prepare_patches.py

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import glob
import math
import os
import pathlib
import shutil
from argparse import ArgumentParser

import numpy as np
import scipy.io as sio
import tqdm
from PIL import Image


def load_img(path):
    return np.array(Image.open(path).convert("RGB"))


def load_ann(path):
    """
    This function is specific to CoNSeP dataset.
    If using other datasets, the code below may need to be modified.
    """
    # assumes that ann is HxW
    ann_inst = sio.loadmat(path)["inst_map"]
    ann_type = sio.loadmat(path)["type_map"]

    # merge classes for CoNSeP (utilise 3 nuclei classes and background keep the same with paper)
    ann_type[(ann_type == 3) | (ann_type == 4)] = 3
    ann_type[(ann_type == 5) | (ann_type == 6) | (ann_type == 7)] = 4

    ann = np.dstack([ann_inst, ann_type])
    ann = ann.astype("int32")

    return ann


class PatchExtractor:
    """Extractor to generate patches with or without padding.
    Turn on debug mode to see how it is done.

    Args:
        x         : input image, should be of shape HWC
        patch_size  : a tuple of (h, w)
        step_size : a tuple of (h, w)
    Return:
        a list of sub patches, each patch has dtype same as x

    Examples:
        >>> extractor = PatchExtractor((450, 450), (120, 120))
        >>> img = np.full([1200, 1200, 3], 255, np.uint8)
        >>> patches = extractor.extract(img, 'mirror')

    """

    def __init__(self, patch_size, step_size):
        self.patch_type = "mirror"
        self.patch_size = patch_size
        self.step_size = step_size

    def __get_patch(self, x, ptx):
        pty = (ptx[0] + self.patch_size[0], ptx[1] + self.patch_size[1])
        win = x[ptx[0] : pty[0], ptx[1] : pty[1]]
        assert (
            win.shape[0] == self.patch_size[0] and win.shape[1] == self.patch_size[1]
        ), "[BUG] Incorrect Patch Size {0}".format(win.shape)
        return win

    def __extract_valid(self, x):
        """Extracted patches without padding, only work in case patch_size > step_size.

        Note: to deal with the remaining portions which are at the boundary a.k.a
        those which do not fit when slide left->right, top->bottom), we flip
        the sliding direction then extract 1 patch starting from right / bottom edge.
        There will be 1 additional patch extracted at the bottom-right corner.

        Args:
            x         : input image, should be of shape HWC
            patch_size  : a tuple of (h, w)
            step_size : a tuple of (h, w)
        Return:
            a list of sub patches, each patch is same dtype as x

        """
        im_h = x.shape[0]
        im_w = x.shape[1]

        def extract_infos(length, patch_size, step_size):
            flag = (length - patch_size) % step_size != 0
            last_step = math.floor((length - patch_size) / step_size)
            last_step = (last_step + 1) * step_size
            return flag, last_step

        h_flag, h_last = extract_infos(im_h, self.patch_size[0], self.step_size[0])
        w_flag, w_last = extract_infos(im_w, self.patch_size[1], self.step_size[1])

        sub_patches = []
        # Deal with valid block
        for row in range(0, h_last, self.step_size[0]):
            for col in range(0, w_last, self.step_size[1]):
                win = self.__get_patch(x, (row, col))
                sub_patches.append(win)
        # Deal with edge case
        if h_flag:
            row = im_h - self.patch_size[0]
            for col in range(0, w_last, self.step_size[1]):
                win = self.__get_patch(x, (row, col))
                sub_patches.append(win)
        if w_flag:
            col = im_w - self.patch_size[1]
            for row in range(0, h_last, self.step_size[0]):
                win = self.__get_patch(x, (row, col))
                sub_patches.append(win)
        if h_flag and w_flag:
            ptx = (im_h - self.patch_size[0], im_w - self.patch_size[1])
            win = self.__get_patch(x, ptx)
            sub_patches.append(win)
        return sub_patches

    def __extract_mirror(self, x):
        """Extracted patches with mirror padding the boundary such that the
        central region of each patch is always within the orginal (non-padded)
        image while all patches' central region cover the whole orginal image.

