# Press the green button in the gutter to run the script. import json from pathlib import Path from typing import List import numpy as np import rasterio from PIL import Image from src import app_logger, MODEL_FOLDER from src.io.tiles_to_tiff import convert from src.io.tms2geotiff import save_geotiff_gdal from src.prediction_api.sam_onnx import SegmentAnythingONNX from src.utilities.constants import MODEL_ENCODER_NAME, ZOOM, MODEL_DECODER_NAME, ROOT from src.utilities.serialize import serialize models_dict = {"fastsam": {"instance": None}} def zip_arrays(arr1, arr2): try: arr1_list = arr1.tolist() arr2_list = arr2.tolist() # return {serialize(k): serialize(v) for k, v in zip(arr1_list, arr2_list)} d = {} for n1, n2 in zip(arr1_list, arr2_list): app_logger.info(f"n1:{n1}, type {type(n1)}, n2:{n2}, type {type(n2)}.") n1f = str(n1) n2f = str(n2) app_logger.info(f"n1:{n1}=>{n1f}, n2:{n2}=>{n2f}.") d[n1f] = n2f app_logger.info(f"zipped dict:{d}.") return d except Exception as e_zip_arrays: app_logger.info(f"exception zip_arrays:{e_zip_arrays}.") return {} def load_affine_transformation_from_matrix(matrix_source_coeffs: List): from affine import Affine if len(matrix_source_coeffs) != 6: raise ValueError(f"Expected 6 coefficients, found {len(matrix_source_coeffs)}; argument type: {type(matrix_source_coeffs)}.") try: a, d, b, e, c, f = (float(x) for x in matrix_source_coeffs) center = tuple.__new__(Affine, [a, b, c, d, e, f, 0.0, 0.0, 1.0]) return center * Affine.translation(-0.5, -0.5) except Exception as e: app_logger.error(f"exception:{e}, check https://github.com/rasterio/affine project for updates") def samexporter_predict(bbox, prompt: list[dict], zoom: float = ZOOM, model_name: str = "fastsam") -> dict: try: from rasterio.features import shapes from geopandas import GeoDataFrame if models_dict[model_name]["instance"] is None: app_logger.info(f"missing instance model {model_name}, instantiating it now") model_instance = SegmentAnythingONNX( encoder_model_path=MODEL_FOLDER / MODEL_ENCODER_NAME, decoder_model_path=MODEL_FOLDER / MODEL_DECODER_NAME ) models_dict[model_name]["instance"] = model_instance app_logger.info(f"using a {model_name} instance model...") models_instance = models_dict[model_name]["instance"] img, matrix = convert( bounding_box=bbox, zoom=int(zoom) ) pt0, pt1 = bbox rio_output = f"/tmp/downloaded_rio_{pt0[0]}_{pt0[1]}_{pt1[0]}_{pt1[1]}.tif" save_geotiff_gdal(img, rio_output, matrix) app_logger.info(f"saved downloaded geotiff image to {rio_output}...") np_img = np.array(img) app_logger.info(f"## img type {type(np_img)}, prompt:{prompt}.") app_logger.info(f"onnxruntime input shape/size (shape if PIL) {np_img.size}," f"start to initialize SamGeo instance:") try: app_logger.info(f"onnxruntime input shape (NUMPY) {np_img.shape}.") except Exception as e_shape: app_logger.error(f"e_shape:{e_shape}.") app_logger.info(f"use {model_name} model, ENCODER model {MODEL_ENCODER_NAME} and" f" {MODEL_DECODER_NAME} from {MODEL_FOLDER}): model instantiated, creating embedding...") embedding = models_instance.encode(np_img) app_logger.info(f"embedding created, running predict_masks...") prediction_masks = models_instance.predict_masks(embedding, prompt) app_logger.info(f"predict_masks terminated...") app_logger.info(f"predict_masks terminated, prediction masks shape:{prediction_masks.shape}, {prediction_masks.dtype}.") pt0, pt1 = bbox prediction_masks_output = f"/tmp/prediction_masks_{pt0[0]}_{pt0[1]}_{pt1[0]}_{pt1[1]}.npy" np.save( prediction_masks_output, prediction_masks, allow_pickle=True, fix_imports=True ) app_logger.info(f"saved prediction_masks:{prediction_masks_output}.") # mask = np.zeros((prediction_masks.shape[2], prediction_masks.shape[3]), dtype=np.uint8) # app_logger.info(f"output mask shape:{mask.shape}, {mask.dtype}.") # ## todo: convert to geojson directly within the loop to avoid merging two objects # for n, m in enumerate(prediction_masks[0, :, :, :]): # app_logger.info(f"## {n} mask => m shape:{mask.shape}, {mask.dtype}.") # mask[m > 0.0] = 255 prediction_masks0 = prediction_masks[0] app_logger.info(f"prediction_masks0 shape:{prediction_masks0.shape}.") try: pmf = np.sum(prediction_masks0, axis=0).astype(np.uint8) except Exception as e_sum_pmf: app_logger.error(f"e_sum_pmf:{e_sum_pmf}.") pmf = prediction_masks0[0] app_logger.info(f"creating pil image from prediction mask with shape {pmf.shape}.") pil_pmf = Image.fromarray(pmf) pil_pmf_output = f"/tmp/pil_pmf_{pmf.shape[0]}_{pmf.shape[1]}.png" pil_pmf.save(pil_pmf_output) app_logger.info(f"saved pil_pmf:{pil_pmf_output}.") mask = np.zeros(pmf.shape, dtype=np.uint8) mask[pmf > 0] = 255 # cv2.imwrite(f"/tmp/cv2_mask_predicted_{mask.shape[0]}_{mask.shape[1]}_{mask.shape[2]}.png", mask) pil_mask = Image.fromarray(mask) pil_mask_predicted_output = f"/tmp/pil_mask_predicted_{mask.shape[0]}_{mask.shape[1]}.png" pil_mask.save(pil_mask_predicted_output) app_logger.info(f"saved pil_mask_predicted:{pil_mask_predicted_output}.") mask_unique_values, mask_unique_values_count = serialize(np.unique(mask, return_counts=True)) app_logger.info(f"mask_unique_values:{mask_unique_values}.") app_logger.info(f"mask_unique_values_count:{mask_unique_values_count}.") app_logger.info(f"read geotiff:{rio_output}: create shapes_generator...") # app_logger.info(f"image/geojson transform:{transform}: create shapes_generator...") with rasterio.open(rio_output, "r", driver="GTiff") as rio_src: band = rio_src.read() try: transform = load_affine_transformation_from_matrix(matrix) app_logger.info(f"geotiff band:{band.shape}, type: {type(band)}, dtype: {band.dtype}.") app_logger.info(f"geotiff band:{mask.shape}.") app_logger.info(f"transform from matrix:{transform}.") app_logger.info(f"rio_src crs:{rio_src.crs}.") app_logger.info(f"rio_src transform:{rio_src.transform}.") except Exception as e_shape_band: app_logger.error(f"e_shape_band:{e_shape_band}.") raise e_shape_band # mask_band = band != 0 shapes_generator = ({ 'properties': {'raster_val': v}, 'geometry': s} for i, (s, v) # in enumerate(shapes(mask, mask=(band != 0), transform=rio_src.transform)) # use mask=None to avoid using source in enumerate(shapes(mask, mask=None, transform=rio_src.transform)) ) app_logger.info(f"created shapes_generator.") shapes_list = list(shapes_generator) app_logger.info(f"created {len(shapes_list)} polygons.") gpd_polygonized_raster = GeoDataFrame.from_features(shapes_list, crs="EPSG:3857") app_logger.info(f"created a GeoDataFrame...") geojson = gpd_polygonized_raster.to_json(to_wgs84=True) app_logger.info(f"created geojson...") output_geojson = str(Path(ROOT) / "geojson_output.json") with open(output_geojson, "w") as jj_out: app_logger.info(f"writing geojson file to {output_geojson}.") json.dump(json.loads(geojson), jj_out) app_logger.info(f"geojson file written to {output_geojson}.") return { "geojson": geojson, "n_shapes_geojson": len(shapes_list), "n_predictions": len(prediction_masks), # "n_pixels_predictions": zip_arrays(mask_unique_values, mask_unique_values_count), } except ImportError as e: app_logger.error(f"Error trying import module:{e}.")