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）
This commit is contained in:
沃巴蒂
@@ -1,101 +1,101 @@
|
||||
# -- encoding: utf-8 --
|
||||
|
||||
"""
|
||||
Description: 图像亮度增强算子。
|
||||
Create: 2025/01/13
|
||||
"""
|
||||
|
||||
import time
|
||||
|
||||
from typing import Dict, Any
|
||||
|
||||
import numpy as np
|
||||
import cv2
|
||||
from loguru import logger
|
||||
|
||||
from deepdata.common.utils import bytes_transform
|
||||
|
||||
from deepdata.core.base_op import Mapper
|
||||
|
||||
|
||||
class ImgBrightness(Mapper):
|
||||
"""图片亮度自适应增强"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(ImgBrightness, self).__init__(*args, **kwargs)
|
||||
# 自适应增强参数
|
||||
self.factor_threshold = 1.1 # 图片增强因子下限(不作为参数传入)。
|
||||
self.standard_mean = 140 # 图片增强后的平均亮度(不作为参数传入)。
|
||||
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)。
|
||||
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)。
|
||||
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)。
|
||||
|
||||
@staticmethod
|
||||
def _get_grey_mean(src: np.ndarray):
|
||||
gray_image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
|
||||
return np.mean(gray_image)
|
||||
|
||||
@staticmethod
|
||||
def _return_gamma_table(gamma):
|
||||
"""返回gamma校正对应的查找表"""
|
||||
scale = np.power(255, 1 - gamma).astype(np.float64)
|
||||
return np.power(np.arange(256), gamma) * scale
|
||||
|
||||
@staticmethod
|
||||
def _return_linear_table(factor):
|
||||
"""返回线性变换对应的查找表"""
|
||||
linear_table = np.arange(256) * factor
|
||||
return np.clip(linear_table, 0, 255).astype(np.uint8)
|
||||
|
||||
def enhance_brightness_linear(self, image_data: np.ndarray, file_name):
|
||||
average_brightness = self._get_grey_mean(image_data)
|
||||
brightness_factor = self.standard_mean / (average_brightness + self.eps)
|
||||
|
||||
# 图像过亮，不需要增强亮度
|
||||
if brightness_factor <= 1:
|
||||
logger.info(f"fileName: {file_name}, method: ImgBrightness not need enhancement")
|
||||
return image_data
|
||||
|
||||
brightness_factor = max(brightness_factor, self.factor_threshold)
|
||||
linear_table = ImgBrightness._return_linear_table(brightness_factor)
|
||||
cv2.LUT(image_data, linear_table, dst=image_data)
|
||||
return image_data
|
||||
|
||||
def enhance_brightness(self, image_data: np.ndarray, file_name):
|
||||
'''
|
||||
亮度自适应增强方法。
|
||||
|
||||
Args:
|
||||
image_data: nd.array 格式图片
|
||||
gamma: gamma变换因子参数。经验值常用1.5, 已写成了成员变量。
|
||||
Returns:
|
||||
亮度自适应增强后的图片
|
||||
'''
|
||||
# 计算图片平均亮度
|
||||
average_brightness = self._get_grey_mean(image_data)
|
||||
|
||||
# 进行 gamma 校正
|
||||
if average_brightness / 255 <= self.brightness_upper_bound:
|
||||
# 预计算查找表
|
||||
gamma_table = ImgBrightness._return_gamma_table(1 / self.gamma).astype(np.uint8)
|
||||
cv2.LUT(image_data, gamma_table, dst=image_data)
|
||||
|
||||
# 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
|
||||
else:
|
||||
image_data = self.enhance_brightness_linear(image_data, file_name)
|
||||
|
||||
return image_data
|
||||
|
||||
def execute(self, sample: Dict[str, Any]):
|
||||
start = time.time()
|
||||
self.read_file_first(sample)
|
||||
img_bytes = sample[self.data_key]
|
||||
file_name = sample[self.filename_key]
|
||||
file_type = "." + sample[self.filetype_key]
|
||||
if img_bytes:
|
||||
# 进行图片增强
|
||||
img_data = bytes_transform.bytes_to_numpy(img_bytes)
|
||||
img_data = self.enhance_brightness(img_data, file_name)
|
||||
sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
|
||||
logger.info(f"fileName: {file_name}, method: ImgBrightness costs {time.time() - start:6f} s")
|
||||
return sample
|
||||
# -- encoding: utf-8 --␍␍
|
||||
␍␍
|
||||
"""␍␍
|
||||
Description: 图像亮度增强算子。␍␍
|
||||
