def test_image_with_gt_and_disp_polygons(runs_dirpath, image_name, ori_image, ori_metadata, ori_gt_polygons, ori_disp_polygons,
ori_disp_properties_list,
batch_size, ds_fac_list, run_name_list, model_disp_max_abs_value, thresholds,
test_output_dir, output_shapefiles=True):
ori_gt_polygons = polygon_utils.polygons_remove_holes(ori_gt_polygons) # TODO: Remove
ori_gt_polygons = polygon_utils.simplify_polygons(ori_gt_polygons, tolerance=1) # Remove redundant vertices
ori_disp_polygons = polygon_utils.polygons_remove_holes(ori_disp_polygons) # TODO: Remove
ori_disp_polygons = polygon_utils.simplify_polygons(ori_disp_polygons, tolerance=1) # Remove redundant vertices
output_name = image_name
test(runs_dirpath, ori_image, ori_metadata, ori_gt_polygons, ori_disp_polygons, batch_size, ds_fac_list, run_name_list,
model_disp_max_abs_value, thresholds, test_output_dir, output_name, output_shapefiles=output_shapefiles,
properties_list=ori_disp_properties_list)
def test_detect_new_buildings(runs_dirpath, image_name, ori_image, ori_metadata, ori_gt_polygons, batch_size, ds_fac_list,
run_name_list, model_disp_max_abs_value, polygonization_params, thresholds,
test_output_dir, output_shapefiles=True):
ori_gt_polygons = polygon_utils.polygons_remove_holes(ori_gt_polygons) # TODO: Remove
ori_gt_polygons = polygon_utils.simplify_polygons(ori_gt_polygons, tolerance=1) # Remove redundant vertices
ori_disp_polygons = [] # Don't input any polygons
output_name = image_name
seg_image_plot_filename_format = "{}.segmentation.png"
ious_filename_format = "{}.iou.npy"
new_polygons_filename_format = "{}.new_polygons.npy"
aligned_new_polygons_filename_format = "{}.aligned_new_polygons.npy"
polygons_image_plot_filename_format = "{}.polygons.png"
shapefile_filename_format = "{}.{}_polygons.shp"
accuracies_filename_format = "{}.accuracy.npy"
# --- Get the segmentation output --- #
seg_ds_fac_list = ds_fac_list[-1:]
seg_run_name_list = run_name_list[-1:]
print("# --- Run the model --- #")
_, segmentation_image = multires_pipeline.multires_inference(runs_dirpath, ori_image, ori_metadata,
ori_disp_polygons,
model_disp_max_abs_value,
batch_size, seg_ds_fac_list,
seg_run_name_list)
# segmentation_image = np.zeros((ori_image.shape[0], ori_image.shape[1], 4))
print("# --- Save segmentation_output --- #")
plot_segmentation_image_filename = seg_image_plot_filename_format.format(output_name)
plot_segmentation_image_filepath = os.path.join(test_output_dir, plot_segmentation_image_filename)
visualization.save_plot_segmentation_image(plot_segmentation_image_filepath, segmentation_image)
seg_image = segmentation_image[:, :, 1:] # Remove background channel
# --- Measure IoUs --- #
print("# --- Measure accuracies --- #")
print(seg_image.min())
print(seg_image.max())
iou_thresholds = np.arange(0, 1.01, 0.01)
ious_filename = ious_filename_format.format(output_name)
ious_filepath = os.path.join(test_output_dir, ious_filename)
ious = measure_ious(ori_gt_polygons, seg_image, iou_thresholds, ious_filepath)
print("IoUs:")
print(ious)
# --- Polygonize segmentation --- #
print("# --- Polygonize segmentation --- #")
# TODO: remove:
# seg_image_filepath = "test/bradbury_buildings.1_double.only_seg/SanFrancisco_01.disp_00.segmentation.png"
# seg_image = image_utils.load_image(seg_image_filepath)
# seg_image = seg_image / 255
fill_threshold = polygonization_params["fill_threshold"]
outline_threshold = polygonization_params["outline_threshold"]
selem_width = polygonization_params["selem_width"]
iterations = polygonization_params["iterations"]
def load_gt_polygons(image_filepath):
gt_polygons = geo_utils.get_polygons_from_osm(image_filepath,
tag="building")
if len(gt_polygons):
gt_polygons = polygon_utils.polygons_remove_holes(
gt_polygons) # TODO: Remove
# Remove redundant vertices
gt_polygons = polygon_utils.simplify_polygons(gt_polygons, tolerance=1)
return gt_polygons
return None
def test_image_with_gt_polygons_and_disp_maps(runs_dirpath,
image_name,
ori_image,
ori_metadata,
ori_gt_polygons,
ori_normed_disp_field_maps,
disp_max_abs_value,
batch_size,
ds_fac_list,
run_name_list,
