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
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# -- encoding: utf-8 --
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"""
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Description: 图片锐度自适应增强
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Version:
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Create: 2025/01/13
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"""
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import time
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from typing import Dict, Any
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import cv2
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import numpy as np
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from loguru import logger
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from deepdata.common.utils import bytes_transform
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from deepdata.core.base_op import Mapper
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class ImgSharpness(Mapper):
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"""图片锐度自适应增强"""
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def __init__(self, *args, **kwargs):
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super(ImgSharpness, self).__init__(*args, **kwargs)
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# 自适应增强参数
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self.factor_threshold = 1.1 # 图片增强因子下限(不作为参数传入)。
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self.standard_mean = 100 # 图片增强后的平均锐度(不作为参数传入)。
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self.kernel = self._init_kernel()
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self.eps = 1 # 小值，计算图像锐度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。
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@classmethod
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def _init_kernel(cls):
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kernel = np.array([[1, 1, 1],
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[1, 5, 1],
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[1, 1, 1]])
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# 对卷积核进行归一化
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kernel = kernel / np.sum(kernel)
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return kernel
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def enhance_sharpness(self, image_data: np.ndarray, file_name):
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"""锐度自适应增强方法"""
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# 打开图像并转换为灰度图像
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image_gray = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)
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sharpness = np.abs(cv2.Laplacian(image_gray, cv2.CV_8U)).mean()
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sharpness_factor = self.standard_mean / (sharpness + self.eps)
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# 图片锐度较高，不需要增强锐度
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if sharpness_factor <= 1:
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logger.info(f"fileName: {file_name}, method: ImgSharpness not need enhancement")
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return image_data
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filtered_img = cv2.filter2D(image_data, -1, self.kernel)
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cv2.addWeighted(image_data, sharpness_factor, filtered_img, 1.0 - sharpness_factor, 0, dst=image_data)
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return image_data
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def execute(self, sample: Dict[str, Any]):
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start = time.time()
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self.read_file_first(sample)
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img_bytes = sample[self.data_key]
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file_name = sample[self.filename_key]
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file_type = "." + sample[self.filetype_key]
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if img_bytes:
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# 进行图片增强
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img_data = bytes_transform.bytes_to_numpy(img_bytes)
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img_data = self.enhance_sharpness(img_data, file_name)
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sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
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logger.info(f"fileName: {file_name}, method: ImgSharpness costs {time.time() - start:6f} s")
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return sample
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# -- encoding: utf-8 --␍␍
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␍␍
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"""␍␍
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Description: 图片锐度自适应增强␍␍
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Version:␍␍
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Create: 2025/01/13␍␍
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"""␍␍
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␍␍
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import time␍␍
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␍␍
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from typing import Dict, Any␍␍
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␍␍
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import cv2␍␍
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import numpy as np␍␍
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from loguru import logger␍␍
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␍␍
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from deepdata.common.utils import bytes_transform␍␍
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from deepdata.core.base_op import Mapper␍␍
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␍␍
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␍␍
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class ImgSharpness(Mapper):␍␍
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"""图片锐度自适应增强"""␍␍
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␍␍
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def __init__(self, *args, **kwargs):␍␍
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super(ImgSharpness, self).__init__(*args, **kwargs)␍␍
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# 自适应增强参数␍␍
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self.factor_threshold = 1.1 # 图片增强因子下限(不作为参数传入)。␍␍
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self.standard_mean = 100 # 图片增强后的平均锐度(不作为参数传入)。␍␍
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self.kernel = self._init_kernel()␍␍
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self.eps = 1 # 小值，计算图像锐度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。␍␍
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␍␍
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@classmethod␍␍
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def _init_kernel(cls):␍␍
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kernel = np.array([[1, 1, 1],␍␍
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[1, 5, 1],␍␍
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[1, 1, 1]])␍␍
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# 对卷积核进行归一化␍␍
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kernel = kernel / np.sum(kernel)␍␍
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return kernel␍␍
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␍␍
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def enhance_sharpness(self, image_data: np.ndarray, file_name):␍␍
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"""锐度自适应增强方法"""␍␍
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␍␍
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# 打开图像并转换为灰度图像␍␍
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image_gray = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)␍␍
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sharpness = np.abs(cv2.Laplacian(image_gray, cv2.CV_8U)).mean()␍␍
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sharpness_factor = self.standard_mean / (sharpness + self.eps)␍␍
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␍␍
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# 图片锐度较高，不需要增强锐度␍␍
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if sharpness_factor <= 1:␍␍
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logger.info(f"fileName: {file_name}, method: ImgSharpness not need enhancement")␍␍
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return image_data␍␍
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␍␍
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filtered_img = cv2.filter2D(image_data, -1, self.kernel)␍␍
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cv2.addWeighted(image_data, sharpness_factor, filtered_img, 1.0 - sharpness_factor, 0, dst=image_data)␍␍
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return image_data␍␍
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␍␍
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def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:␍␍
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start = time.time()␍␍
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self.read_file_first(sample)␍␍
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img_bytes = sample[self.data_key]␍␍
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file_name = sample[self.filename_key]␍␍
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file_type = "." + sample[self.filetype_key]␍␍
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if img_bytes:␍␍
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# 进行图片增强␍␍
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img_data = bytes_transform.bytes_to_numpy(img_bytes)␍␍
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img_data = self.enhance_sharpness(img_data, file_name)␍␍
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sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)␍␍
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logger.info(f"fileName: {file_name}, method: ImgSharpness costs {time.time() - start:.6f} s")␍␍
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return sample␍␍
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