        Args:
            x         : input image, should be of shape HWC
            patch_size  : a tuple of (h, w)
            step_size : a tuple of (h, w)
        Return:
            a list of sub patches, each patch is same dtype as x

        """
        diff_h = self.patch_size[0] - self.step_size[0]
        padt = diff_h // 2
        padb = diff_h - padt

        diff_w = self.patch_size[1] - self.step_size[1]
        padl = diff_w // 2
        padr = diff_w - padl

        pad_type = "reflect"
        x = np.lib.pad(x, ((padt, padb), (padl, padr), (0, 0)), pad_type)
        sub_patches = self.__extract_valid(x)
        return sub_patches

    def extract(self, x, patch_type):
        patch_type = patch_type.lower()
        self.patch_type = patch_type
        if patch_type == "valid":
            return self.__extract_valid(x)
        elif patch_type == "mirror":
            return self.__extract_mirror(x)
        else:
            raise ValueError(f"Unknown Patch Type {patch_type}")


def main(cfg):
    xtractor = PatchExtractor(cfg["patch_size"], cfg["step_size"])
    for phase in cfg["phase"]:
        img_dir = os.path.join(cfg["root"], f"{phase}/Images")
        ann_dir = os.path.join(cfg["root"], f"{phase}/Labels")

        file_list = glob.glob(os.path.join(ann_dir, f"*{cfg['label_suffix']}"))
        file_list.sort()  # ensure same ordering across platform

        out_dir = f"{cfg['root']}/Prepared/{phase}"
        if os.path.isdir(out_dir):
            shutil.rmtree(out_dir)
        os.makedirs(out_dir)

        pbar_format = "Process File: |{bar}| {n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_fmt}]"
        pbarx = tqdm.tqdm(total=len(file_list), bar_format=pbar_format, ascii=True, position=0)

        for file_path in file_list:
            base_name = pathlib.Path(file_path).stem

            img = load_img(f"{img_dir}/{base_name}.{cfg['image_suffix']}")
            ann = load_ann(f"{ann_dir}/{base_name}.{cfg['label_suffix']}")

            # *
            img = np.concatenate([img, ann], axis=-1)
            sub_patches = xtractor.extract(img, cfg["extract_type"])

            pbar_format = "Extracting  : |{bar}| {n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_fmt}]"
            pbar = tqdm.tqdm(total=len(sub_patches), leave=False, bar_format=pbar_format, ascii=True, position=1)

            for idx, patch in enumerate(sub_patches):
                image_patch = patch[..., :3]
                inst_map_patch = patch[..., 3:4]
                type_map_patch = patch[..., 4:5]
                np.save("{0}/{1}_{2:03d}_image.npy".format(out_dir, base_name, idx), image_patch)
                np.save("{0}/{1}_{2:03d}_inst_map.npy".format(out_dir, base_name, idx), inst_map_patch)
                np.save("{0}/{1}_{2:03d}_type_map.npy".format(out_dir, base_name, idx), type_map_patch)
                pbar.update()
            pbar.close()
            # *

            pbarx.update()
        pbarx.close()


def parse_arguments():
    parser = ArgumentParser(description="Extract patches from the original images")

    parser.add_argument(
        "--root",
        type=str,
        default="/workspace/Data/Pathology/CoNSeP",
        help="root path to image folder containing training/test",
    )
    parser.add_argument(
        "--phase",
        nargs="+",
        type=str,
        default=["Train", "Test"],
        dest="phase",
        help="Phases of data need to be extracted",
    )
    parser.add_argument("--type", type=str, default="mirror", dest="extract_type", help="Choose 'mirror' or 'valid'")
    parser.add_argument("--is", type=str, default="png", dest="image_suffix", help="image file name suffix")
    parser.add_argument("--ls", type=str, default="mat", dest="label_suffix", help="label file name suffix")
    parser.add_argument("--ps", nargs="+", type=int, default=[540, 540], dest="patch_size", help="patch size")
    parser.add_argument("--ss", nargs="+", type=int, default=[164, 164], dest="step_size", help="patch size")
    args = parser.parse_args()
    config_dict = vars(args)

    return config_dict


if __name__ == "__main__":
    cfg = parse_arguments()

    main(cfg)