model_disp_max_abs_value,
thresholds,
test_output_dir,
output_shapefiles=True):
output_name_format = "{}.disp_{:02d}"
ori_gt_polygons = polygon_utils.polygons_remove_holes(
ori_gt_polygons) # TODO: Remove
# Remove redundant vertices
ori_gt_polygons = polygon_utils.simplify_polygons(ori_gt_polygons,
tolerance=1)
disp_polygons_list = generate_disp_data(ori_normed_disp_field_maps,
ori_gt_polygons,
disp_max_abs_value)
for i in range(len(disp_polygons_list)):
print("# --- Testing with disp {:02d} --- #".format(i))
disp_polygons = disp_polygons_list[i]
output_name = output_name_format.format(image_name, i)
test(runs_dirpath,
ori_image,
ori_metadata,
ori_gt_polygons,
disp_polygons,
batch_size,
ds_fac_list,
run_name_list,
model_disp_max_abs_value,
thresholds,
test_output_dir,
output_name,
output_shapefiles=output_shapefiles)
def process_image(dataset_raw_dirpath, image_info, patch_stride, patch_res, downsampling_factors, disp_max_abs_value,
include_polygons,
downsampling_factor_writers):
"""
Writes to all the writers (one for each resolution) all sample patches extracted from the image_info.
:param raw_dirpath:
:param image_info:
:param patch_stride:
:param patch_res:
:param downsampling_factors:
:param disp_max_abs_value:
:param include_polygons:
:param downsampling_factor_writers:
:return:
"""
ori_image, ori_metadata, ori_gt_polygons = read.load_gt_data(dataset_raw_dirpath, image_info["city"],
image_info["number"])
if ori_gt_polygons is None:
return False
ori_gt_polygons = polygon_utils.polygons_remove_holes(ori_gt_polygons) # TODO: Remove
# Remove redundant vertices
ori_gt_polygons = polygon_utils.simplify_polygons(ori_gt_polygons, tolerance=1)
# visualization.init_figures(["gt_data"], figsize=(60, 40))
# visualization.plot_example_polygons("gt_data", ori_image, ori_gt_polygons)
# Create displacement maps
ori_normed_disp_field_maps = math_utils.create_displacement_field_maps(ori_image.shape[:2], config.DISP_MAP_COUNT,
config.DISP_MODES,
config.DISP_GAUSS_MU_RANGE,
config.DISP_GAUSS_SIG_SCALING) # TODO: uncomment
# ori_normed_disp_field_maps = np.zeros((config.DISP_MAP_COUNT, ori_image.shape[0], ori_image.shape[1], 2)) # TODO: remove
# # TODO: remove
# np.random.seed(seed=0)
# colors = np.random.randint(0, 255, size=(len(downsampling_factors), 3), dtype=np.uint8)
for index, downsampling_factor in enumerate(downsampling_factors):
print("downsampling_factor: {}".format(downsampling_factor))
# Downsample ground-truth
image, gt_polygons, normed_disp_field_maps = downsample_gt_data(ori_image, ori_metadata, ori_gt_polygons,
ori_normed_disp_field_maps, downsampling_factor)
spatial_shape = image.shape[:2]
# Random color
# image = np.tile(colors[index], reps=[image.shape[0], image.shape[1], 1]) # TODO: remove
# Draw gt polygon map
gt_polygon_map = polygon_utils.draw_polygon_map(gt_polygons, spatial_shape, fill=True, edges=True,
vertices=True)
# Generate final displacement
disp_polygons_list, disp_polygon_maps = generate_disp_data(normed_disp_field_maps, gt_polygons,
disp_max_abs_value, spatial_shape)
# Compress data
gt_polygons = [polygon.astype(np.float16) for polygon in gt_polygons]
disp_polygons_list = [[polygon.astype(np.float16) for polygon in polygons] for polygons in disp_polygons_list]
disp_field_maps = normed_disp_field_maps * 32767 # int16 max value = 32767
disp_field_maps = np.round(disp_field_maps)
disp_field_maps = disp_field_maps.astype(np.int16)
# Cut sample into patches
if include_polygons:
patches = process_sample_into_patches(patch_stride, patch_res, image, gt_polygon_map,
disp_field_maps, disp_polygon_maps,
gt_polygons, disp_polygons_list)
else:
patches = process_sample_into_patches(patch_stride, patch_res, image, gt_polygon_map,
disp_field_maps, disp_polygon_maps)
for patch in patches:
save_patch_to_tfrecord(patch, downsampling_factor_writers[downsampling_factor])
return True
def process_image(reader, image_id, downsampling_factors, disp_field_maps_patch_creator, disp_max_abs_value,
include_polygons,
downsampling_factor_writers):
"""
Writes to all the writers (one for each resolution) all sample patches extracted from the image_info.
