convert_format.py

"""convert_format.py.

Used to convert output to a format that can be used for visualisaton with QuPath.
Note, this is only used for tile segmentation results; not WSI.

"""

import os
import re
import glob
import json
import pathlib
import numpy as np
import shutil

from misc.utils import rm_n_mkdir, mkdir

####
def to_qupath(file_path, nuc_pos_list, nuc_type_list, type_info_dict):
    """
    For QuPath v0.2.3
    """

    def rgb2int(rgb):
        r, g, b = rgb
        return (r << 16) + (g << 8) + b

    nuc_pos_list = np.array(nuc_pos_list)
    nuc_type_list = np.array(nuc_type_list)
    assert nuc_pos_list.shape[0] == nuc_type_list.shape[0]
    with open(file_path, "w") as fptr:
        fptr.write("x\ty\tclass\tname\tcolor\n")

        nr_nuc = nuc_pos_list.shape[0]
        for idx in range(nr_nuc):
            nuc_type = nuc_type_list[idx]
            nuc_pos = nuc_pos_list[idx]
            type_name = type_info_dict[nuc_type][0]
            type_color = type_info_dict[nuc_type][1]
            type_color = rgb2int(type_color)  # color in qupath format
            fptr.write(
                "{x}\t{y}\t{type_class}\t{type_name}\t{type_color}\n".format(
                    x=nuc_pos[0],
                    y=nuc_pos[1],
                    type_class="",
                    type_name=type_name,
                    type_color=type_color,
                )
            )
    return


####
if __name__ == "__main__":
    target_format = "qupath"
    # to rescale the coordinate set to match with lv0 mag of the wsi
    scale_factor = 1.0
    root_dir = "dataset/dummy/out/"

    # to define the name, and color conversion code for each target format
    type_info_dict = {
        0: ("nolabe", (0, 0, 0)),  # no label
        1: ("neopla", (255, 0, 0)),  # neoplastic
        2: ("inflam", (0, 255, 0)),  # inflamm
        3: ("connec", (0, 0, 255)),  # connective
        4: ("necros", (255, 255, 0)),  # dead
        5: ("no-neo", (255, 165, 0)),  # non-neoplastic epithelial
    }

    patterning = lambda x: re.sub("([\[\]])", "[\\1]", x)
    code_name_list = glob.glob(patterning("%s/*.json" % root_dir))
    code_name_list = [pathlib.Path(v).stem for v in code_name_list]
    code_name_list.sort()

    output_dir = root_dir

    for code_name in code_name_list[:]:
        nuc_info_path = "%s/%s.json" % (root_dir, code_name)
        if not os.path.exists(nuc_info_path):
            continue
        print(code_name)

        with open(nuc_info_path, "r") as handle:
            info_dict = json.load(handle)["nuc"]

        # from json to array
        new_info_dict = {}
        for inst_id, inst_info in info_dict.items():
            new_inst_info = {}
            for info_name, info_value in inst_info.items():
                if isinstance(info_value, list):
                    info_value = np.array(info_value)
                    info_value = info_value * scale_factor
                    info_value = info_value.astype(np.int32)
                new_inst_info[info_name] = info_value
            new_info_dict[inst_id] = new_inst_info

        centroid_list = np.array([v["centroid"] for v in list(new_info_dict.values())])
        type_list = np.array([v["type"] for v in list(new_info_dict.values())])

        save_path = "%s/%s.tsv" % (output_dir, code_name)
        to_qupath(save_path, centroid_list, type_list, type_info_dict)