def output_preprocessed_image_patch_to_output_directory(
full_image_path, output_directory_path, first_centermost_crop_size,
downscale_to_size, second_centermost_crop_size):
input_image = Image.open(full_image_path)
first_centermost_crop_image = image_preprocessing_utils.crop_to_the_centermost(
input_image, first_centermost_crop_size)
scaled_image = image_preprocessing_utils.scale_image(
first_centermost_crop_image, downscale_to_size)
second_centermost_crop_image = image_preprocessing_utils.crop_to_the_centermost(
scaled_image, second_centermost_crop_size)
is_mostly_white_pixels = is_image_mostly_white_pixels(
0.95, second_centermost_crop_image)
case_id = full_image_path.split('/')[-2]
image_patch_id = full_image_path.split('/')[-1][:-4]
output_case_subfolder = join(output_directory_path, case_id)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_case_subfolder)
if is_mostly_white_pixels:
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_case_subfolder + "/white_background_image_patches")
output_image_name = join(
output_case_subfolder + "/white_background_image_patches/",
image_patch_id + '.jpg')
second_centermost_crop_image.save(output_image_name)
else:
output_image_name = join(output_case_subfolder,
image_patch_id + '.jpg')
second_centermost_crop_image.save(output_image_name)
def output_preprocessed_image_patch_to_output_directory(
full_image_path, output_directory_path, downscale_to_size):
input_image = Image.open(full_image_path).convert('LA')
scaled_image = image_preprocessing_utils.scale_image(
input_image, downscale_to_size)
case_id = full_image_path.split('/')[-2]
image_patch_id = full_image_path.split('/')[-1][:-4]
output_case_subfolder = join(output_directory_path, case_id)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_case_subfolder)
output_image_name = join(output_case_subfolder, image_patch_id + '.png')
scaled_image.save(output_image_name)
def create_saliency_prediction_overview_visualization_for_case(
output_folder_path_for_saliancy_prediction_visualization_directory,
svs_image, case_ID, image_patch_metadata_objects_corresponding_to_CID):
output_path = output_folder_path_for_saliancy_prediction_visualization_directory + '/' + case_ID + '/'
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_path)
thumbnail = create_jpeg_thumbnail_of_wsi(image_paths_of_sample[0])
thumbnail = thumbnail.convert("RGB")
thumbnail_path = output_path + case_ID + "_original.jpeg"
thumbnail.save(thumbnail_path, 'JPEG')
thumbnail = thumbnail.convert("RGBA")
thumbnail_with_predictions_above_threshold = draw_saliency_prediction_annotation_boxes_onto_thumbnail(
svs_image,
thumbnail,
image_patch_metadata_objects_corresponding_to_CID,
accuracy_percentage_threshold,
draw_border=False)
thumbnail_with_predictions_above_threshold_path = output_path + case_ID + "_multiple_annotations.jpeg"
thumbnail_with_predictions_above_threshold.save(
thumbnail_with_predictions_above_threshold_path, 'JPEG')
thumbnail = thumbnail.convert("RGBA")
image_patch_metadata_object_with_highest_saliency = image_patch_metadata_object_utils.get_image_patch_metadata_object_with_the_highest_saliency(
image_patch_metadata_objects_corresponding_to_CID)
thumbnail_with_single_predication = draw_saliency_prediction_annotation_boxes_onto_thumbnail(
svs_image,
thumbnail, [image_patch_metadata_object_with_highest_saliency],
accuracy_percentage_threshold,
draw_border=True)
thumbnail_with_single_predication_path = output_path + case_ID + "_single_annotations.jpeg"
thumbnail_with_single_predication.save(
thumbnail_with_single_predication_path, 'JPEG')
merged_thumbnail_image = image_utils.merge_images_horizontally([
thumbnail_path, thumbnail_with_predictions_above_threshold_path,
thumbnail_with_single_predication_path
])
merged_thumbnail_image_path = output_path + case_ID + "_all_thumbnail_images.jpeg"
merged_thumbnail_image.save(merged_thumbnail_image_path, 'JPEG')
def create_most_salient_image_patch_high_res_visualization(
output_folder_path_for_most_salient_image_patch_visualization_directory,
svs_image_path, image_patch_metadata_objects_corresponding_to_CID):
output_path = output_folder_path_for_most_salient_image_patch_visualization_directory
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_path)
image_patch_metadata_object_with_highest_saliency = image_patch_metadata_object_utils.get_image_patch_metadata_object_with_the_highest_saliency(
image_patch_metadata_objects_corresponding_to_CID)
svs_image_patch_extractor.extract_image_patch_jpeg(
svs_image_path,
output_path,
from_resolution_level=image_patch_metadata_object_with_highest_saliency
.resolution_level,
to_resolution_level=enums.ResolutionLevel.LEVEL_2,
patch_area_x=image_patch_metadata_object_with_highest_saliency.
