training_data.py 文件源码

def tag_with_locations(test_images, predictions, tile_size, state_abbrev):
    """Combine image data with label data, so info can be rendered in a map and list UI.

    Add location data for convenience too.
    """
    combined_data = []
    for idx, img_loc_tuple in enumerate(test_images):
        raster_filename = img_loc_tuple[2]
        raster_dataset = gdal.Open(os.path.join(NAIP_DATA_DIR, raster_filename), gdal.GA_ReadOnly)
        raster_tile_x = img_loc_tuple[1][0]
        raster_tile_y = img_loc_tuple[1][1]
        ne_lat, ne_lon = pixel_to_lat_lon_web_mercator(raster_dataset, raster_tile_x +
                                                       tile_size, raster_tile_y)
        sw_lat, sw_lon = pixel_to_lat_lon_web_mercator(raster_dataset, raster_tile_x,
                                                       raster_tile_y + tile_size)
        certainty = predictions[idx][0]
        formatted_info = {'certainty': certainty, 'ne_lat': ne_lat, 'ne_lon': ne_lon,
                          'sw_lat': sw_lat, 'sw_lon': sw_lon, 'raster_tile_x': raster_tile_x,
                          'raster_tile_y': raster_tile_y, 'raster_filename': raster_filename,
                          'state_abbrev': state_abbrev, 'country_abbrev': 'USA'
                          }
        combined_data.append(formatted_info)
    return combined_data