda(image=preprocessing_fn), albu.Lambda(image=to_tensor, mask=to_tensor), ToTensorV2(), ] return albu.Compose(_transform) preprocess = get_preprocessing() @torch.no_grad() def process_and_predict(image, model): if isinstance(image, Image.Image): image = np.array(image) if image.ndim == 2: image = np.stack([image] * 3, axis=-1) elif image.shape[2] == 4: image = image[:, :, :3] preprocessed = preprocess(image=image)['image'] input_tensor = preprocessed.unsqueeze(0).to(DEVICE) mask = model(input_tensor) mask = torch.sigmoid(mask) mask = (mask > 0.6).float() mask_image = Image.fromarray((mask.squeeze().cpu().numpy() * 255).astype(np.uint8)) return mask_image def extract_tiles(map_file, model, tile_size=512, overlap=0, batch_size=4,threshold=0.6): tiles = [] with rasterio.open(map_file) as src: height = src.height width = src.width effective_tile_size = tile_size - overlap for y in tqdm(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.to(DEVICE)) 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() # Convert to hectares area_ha = area_m2 / 10000 return gdf, area_ha def main(): st.title("TIF File Processor") uploaded_file = st.file_uploader("Choose a TIF file", type="tif") if uploaded_file is not None: st.write("File uploaded successfully!") threshold= st.slider( 'Select a float value', min_value=0.1, max_value=0.9, value=0.5, step=0.05 ) overlap= st.slider( 'Select a float value', min_value=50, max_value=150, value=100, step=25 ) st.write('Selected threshold value:', threshold) st.write('Selected overlap value:', overlap) 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=512, overlap=overlap, batch_size=4,threshold=threshold) st.write("Processing complete!") output_path = "output_mask.shp" result_gdf, area_ha = create_vector_mask(tiles, output_path) st.write("Vector mask created successfully!") st.write(f"Total area occupied by polygons: {area_ha:.2f} hectares") # 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" ) # Clean up temporary files os.remove("temp.tif") for file in shp_files: os.remove(file) if __name__ == "__main__": main()