x_coordinate,
patch_area_y=image_patch_metadata_object_with_highest_saliency.
y_coordinate,
patch_area_width=image_patch_metadata_object_with_highest_saliency.
width,
patch_area_height=image_patch_metadata_object_with_highest_saliency.
height)
def annotate_the_region_on_image_patch_with_the_highest_nuclei_count(
highest_saliency_image_patch_metadata_object,
high_nuclei_count_metadata_object, svs_loader, output_path):
case_ID = highest_saliency_image_patch_metadata_object.case_id
output_path = output_path + '/' + case_ID + '/'
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_path)
image_patch_with_highest_saliency = Image.open(
highest_saliency_image_patch_metadata_object.image_patch_path)
image_patch_with_highest_saliency = image_patch_with_highest_saliency.convert(
"RGBA")
svs_path = svs_loader.get_0_indexed_svs_path_for_CID(case_ID)
svs_image = svs_utils.get_svs_image_of_wsi_from_path(svs_path)
scaled_high_nuclei_count_metadata_image_patch_data = svs_utils.scale_image_patch_metadata_object_to_new_resolution_level(
high_nuclei_count_metadata_object,
highest_saliency_image_patch_metadata_object.resolution_level,
svs_image)
scaled_high_nuclei_count_metadata_image_patch_data.x_coordinate = abs(
int(scaled_high_nuclei_count_metadata_image_patch_data.x_coordinate) -
int(highest_saliency_image_patch_metadata_object.x_coordinate))
scaled_high_nuclei_count_metadata_image_patch_data.y_coordinate = abs(
int(scaled_high_nuclei_count_metadata_image_patch_data.y_coordinate) -
int(highest_saliency_image_patch_metadata_object.y_coordinate))
image_patch_with_annotation_box_of_highest_nuclei_count = image_utils.draw_annotation_box_onto_image(
image_patch_with_highest_saliency,
scaled_high_nuclei_count_metadata_image_patch_data,
draw_border=True)
thumbnail_with_single_predication_path = output_path + case_ID + "_highest_nuclei_count_annotation.jpeg"
image_patch_with_annotation_box_of_highest_nuclei_count.save(
thumbnail_with_single_predication_path, 'JPEG')
args = parser.parse_args()
input_folder_path = args.input_folder_path
input_folder_path_for_high_res_image_patches = args.input_folder_path + "/image_patches_with_highest_nuclei_count/images_original"
svs_input_folder_path = args.svs_input_folder_path
output_folder_path = args.output_folder_path
visualizations_output_path = output_folder_path + "/visualizations"
copy_tree(input_folder_path + "/visualizations", visualizations_output_path)
highest_nuclei_count_visualization_output_path = visualizations_output_path + "/highest_nuclei_count_annotations_on_most_salient_high_res_image_patch"
svs_loader = svs_l.SVSLoader(svs_input_folder_path)
path_utils.halt_script_if_path_does_not_exist(input_folder_path)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path)
most_salient_image_patch_case_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
input_folder_path + "/visualizations/most_salient_image_patch_high_res")
CID_indexed_high_saliency_image_patch_metadata_objects_dict = image_patch_metadata_object_utils.case_directory_paths_containing_image_patches_to_dict_indexed_by_CID(
most_salient_image_patch_case_directory_paths)
high_nuclei_count_case_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
input_folder_path_for_high_res_image_patches)
for high_nuclei_count_case_directory_path in high_nuclei_count_case_directory_paths:
high_nuclei_count_case_image_patches_paths = path_utils.create_full_paths_to_files_in_directory_path(
high_nuclei_count_case_directory_path)
first_image_patch_path = high_nuclei_count_case_image_patches_paths[0]
image_name = first_image_patch_path.split('/')[-1]
image_patch_metadata_object_with_high_nucleus = image_patch_file_name_parser.parse_image_patch_file_name_into_image_patch_metadata_object(
required=True)
args = parser.parse_args()
input_folder_path = args.input_folder_path
image_patches_with_nuclei_counts_path = args.input_folder_path + '/' + "image_patches_with_nuclei_counts" + "/"
preprocessed_high_res_image_patches_path = args.input_folder_path + '/' + "preprocessed_high_res_image_patches" + "/"
output_folder_path = args.output_folder_path
image_mode = args.image_mode
path_utils.halt_script_if_path_does_not_exist(
image_patches_with_nuclei_counts_path)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path)
output_folder_path_for_images_with_highest_nuclei_count = output_folder_path + '/' + "image_patches_with_highest_nuclei_count"
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path_for_images_with_highest_nuclei_count)
case_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
image_patches_with_nuclei_counts_path)
for case_directory_path in case_directory_paths:
path_to_image_patches = case_directory_path + "/" + "images_original"
