沃巴蒂
ff22ed3a8b
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）
102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
# -- encoding: utf-8 --
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"""
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Description: 图像亮度增强算子。
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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 numpy as np
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import cv2
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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 ImgBrightness(Mapper):
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"""图片亮度自适应增强"""
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def __init__(self, *args, **kwargs):
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super(ImgBrightness, 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 = 140 # 图片增强后的平均亮度(不作为参数传入)。
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self.gamma = 1.5 # gamma correction 中的gamma系数，大于1时，使得图像变亮。小于1时，使得图像变暗(不作为参数传入)。
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self.brightness_upper_bound = 0.35 # 非线性亮度增强阈值上界: 超过这个百分比，就进行线性亮度增强(不作为参数传入)。
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self.eps = 1 # 极小值，计算图像亮度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。
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@staticmethod
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def _get_grey_mean(src: np.ndarray):
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gray_image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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return np.mean(gray_image)
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@staticmethod
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def _return_gamma_table(gamma):
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"""返回gamma校正对应的查找表"""
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scale = np.power(255, 1 - gamma).astype(np.float64)
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return np.power(np.arange(256), gamma) * scale
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@staticmethod
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def _return_linear_table(factor):
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"""返回线性变换对应的查找表"""
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linear_table = np.arange(256) * factor
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return np.clip(linear_table, 0, 255).astype(np.uint8)
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def enhance_brightness_linear(self, image_data: np.ndarray, file_name):
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average_brightness = self._get_grey_mean(image_data)
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brightness_factor = self.standard_mean / (average_brightness + self.eps)
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# 图像过亮，不需要增强亮度
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if brightness_factor <= 1:
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logger.info(f"fileName: {file_name}, method: ImgBrightness not need enhancement")
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return image_data
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brightness_factor = max(brightness_factor, self.factor_threshold)
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linear_table = ImgBrightness._return_linear_table(brightness_factor)
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cv2.LUT(image_data, linear_table, dst=image_data)
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return image_data
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def enhance_brightness(self, image_data: np.ndarray, file_name):
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'''
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亮度自适应增强方法。
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Args:
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image_data: nd.array 格式图片
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gamma: gamma变换因子参数。经验值常用1.5, 已写成了成员变量。
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Returns:
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亮度自适应增强后的图片
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'''
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# 计算图片平均亮度
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average_brightness = self._get_grey_mean(image_data)
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# 进行 gamma 校正
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if average_brightness / 255 <= self.brightness_upper_bound:
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# 预计算查找表
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gamma_table = ImgBrightness._return_gamma_table(1 / self.gamma).astype(np.uint8)
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cv2.LUT(image_data, gamma_table, dst=image_data)
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# 如果亮度超过非线性亮度调整的上界，就进行非线性亮度调整
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else:
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image_data = self.enhance_brightness_linear(image_data, file_name)
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return image_data
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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_brightness(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: ImgBrightness costs {time.time() - start:.6f} s")
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return sample
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