def downsample_gt_data(image, metadata, gt_polygons, normed_disp_field_maps,
downsampling_factor):
# First, correct the downsampling_factor so that:
# A downsampling_factor of 1 results in a final pixel_size equal to config.REFERENCE_PIXEL_SIZE
# A downsampling_factor of 2 results in a final pixel_size equal to 2 * config.REFERENCE_PIXEL_SIZE
corrected_downsampling_factor = downsampling_factor * config.REFERENCE_PIXEL_SIZE / metadata[
"pixelsize"]
scale = 1 / corrected_downsampling_factor
downsampled_image = skimage.transform.rescale(image,
scale,
order=3,
preserve_range=True,
multichannel=True)
downsampled_image = downsampled_image.astype(image.dtype)
downsampled_gt_polygons = polygon_utils.rescale_polygon(gt_polygons, scale)
downsampled_normed_disp_field_maps = np.empty(
(normed_disp_field_maps.shape[0],
round(normed_disp_field_maps.shape[1] /
corrected_downsampling_factor),
round(normed_disp_field_maps.shape[2] /
corrected_downsampling_factor),
normed_disp_field_maps.shape[3]))
for i in range(normed_disp_field_maps.shape[0]):
downsampled_normed_disp_field_maps[i] = skimage.transform.rescale(
normed_disp_field_maps[i],
scale,
order=3,
preserve_range=True,
multichannel=True)
return downsampled_image, downsampled_gt_polygons, downsampled_normed_disp_field_maps
def rescale_data(image, polygons, scale):
downsampled_image = skimage.transform.rescale(image,
scale,
order=3,
preserve_range=True,
multichannel=True,
anti_aliasing=True)
downsampled_image = downsampled_image.astype(image.dtype)
downsampled_polygons = polygon_utils.rescale_polygon(polygons, scale)
return downsampled_image, downsampled_polygons
def downsample_gt_data(image, gt_polygons, normed_disp_field_maps, downsampling_factor):
downsampled_image = skimage.transform.rescale(image, 1 / downsampling_factor, order=3, preserve_range=True, multichannel=True)
downsampled_image = downsampled_image.astype(image.dtype)
downsampled_gt_polygons = polygon_utils.rescale_polygon(gt_polygons, 1 / downsampling_factor)
downsampled_normed_disp_field_maps = np.empty((normed_disp_field_maps.shape[0],
round(normed_disp_field_maps.shape[1] / downsampling_factor),
round(normed_disp_field_maps.shape[2] / downsampling_factor),
normed_disp_field_maps.shape[3]))
for i in range(normed_disp_field_maps.shape[0]):
downsampled_normed_disp_field_maps[i] = skimage.transform.rescale(normed_disp_field_maps[i], 1 / downsampling_factor, order=3, preserve_range=True, multichannel=True)
return downsampled_image, downsampled_gt_polygons, downsampled_normed_disp_field_maps
def sample_patches(params):
raw_dirpath, tile_info, ds_fac, size, count, seed = params
im_filepath = read.get_image_filepath(raw_dirpath, tile_info["city"],
tile_info["number"])
im_size = image_utils.get_image_size(im_filepath)
polygon_list = read.load_polygons(raw_dirpath, read.POLYGON_DIRNAME,
tile_info["city"], tile_info["number"])
# Rescale data
corrected_factor = ds_fac * REFERENCE_PIXEL_SIZE / tile_info["pixelsize"]
scale_factor = 1 / corrected_factor
im_size = (int(np.round(im_size[0] * scale_factor)),
int(np.round(im_size[1] * scale_factor)))
ds_polygon_list = polygon_utils.rescale_polygon(polygon_list,
1 / corrected_factor)
bbox_list = image_utils.compute_patch_boundingboxes(im_size, size, size)
random.seed(seed)
random.shuffle(bbox_list)
# Sample <count> patches in tile, making sure there is at least a polygon inside
sampled_bbox_list = []
for bbox in bbox_list:
bbox_polygon_list = polygon_utils.filter_polygons_in_bounding_box(
ds_polygon_list, bbox)
if 1 <= len(bbox_polygon_list):
sampled_bbox_list.append(bbox)
if count <= len(sampled_bbox_list):
break
tile_info["bbox_list"] = sampled_bbox_list
tile_info["scale_factor"] = scale_factor
return tile_info