case_id = case_directory_path.split('/')[-1]
case_id_output_path = output_folder_path_for_images_with_highest_nuclei_count + "/images_original/" + case_id
path_utils.create_directory_if_directory_does_not_exist_at_path(
case_id_output_path)
all_image_patches_for_case_id_directory_path = preprocessed_high_res_image_patches_path + "/" + case_id
output_image_patch_with_highest_predicted_nuclei_count(
def split_to_jpeg_image_patches(
full_image_path,
full_output_path,
to_resolution_level,
overlapping_percentage,
window_size,
from_resolution_level=enums.ResolutionLevel.LEVEL_0_BASE,
patching_area_x=None,
patching_area_y=None,
patching_area_width=None,
patching_area_height=None):
img = openslide.OpenSlide(full_image_path)
has_specific_patching_area = patching_area_x is not None and patching_area_y is not None and patching_area_width is not None and patching_area_height is not None
if has_specific_patching_area:
original_start_position_x = int(patching_area_x)
original_start_position_y = int(patching_area_y)
width, height = int(patching_area_width), int(patching_area_height)
else:
original_start_position_x = 0
original_start_position_y = 0
width, height = img.level_dimensions[from_resolution_level]
original_start_position_x_label = int(
svs_utils.scale(int(original_start_position_x), from_resolution_level,
to_resolution_level, img))
original_start_position_y_label = int(
svs_utils.scale(int(original_start_position_y), from_resolution_level,
to_resolution_level, img))
width = int(
svs_utils.scale(int(width), from_resolution_level, to_resolution_level,
img))
height = int(
svs_utils.scale(int(height), from_resolution_level,
to_resolution_level, img))
x_start_positions_labels = get_start_positions(
original_start_position_x_label, width, height, window_size,
enums.Axis.X, overlapping_percentage)
y_start_positions_label = get_start_positions(
original_start_position_y_label, width, height, window_size,
enums.Axis.Y, overlapping_percentage)
total_number_of_patches = len(x_start_positions_labels) * len(
y_start_positions_label)
tile_number = 1
for x_index, x_start_position in enumerate(x_start_positions_labels):
for y_index, y_start_position in enumerate(y_start_positions_label):
x_end_position = min(original_start_position_x_label + width,
x_start_position + window_size)
y_end_position = min(original_start_position_y_label + height,
y_start_position + window_size)
patch_width = x_end_position - x_start_position
patch_height = y_end_position - y_start_position
is_image_patch_size_equal_to_window_size = (
(patch_height == window_size) and (patch_width == window_size))
if not is_image_patch_size_equal_to_window_size:
continue
#must always convert to resolution 0 when doing patch extraction
reader_x_start_position = x_start_position
reader_y_start_position = y_start_position
if to_resolution_level != enums.ResolutionLevel.LEVEL_0_BASE:
reader_x_start_position = int(
svs_utils.scale(x_start_position, to_resolution_level,
from_resolution_level, img))
reader_y_start_position = int(
svs_utils.scale(y_start_position, to_resolution_level,
from_resolution_level, img))
patch = img.read_region(
(reader_x_start_position, reader_y_start_position),
to_resolution_level, (patch_width, patch_height))
patch.load()
patch_rgb = Image.new("RGB", patch.size, (255, 255, 255))
patch_rgb.paste(patch, mask=patch.split()[3])
print("\n")
print("Patch data", x_start_position, y_start_position,
to_resolution_level, patch_width, patch_height)
print("Tile size for tile number " + str(tile_number) + ":" +
str(patch.size))
# compress the image
# patch_rgb = patch_rgb.resize(
# (int(patch_rgb.size[0] / compression_factor), int(patch_rgb.size[1] / compression_factor)),
# Image.ANTIALIAS)
# save the image
case_id = full_image_path.split('/')[-1][:-4]
output_subfolder = join(full_output_path, case_id)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_subfolder)
output_image_name = join(
output_subfolder,
image_patch_file_name_builder.build_image_patch_file_name(
case_id, to_resolution_level, x_start_position,
y_start_position, patch_width, patch_height))
patch_rgb.save(output_image_name)
print("Tile", tile_number, "/", total_number_of_patches, "created")
tile_number = tile_number + 1
def extract_image_patch_jpeg(
full_image_path,
full_output_path,
from_resolution_level=enums.ResolutionLevel.LEVEL_0_BASE,
to_resolution_level=None,
patch_area_x=None,
patch_area_y=None,
patch_area_width=None,
patch_area_height=None):
img = openslide.OpenSlide(full_image_path)
original_start_position_x = int(patch_area_x)
original_start_position_y = int(patch_area_y)
width, height = int(patch_area_width), int(patch_area_height)