:param reader:
:param image_id:
:param downsampling_factors:
:param disp_field_maps_patch_creator:
:param disp_max_abs_value:
:param include_polygons:
:param downsampling_factor_writers:
:return:
"""
ori_image, ori_metadata, ori_gt_polygons = reader.load_gt_data(image_id)
ori_gt_polygons = polygon_utils.polygons_remove_holes(ori_gt_polygons) # TODO: Remove
# Remove redundant vertices
ori_gt_polygons = polygon_utils.simplify_polygons(ori_gt_polygons, tolerance=1)
# visualization.init_figures(["gt_data"], figsize=(60, 40))
# visualization.plot_example_polygons("gt_data", ori_image, ori_gt_polygons)
# Get displacement maps
ori_normed_disp_field_maps = disp_field_maps_patch_creator.get_patch()
# ori_normed_disp_field_maps = np.zeros((config.DISP_MAP_COUNT, ori_image.shape[0], ori_image.shape[1], 2)) # TODO: remove
# # TODO: remove
# np.random.seed(seed=0)
# colors = np.random.randint(0, 255, size=(len(downsampling_factors), 3), dtype=np.uint8)
for index, downsampling_factor in enumerate(downsampling_factors):
# print("downsampling_factor: {}".format(downsampling_factor))
# Downsample ground-truth
image, gt_polygons, normed_disp_field_maps = downsample_gt_data(ori_image, ori_metadata, ori_gt_polygons,
ori_normed_disp_field_maps, downsampling_factor)
spatial_shape = image.shape[:2]
# Random color
# image = np.tile(colors[index], reps=[image.shape[0], image.shape[1], 1]) # TODO: remove
# Draw gt polygon map
gt_polygon_map = polygon_utils.draw_polygon_map(gt_polygons, spatial_shape, fill=True, edges=True,
vertices=True)
# Generate final displacement
disp_polygons_list, disp_polygon_maps = generate_disp_data(normed_disp_field_maps, gt_polygons,
disp_max_abs_value, spatial_shape)
if gt_polygons[0][0][0] == np.nan or gt_polygons[0][0][1] == np.nan:
print(gt_polygons[0][0])
if disp_polygons_list[0][0][0][0] == np.nan or disp_polygons_list[0][0][0][1] == np.nan:
print("disp_polygons_list:")
print(disp_polygons_list[0][0])
# Compress data
gt_polygons = [polygon.astype(np.float16) for polygon in gt_polygons]
disp_polygons_list = [[polygon.astype(np.float16) for polygon in polygons] for polygons in disp_polygons_list]
disp_field_maps = normed_disp_field_maps * 32767 # int16 max value = 32767
disp_field_maps = np.round(disp_field_maps)
disp_field_maps = disp_field_maps.astype(np.int16)
if include_polygons:
gt_polygons, \
disp_polygons_array = polygon_utils.prepare_polygons_for_tfrecord(gt_polygons, disp_polygons_list)
else:
gt_polygons = disp_polygons_array = None
assert image.shape[0] == image.shape[1], "image should be square otherwise tile_res cannot be defined"
tile_res = image.shape[0]
disp_map_count = disp_polygon_maps.shape[0]
patch = {
"tile_res": tile_res,
"disp_map_count": disp_map_count,
"image": image,
"gt_polygons": gt_polygons,
"disp_polygons": disp_polygons_array,
"gt_polygon_map": gt_polygon_map,
"disp_field_maps": disp_field_maps,
"disp_polygon_maps": disp_polygon_maps,
}
save_patch_to_tfrecord(patch, downsampling_factor_writers[downsampling_factor])
return True