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import os |
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import numpy as np |
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from src import app_logger, MODEL_FOLDER |
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from src.io.tms2geotiff import download_extent |
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from src.prediction_api.sam_onnx import SegmentAnythingONNX |
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from src.utilities.constants import ROOT, MODEL_ENCODER_NAME, ZOOM, SOURCE_TYPE, DEFAULT_TMS, MODEL_DECODER_NAME |
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from src.utilities.serialize import serialize |
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from src.utilities.type_hints import input_float_tuples |
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from src.utilities.utilities import get_system_info |
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def zip_arrays(arr1, arr2): |
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arr1_list = arr1.tolist() |
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arr2_list = arr2.tolist() |
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d = {} |
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for n1, n2 in enumerate(zip(arr1_list, arr2_list)): |
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app_logger.info(f"n1:{n1}, type {type(n1)}, n2:{n2}, type {type(n2)}.") |
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n1f = str(n1) |
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n2f = str(n2) |
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app_logger.info(f"n1:{n1}=>{n1f}, n2:{n2}=>{n2f}.") |
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d[n1f] = n2f |
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app_logger.info(f"zipped dict:{d}.") |
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return d |
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def samexporter_predict(bbox: input_float_tuples, prompt: list[dict], zoom: float = ZOOM) -> dict: |
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import tempfile |
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try: |
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os.environ['MPLCONFIGDIR'] = ROOT |
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get_system_info() |
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except Exception as e: |
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app_logger.error(f"Error while setting 'MPLCONFIGDIR':{e}.") |
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with tempfile.NamedTemporaryFile(prefix=f"{SOURCE_TYPE}_", suffix=".tif", dir=ROOT) as image_input_tmp: |
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for coord in bbox: |
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app_logger.info(f"bbox coord:{coord}, type:{type(coord)}.") |
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app_logger.info(f"start download_extent using bbox:{bbox}, type:{type(bbox)}, download image...") |
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pt0 = bbox[0] |
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pt1 = bbox[1] |
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img, matrix = download_extent(DEFAULT_TMS, pt0[0], pt0[1], pt1[0], pt1[1], zoom) |
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app_logger.info(f"img type {type(img)}, matrix type {type(matrix)}.") |
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np_img = np.array(img) |
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app_logger.info(f"np_img type {type(np_img)}.") |
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app_logger.info(f"np_img dtype {np_img.dtype}, shape {np_img.shape}.") |
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app_logger.info(f"geotiff created with size/shape {img.size} and transform matrix {str(matrix)}, start to initialize SamGeo instance:") |
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app_logger.info(f"use ENCODER model {MODEL_ENCODER_NAME} from {MODEL_FOLDER})...") |
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app_logger.info(f"use DECODER model {MODEL_DECODER_NAME} from {MODEL_FOLDER})...") |
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model = SegmentAnythingONNX( |
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encoder_model_path=MODEL_FOLDER / MODEL_ENCODER_NAME, |
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decoder_model_path=MODEL_FOLDER / MODEL_DECODER_NAME |
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) |
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app_logger.info(f"model instantiated, creating embedding...") |
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embedding = model.encode(np_img) |
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app_logger.info(f"embedding created, running predict_masks...") |
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prediction_masks = model.predict_masks(embedding, prompt) |
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app_logger.info(f"predict_masks terminated") |
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app_logger.info(f"prediction masks shape:{prediction_masks.shape}, {prediction_masks.dtype}.") |
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mask = np.zeros((prediction_masks.shape[2], prediction_masks.shape[3]), dtype=np.uint8) |
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for m in prediction_masks[0, :, :, :]: |
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mask[m > 0.0] = 255 |
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mask_unique_values, mask_unique_values_count = serialize(np.unique(mask, return_counts=True)) |
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app_logger.info(f"mask_unique_values:{mask_unique_values}.") |
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app_logger.info(f"mask_unique_values_count:{mask_unique_values_count}.") |
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output = { |
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"img_size": serialize(img.size), |
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"mask_unique_values_count": zip_arrays(mask_unique_values, mask_unique_values_count), |
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"masks_dtype": serialize(prediction_masks.dtype), |
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"masks_shape": serialize(prediction_masks.shape), |
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"matrix": serialize(matrix) |
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} |
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app_logger.info(f"output:{output}.") |
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return output |
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