You've already forked DataMate
99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
# -- encoding: utf-8 --
|
|
|
|
"""
|
|
Description: 医疗图片按坐标切片
|
|
Create: 2025/02/08 11:00
|
|
"""
|
|
import copy
|
|
import time
|
|
from typing import List, Tuple, Dict, Any
|
|
|
|
import itertools
|
|
from loguru import logger
|
|
|
|
from openslide import OpenSlide
|
|
import numpy as np
|
|
|
|
from datamate.core.base_op import Slicer
|
|
|
|
from datamate.common.utils import bytes_transform
|
|
|
|
|
|
class SimpleSlicer(Slicer):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(SimpleSlicer, self).__init__(*args, **kwargs)
|
|
|
|
self._target_size = kwargs.get("sliceSize", [128, 128])
|
|
self._overlap = kwargs.get("overlap", 0)
|
|
self.last_ops = True
|
|
|
|
if not isinstance(self._target_size, List):
|
|
raise TypeError(f"<targetSize> received as {type(self._target_size)}, but expected list.")
|
|
if len(self._target_size) != 2:
|
|
raise ValueError(f"<targetSize> has {len(self._target_size)} elements, but expected 2.")
|
|
if not all(isinstance(dim, int) for dim in self._target_size):
|
|
raise TypeError(f"Elements in <targetSize> must be integers, but got {self._target_size}.")
|
|
if not isinstance(self._overlap, (int, float)):
|
|
raise TypeError(f"<overlap> received as {type(self._overlap)}, but expected int.")
|
|
if self._overlap < 0 or self._overlap > 1:
|
|
raise ValueError(
|
|
f"<overlap> received an out of range value: {self._overlap}, "
|
|
f"but (0 <= overlap <= 1) is expected."
|
|
)
|
|
|
|
def execute(self, sample: Dict[str, Any]) -> List[Dict]:
|
|
start = time.time()
|
|
|
|
slide: OpenSlide = OpenSlide(sample["filePath"])
|
|
if not isinstance(slide, OpenSlide):
|
|
logger.error("Not desired <Image.Image> object.")
|
|
dimensions: tuple[int, int] = slide.dimensions
|
|
|
|
target_size = self._target_size
|
|
overlap = self._overlap
|
|
|
|
patch_num = self.auto_simple_slicer(sample, slide, dimensions, target_size, overlap)
|
|
sample["slice_num"] = patch_num
|
|
|
|
file_name = sample[self.filename_key]
|
|
logger.info(f"fileName: {file_name}, method: CoordinateSlider costs {(time.time() - start):6f} s")
|
|
|
|
return [sample]
|
|
|
|
def auto_simple_slicer(
|
|
self,
|
|
original_sample: Dict[str, Any],
|
|
slide: OpenSlide,
|
|
dimensions: Tuple[int, int],
|
|
target_size: Tuple[int, int],
|
|
overlap: float
|
|
) -> int:
|
|
"""
|
|
自动根据给定规格切片原图像
|
|
|
|
Return:
|
|
List[Content] 每个 content 都是一个 data 为 patch 的 content
|
|
"""
|
|
stride_x, stride_y = map(lambda x: int(x * (1 - overlap)), target_size)
|
|
w, h = target_size
|
|
|
|
patch_no = 0
|
|
for x, y in itertools.product(
|
|
range(0, dimensions[0] - w + 1, stride_x),
|
|
range(0, dimensions[1] - h + 1, stride_y)
|
|
):
|
|
# 切片
|
|
region = slide.read_region((x, y), 0, target_size)
|
|
|
|
region_np = np.array(region.convert("RGB"))
|
|
|
|
patch_sample = copy.deepcopy(original_sample)
|
|
patch_sample[self.data_key] = bytes_transform.numpy_to_bytes(region_np, ".png")
|
|
patch_no += 1
|
|
self.save_patch_sample(patch_sample, patch_no, save_format="image")
|
|
|
|
logger.info(f"One image sliced into pieces: {patch_no}")
|
|
|
|
return patch_no
|