original_start_position_x_label = int(
svs_utils.scale(int(original_start_position_x), from_resolution_level,
to_resolution_level, img))
original_start_position_y_label = int(
svs_utils.scale(int(original_start_position_y), from_resolution_level,
to_resolution_level, img))
width = int(
svs_utils.scale(int(width), from_resolution_level, to_resolution_level,
img))
height = int(
svs_utils.scale(int(height), from_resolution_level,
to_resolution_level, img))
x_end_position = original_start_position_x_label + width
y_end_position = original_start_position_y_label + height
patch_width = width
patch_height = height
# must always convert to resolution 0 when doing patch extraction
reader_x_start_position = original_start_position_x_label
reader_y_start_position = original_start_position_y_label
if to_resolution_level != enums.ResolutionLevel.LEVEL_0_BASE:
reader_x_start_position = int(
svs_utils.scale(original_start_position_x_label,
to_resolution_level,
enums.ResolutionLevel.LEVEL_0_BASE, img))
reader_y_start_position = int(
svs_utils.scale(original_start_position_y_label,
to_resolution_level,
enums.ResolutionLevel.LEVEL_0_BASE, img))
patch = img.read_region((reader_x_start_position, reader_y_start_position),
to_resolution_level, (patch_width, patch_height))
patch.load()
patch_rgb = Image.new("RGB", patch.size, (255, 255, 255))
patch_rgb.paste(patch, mask=patch.split()[3])
print("\n")
print("Image patch data", original_start_position_x_label,
original_start_position_y_label, to_resolution_level, patch_width,
patch_height)
print("\n")
print("\n")
print("\n")
# compress the image
# patch_rgb = patch_rgb.resize(
# (int(patch_rgb.size[0] / compression_factor), int(patch_rgb.size[1] / compression_factor)),
# Image.ANTIALIAS)
# save the image
case_id = full_image_path.split('/')[-1][:-4]
output_subfolder = join(full_output_path, case_id)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_subfolder)
output_image_name = join(
output_subfolder,
image_patch_file_name_builder.build_image_patch_file_name(
case_id, to_resolution_level, original_start_position_x_label,
original_start_position_y_label, patch_width, patch_height))
patch_rgb.save(output_image_name)
help="The path to the input folder.",
required=True)
parser.add_argument("-o",
"--output_folder_path",
type=str,
help="The path to the output folder."
" If output folder doesn't exists at runtime "
"the script will create it.",
required=True)
args = parser.parse_args()
input_folder_path = args.input_folder_path
output_folder_path = args.output_folder_path
path_utils.halt_script_if_path_does_not_exist(input_folder_path)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path)
output_folder_path_for_prediction_csv_files = output_folder_path + '/' + "saliency_predictions_csvs"
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path_for_prediction_csv_files)
case_paths = path_utils.create_full_paths_to_directories_in_directory_path(
input_folder_path)
for case_path in case_paths:
output_case_prediction_csv_path = output_folder_path_for_prediction_csv_files + '/' + case_path.split(
'/')[-1] + '.csv'
is_case_already_classified = auto_resume_utils.is_case_already_classified_into_output_csv(
case_path, output_case_prediction_csv_path)
if is_case_already_classified:
continue
"--accuracy_percentage_threshold",
type=float,
help="Accuracy percentage threshold for showing annotations",
required=True)
args = parser.parse_args()
svs_input_folder_path = args.svs_input_folder_path
csv_input_folder_path = args.csv_input_folder_path + "/saliency_predictions_csvs"
output_folder_path = args.output_folder_path
accuracy_percentage_threshold = args.accuracy_percentage_threshold
path_utils.halt_script_if_path_does_not_exist(svs_input_folder_path)
path_utils.halt_script_if_path_does_not_exist(csv_input_folder_path)
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path)
output_folder_path_for_visualization_directory = output_folder_path + '/' + "visualizations"
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path_for_visualization_directory)
output_folder_path_for_saliancy_prediction_visualization_directory = output_folder_path_for_visualization_directory + '/' + "saliency_prediction_overview_visualization"
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path_for_saliancy_prediction_visualization_directory)
output_folder_path_for_most_salient_image_patch_high_res_visualization_directory = output_folder_path_for_visualization_directory + '/' + "most_salient_image_patch_high_res"
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_folder_path_for_saliancy_prediction_visualization_directory)
tcga_download_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
svs_input_folder_path)
