feat(deepdata): 引入修改密码、文件预览组件与清洗算子增强，deepflow 改名为 deep-flow
本次同步 09-12 至 09-28 的代码变更，主要内容： 新增功能 - 修改密码：后端新增 ChangePasswordRequest DTO 与UserController/UserService 对应接口，前端新增 ChangePasswordDialog 并在 Header 接入入口 - 文件预览组件重构：file-preview 目录拆分出 VideoPreview（支持 blobUrl 直传）、 ImagePreview、AudioPreview，index.tsx 统一调度各类型预览器 - 新增 my_ops 算子模块：markitdown_parser、mineru_pdf_parser、text_quality_evaluator - 新增 tools/minio-file-server MinIO 文件服务 功能增强 - 数据清洗：cleaning_task_service/scheduler/routes 重构， data_juicer_executor 与 wrapper 增强，OperatorTable/LogsTable/TaskDetail 同步调整 - 数据标注：annotation sync.py、project.py 接口与schema 更新 - 数据采集、算子市场、首页与菜单路由相关页面调整 - DataJuicer 算子接入（useOperatorOperations、dj_ops_schema） 结构调整 - deepflow/ 更名为 deep-flow/（419 个文件重命名） - 新增 helm 部署编排、GitHub Actions 之外的自建镜像构建脚本 未纳入本次提交 - scripts-backup/、runtime/python-executor-backup/、components_backup/（备份目录） - *.backup*、__pycache__ 产物 - zh_core_web_sm-3.8.0-py3-none-any.whl（46MB 构建产物） - DataX/（内嵌独立仓库，远程亦未收录其源码）
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沃巴蒂
@@ -1,98 +1,98 @@
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# -- encoding: utf-8 --
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"""
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Description: 医疗图片按坐标切片
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Create: 2025/02/08 11:00
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"""
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import copy
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import time
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from typing import List, Tuple, Dict, Any
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import itertools
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from loguru import logger
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from openslide import OpenSlide
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import numpy as np
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from deepdata.core.base_op import Slicer
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from deepdata.common.utils import bytes_transform
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class SimpleSlicer(Slicer):
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def __init__(self, *args, **kwargs):
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super(SimpleSlicer, self).__init__(*args, **kwargs)
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self._target_size = kwargs.get("sliceSize", [128, 128])
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self._overlap = kwargs.get("overlap", 0)
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self.last_ops = True
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if not isinstance(self._target_size, List):
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raise TypeError(f"<targetSize> received as {type(self._target_size)}, but expected list.")
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if len(self._target_size) != 2:
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raise ValueError(f"<targetSize> has {len(self._target_size)} elements, but expected 2.")
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if not all(isinstance(dim, int) for dim in self._target_size):
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raise TypeError(f"Elements in <targetSize> must be integers, but got {self._target_size}.")
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if not isinstance(self._overlap, (int, float)):
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raise TypeError(f"<overlap> received as {type(self._overlap)}, but expected int.")
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if self._overlap < 0 or self._overlap > 1:
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raise ValueError(
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f"<overlap> received an out of range value: {self._overlap}, "
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f"but (0 <= overlap <= 1) is expected."
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)
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def execute(self, sample: Dict[str, Any]) -> List[Dict]:
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start = time.time()
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slide: OpenSlide = OpenSlide(sample["filePath"])
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if not isinstance(slide, OpenSlide):
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logger.error("Not desired <Image.Image> object.")
