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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:
>>> xtractor = PatchExtractor((450, 450), (120, 120))
>>> img = np.full([1200, 1200, 3], 255, np.uint8)
>>> patches = xtractor.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']}")
np.save("{0}/label_{1}.npy".format(out_dir, base_name), ann)
np.save("{0}/image_{1}.npy".format(out_dir, base_name), img)
# *
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)
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