Create: 2025/01/13␍␍
|
||||
"""␍␍
|
||||
␍␍
|
||||
import time␍␍
|
||||
␍␍
|
||||
from typing import Dict, Any␍␍
|
||||
␍␍
|
||||
import numpy as np␍␍
|
||||
import cv2␍␍
|
||||
from loguru import logger␍␍
|
||||
␍␍
|
||||
from deepdata.common.utils import bytes_transform␍␍
|
||||
␍␍
|
||||
from deepdata.core.base_op import Mapper␍␍
|
||||
␍␍
|
||||
␍␍
|
||||
class ImgBrightness(Mapper):␍␍
|
||||
"""图片亮度自适应增强"""␍␍
|
||||
␍␍
|
||||
def __init__(self, *args, **kwargs):␍␍
|
||||
super(ImgBrightness, self).__init__(*args, **kwargs)␍␍
|
||||
# 自适应增强参数␍␍
|
||||
self.factor_threshold = 1.1 # 图片增强因子下限(不作为参数传入)。␍␍
|
||||
self.standard_mean = 140 # 图片增强后的平均亮度(不作为参数传入)。␍␍
|
||||
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)。␍␍
|
||||
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)。␍␍
|
||||
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)。␍␍
|
||||
␍␍
|
||||
@staticmethod␍␍
|
||||
def _get_grey_mean(src: np.ndarray):␍␍
|
||||
gray_image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)␍␍
|
||||
return np.mean(gray_image)␍␍
|
||||
␍␍
|
||||
@staticmethod␍␍
|
||||
def _return_gamma_table(gamma):␍␍
|
||||
"""返回gamma校正对应的查找表"""␍␍
|
||||
scale = np.power(255, 1 - gamma).astype(np.float64)␍␍
|
||||
return np.power(np.arange(256), gamma) * scale␍␍
|
||||
␍␍
|
||||
@staticmethod␍␍
|
||||
def _return_linear_table(factor):␍␍
|
||||
"""返回线性变换对应的查找表"""␍␍
|
||||
linear_table = np.arange(256) * factor␍␍
|
||||
return np.clip(linear_table, 0, 255).astype(np.uint8)␍␍
|
||||
␍␍
|
||||
def enhance_brightness_linear(self, image_data: np.ndarray, file_name):␍␍
|
||||
average_brightness = self._get_grey_mean(image_data)␍␍
|
||||
brightness_factor = self.standard_mean / (average_brightness + self.eps)␍␍
|
||||
␍␍
|
||||
# 图像过亮，不需要增强亮度␍␍
|
||||
if brightness_factor <= 1:␍␍
|
||||
logger.info(f"fileName: {file_name}, method: ImgBrightness not need enhancement")␍␍
|
||||
return image_data␍␍
|
||||
␍␍
|
||||
brightness_factor = max(brightness_factor, self.factor_threshold)␍␍
|
||||
linear_table = ImgBrightness._return_linear_table(brightness_factor)␍␍
|
||||
cv2.LUT(image_data, linear_table, dst=image_data)␍␍
|
||||
return image_data␍␍
|
||||
␍␍
|
||||
def enhance_brightness(self, image_data: np.ndarray, file_name):␍␍
|
||||
'''␍␍
|
||||
亮度自适应增强方法。␍␍
|
||||
␍␍
|
||||
Args:␍␍
|
||||
image_data: nd.array 格式图片␍␍
|
||||
gamma: gamma变换因子参数。经验值常用1.5, 已写成了成员变量。␍␍
|
||||
Returns:␍␍
|
||||
亮度自适应增强后的图片␍␍
|
||||
'''␍␍
|
||||
# 计算图片平均亮度␍␍
|
||||
average_brightness = self._get_grey_mean(image_data)␍␍
|
||||
␍␍
|
||||
# 进行 gamma 校正␍␍
|
||||
if average_brightness / 255 <= self.brightness_upper_bound:␍␍
|
||||
# 预计算查找表␍␍
|
||||
gamma_table = ImgBrightness._return_gamma_table(1 / self.gamma).astype(np.uint8)␍␍
|
||||
cv2.LUT(image_data, gamma_table, dst=image_data)␍␍
|
||||
␍␍
|
||||
# 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␍␍
|
||||
else:␍␍
|
||||
image_data = self.enhance_brightness_linear(image_data, file_name)␍␍
|
||||
␍␍
|
||||
return image_data␍␍
|
||||
␍␍
|
||||
def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:␍␍
|
||||
start = time.time()␍␍
|
||||
self.read_file_first(sample)␍␍
|
||||
img_bytes = sample[self.data_key]␍␍
|
||||
file_name = sample[self.filename_key]␍␍
|
||||
file_type = "." + sample[self.filetype_key]␍␍
|
||||
if img_bytes:␍␍
|
||||
# 进行图片增强␍␍
|
||||
img_data = bytes_transform.bytes_to_numpy(img_bytes)␍␍
|
||||
img_data = self.enhance_brightness(img_data, file_name)␍␍
|
||||
sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)␍␍
|
||||
logger.info(f"fileName: {file_name}, method: ImgBrightness costs {time.time() - start:.6f} s")␍␍
|
||||
return sample␍␍
|
||||
|
||||
Reference in New Issue
Block a user