You've already forked DataMate
72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
# -- encoding: utf-8 --
|
|
|
|
"""
|
|
Description: 图片对比度自适应增强
|
|
Version:
|
|
Create: 2025/01/13
|
|
"""
|
|
|
|
import time
|
|
|
|
from typing import Dict, Any
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from loguru import logger
|
|
|
|
from datamate.common.utils import bytes_transform
|
|
from datamate.core.base_op import Mapper
|
|
|
|
|
|
class ImgContrast(Mapper):
|
|
"""图片对比度自适应增强"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(ImgContrast, self).__init__(*args, **kwargs)
|
|
# 自适应增强参数
|
|
self.clip_limit = 2 # 指定对比度限制阈值, 较大的值会产生更大的对比度增强效(不作为参数传入)。
|
|
self.tile_grid = 16 # 指定图像划分的网格大小,较小的网格大小会导致更局部的均衡化效果(不作为参数传入)。
|
|
self.standard_mean = 100 # 图片增强后的平均对比度(不作为参数传入)。
|
|
self.eps = 0.5 # 小值，计算图像对比度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。
|
|
|
|
@staticmethod
|
|
def _get_contrast(image: np.ndarray):
|
|
"""计算图像所有通道的平均标准差"""
|
|
_, stddev = cv2.meanStdDev(image)
|
|
contrast_std = np.mean(stddev)
|
|
return contrast_std
|
|
|
|
def enhance_contrast(self, image_data: np.ndarray, file_name):
|
|
"""对比度自适应增强方法"""
|
|
|
|
contrast_std = self._get_contrast(image_data)
|
|
contrast_factor = self.standard_mean / (contrast_std + self.eps)
|
|
|
|
# 图片对比度较高，不需要增强对比度
|
|
if contrast_factor <= 1:
|
|
logger.info(f"fileName: {file_name}, method: ImgContrast not need enhancement")
|
|
return image_data
|
|
# 将彩色图像转换为Lab颜色空间
|
|
cv2.cvtColor(image_data, cv2.COLOR_BGR2Lab, dst=image_data)
|
|
|
|
# 使用局部自适应直方图均衡化进行对比度调整。
|
|
clahe = cv2.createCLAHE(clipLimit=self.clip_limit, tileGridSize=(self.tile_grid, self.tile_grid))
|
|
image_data[:, :, 0] = clahe.apply(image_data[:, :, 0])
|
|
|
|
# 将增强后的Lab图像转换回BGR颜色空间
|
|
cv2.cvtColor(image_data, cv2.COLOR_Lab2BGR, dst=image_data)
|
|
return image_data
|
|
|
|
def execute(self, sample: Dict[str, Any]):
|
|
start = time.time()
|
|
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_contrast(img_data, file_name)
|
|
sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
|
|
logger.info(f"fileName: {file_name}, method: ImgContrast costs {time.time() - start:6f} s")
|
|
return sample
|