def output_image_patch_with_highest_predicted_nuclei_count(
image_patches_path,
output_diretory_path,
all_image_patches_for_case_id_directory_path,
image_mode,
with_image_mask=False):
image_patch_paths = path_utils.create_full_paths_to_files_in_directory_path(
image_patches_path)
image_patch_path_with_highest_nuclei_count = get_image_patch_path_with_highest_nuclei_count(
image_patch_paths)
image_patch_file = image_patch_path_with_highest_nuclei_count.split(
'/')[-1]
if (with_image_mask is True):
new_image_patch_path = output_diretory_path + "/" + image_patch_file
shutil.copy(image_patch_path_with_highest_nuclei_count,
new_image_patch_path)
return
all_image_patches_mode_paths = path_utils.create_full_paths_to_directories_in_directory_path(
all_image_patches_for_case_id_directory_path)
directory_path_for_all_image_patches_with_specific_image_mode = get_path_for_all_image_patches_with_specific_image_mode(
all_image_patches_mode_paths, image_mode)
all_image_patches_with_specific_image_mode_paths = path_utils.create_full_paths_to_files_in_directory_path(
directory_path_for_all_image_patches_with_specific_image_mode)
parse_target_image_patch_file_name_to_dict = image_patch_file_name_parser.parse_image_patch_file_name_to_dict(
image_patch_file)
target_image_patch_metadata_object = image_patch_metadata_object.image_patch_metadata_object_from_image_patch_dict(
parse_target_image_patch_file_name_to_dict)
target_image_patch_path = get_target_image_patch_path(
all_image_patches_with_specific_image_mode_paths,
target_image_patch_metadata_object)
new_image_patch_path = output_diretory_path + "/" + image_patch_file
shutil.copy(target_image_patch_path, new_image_patch_path)
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(
image_name)
corresponding_image_patch_metadata_object_with_highest_saliency = CID_indexed_high_saliency_image_patch_metadata_objects_dict[
required=True)
args = parser.parse_args()
svs_input_folder_path = args.svs_input_folder_path
csv_input_folder_path = args.csv_input_folder_path
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)
full_tcga_download_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
svs_input_folder_path)
full_image_patch_data_dict_paths = path_utils.create_full_paths_to_files_in_directory_path(
csv_input_folder_path)
for full_tcga_download_directories_path_index, full_tcga_download_directory_path in enumerate(
full_tcga_download_directory_paths):
full_image_name_paths = path_utils.create_full_paths_to_files_in_directory_path(
full_tcga_download_directory_path)
image_name = full_image_name_paths[0].split('/')[-1][:-4]
output_path = output_folder_path + '/' + image_name + '/'
path_utils.create_directory_if_directory_does_not_exist_at_path(
output_path)
thumbnail = create_jpeg_thumbnail_of_wsi(full_image_name_paths[0])
thumbnail = thumbnail.convert("RGB")
thumbnail.save(output_path + image_name + "_original.jpeg", 'JPEG')
return selection_processing_log_image
parser = argparse.ArgumentParser(description='Create visual image patch selection process log.')
parser.add_argument("-i", "--input_folder_path", type=str, 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
visualizations_input_folder_path = args.input_folder_path + "/visualizations"
output_folder_path = args.output_folder_path
visualizations_output_path = output_folder_path + "/visualizations/selection_process_log_image/"
path_utils.create_directory_if_directory_does_not_exist_at_path(output_folder_path)
path_utils.create_directory_if_directory_does_not_exist_at_path(visualizations_output_path)
saliency_prediction_overview_visualization_paths = path_utils.create_full_paths_to_directories_in_directory_path(
input_folder_path + "/visualizations/saliency_prediction_overview_visualization")
list_of_case_ids = [case_id_path.split('/')[-1] for case_id_path in saliency_prediction_overview_visualization_paths]
for case_id in list_of_case_ids:
image_patch_selection_process_log_image = create_image_patch_selection_process_log_image(case_id)
image_patch_selection_process_log_image.save(visualizations_output_path + case_id +".png", 'PNG')
copy_tree(visualizations_input_folder_path, output_folder_path + "/visualizations")
copy_tree(input_folder_path + "/image_patches_with_highest_nuclei_count", output_folder_path + "/image_patches_with_highest_nuclei_count")
"--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)
case_directory_paths = path_utils.create_full_paths_to_directories_in_directory_path(
input_folder_path)
for case_directory_path in case_directory_paths:
full_image_patch_paths = path_utils.create_full_paths_to_files_in_directory_path(
case_directory_path)
for full_input_image_patch_path in full_image_patch_paths:
case_id = full_input_image_patch_path.split('/')[-2]
case_id_directory_path = output_folder_path + "/" + case_id
path_utils.create_directory_if_directory_does_not_exist_at_path(
case_id_directory_path)
image_patch_file = full_input_image_patch_path.split('/')[-1][:-4]
image_patch_root_path = case_id_directory_path + "/" + image_patch_file
path_utils.create_directory_if_directory_does_not_exist_at_path(
case_id_directory_path)
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(
path_to_image_patches, case_id_output_path,
all_image_patches_for_case_id_directory_path, image_mode)
for case_directory_path in case_directory_paths:
path_to_image_patches = case_directory_path + "/" + "images_annotated"
case_id = case_directory_path.split('/')[-1]