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dimensions: tuple[int, int] = slide.dimensions
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target_size = self._target_size
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overlap = self._overlap
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patch_num = self.auto_simple_slicer(sample, slide, dimensions, target_size, overlap)
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sample["slice_num"] = patch_num
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file_name = sample[self.filename_key]
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logger.info(f"fileName: {file_name}, method: CoordinateSlider costs {(time.time() - start):6f} s")
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return [sample]
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def auto_simple_slicer(
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self,
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original_sample: Dict[str, Any],
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slide: OpenSlide,
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dimensions: Tuple[int, int],
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target_size: Tuple[int, int],
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overlap: float
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) -> int:
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"""
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自动根据给定规格切片原图像
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Return:
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List[Content] 每个 content 都是一个 data 为 patch 的 content
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"""
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stride_x, stride_y = map(lambda x: int(x * (1 - overlap)), target_size)
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w, h = target_size
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patch_no = 0
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for x, y in itertools.product(
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range(0, dimensions[0] - w + 1, stride_x),
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range(0, dimensions[1] - h + 1, stride_y)
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):
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# 切片
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region = slide.read_region((x, y), 0, target_size)
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region_np = np.array(region.convert("RGB"))
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patch_sample = copy.deepcopy(original_sample)
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patch_sample[self.data_key] = bytes_transform.numpy_to_bytes(region_np, ".png")
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patch_no += 1
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self.save_patch_sample(patch_sample, patch_no, save_format="image")
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logger.info(f"One image sliced into pieces: {patch_no}")
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return patch_no
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# -- encoding: utf-8 --␍
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␍
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"""␍
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Description: 医疗图片按坐标切片␍
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Create: 2025/02/08 11:00␍
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"""␍
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import copy␍
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import time␍
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from typing import List, Tuple, Dict, Any␍
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␍
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import itertools␍
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from loguru import logger␍
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␍
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from openslide import OpenSlide␍
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import numpy as np␍
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␍
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from deepdata.core.base_op import Slicer␍
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␍
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from deepdata.common.utils import bytes_transform␍
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␍
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␍
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class SimpleSlicer(Slicer):␍
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␍
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def __init__(self, *args, **kwargs):␍
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super(SimpleSlicer, self).__init__(*args, **kwargs)␍
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␍
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self._target_size = kwargs.get("sliceSize", [128, 128])␍
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self._overlap = kwargs.get("overlap", 0)␍
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self.last_ops = True␍
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␍
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if not isinstance(self._target_size, List):␍
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raise TypeError(f"<targetSize> received as {type(self._target_size)}, but expected list.")␍
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if len(self._target_size) != 2:␍
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raise ValueError(f"<targetSize> has {len(self._target_size)} elements, but expected 2.")␍
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if not all(isinstance(dim, int) for dim in self._target_size):␍
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raise TypeError(f"Elements in <targetSize> must be integers, but got {self._target_size}.")␍
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if not isinstance(self._overlap, (int, float)):␍
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raise TypeError(f"<overlap> received as {type(self._overlap)}, but expected int.")␍
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if self._overlap < 0 or self._overlap > 1:␍
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raise ValueError(␍
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f"<overlap> received an out of range value: {self._overlap}, "␍
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f"but (0 <= overlap <= 1) is expected."␍
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)␍
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␍
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def execute(self, sample: Dict[str, Any]) -> List[Dict]:␍
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start = time.time()␍
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␍
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slide: OpenSlide = OpenSlide(sample["filePath"])␍
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if not isinstance(slide, OpenSlide):␍
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logger.error("Not desired <Image.Image> object.")␍
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dimensions: tuple[int, int] = slide.dimensions␍
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␍
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target_size = self._target_size␍
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overlap = self._overlap␍
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␍
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patch_num = self.auto_simple_slicer(sample, slide, dimensions, target_size, overlap)␍
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sample["slice_num"] = patch_num␍
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␍
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file_name = sample[self.filename_key]␍
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logger.info(f"fileName: {file_name}, method: CoordinateSlider costs {(time.time() - start):.6f} s")␍
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␍
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return [sample]␍
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␍
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def auto_simple_slicer(␍
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self,␍
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original_sample: Dict[str, Any],␍
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slide: OpenSlide,␍
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dimensions: Tuple[int, int],␍
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target_size: Tuple[int, int],␍
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overlap: float␍
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) -> int:␍
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"""␍
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自动根据给定规格切片原图像␍
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␍
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Return: ␍
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List[Content] 每个 content 都是一个 data 为 patch 的 content␍
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"""␍
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stride_x, stride_y = map(lambda x: int(x * (1 - overlap)), target_size)␍
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w, h = target_size␍
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␍
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patch_no = 0␍
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for x, y in itertools.product(␍
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range(0, dimensions[0] - w + 1, stride_x),␍
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range(0, dimensions[1] - h + 1, stride_y)␍
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):␍
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# 切片␍
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region = slide.read_region((x, y), 0, target_size)␍
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␍
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region_np = np.array(region.convert("RGB"))␍
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␍
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patch_sample = copy.deepcopy(original_sample)␍
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patch_sample[self.data_key] = bytes_transform.numpy_to_bytes(region_np, ".png")␍
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patch_no += 1␍
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self.save_patch_sample(patch_sample, patch_no, save_format="image")␍
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␍
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logger.info(f"One image sliced into pieces: {patch_no}")␍
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␍
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return patch_no␍
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