You've already forked DataMate
d59c167da4
* feature: 将抽取动作移到每一个算子中 * feature: 落盘算子改为默认执行 * feature: 优化前端展示 * feature: 使用pyproject管理依赖
83 lines
3.5 KiB
Python
83 lines
3.5 KiB
Python
# -- encoding: utf-8 --
|
|
|
|
"""
|
|
Description: 图片饱和度自适应增强
|
|
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 ImgSaturation(Mapper):
|
|
"""图片饱和度自适应增强"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(ImgSaturation, 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 = 130 # 图片增强后的平均饱和度(不作为参数传入)。
|
|
self.eps = 1 # 极小值，计算图像饱和度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。
|
|
self.zeros_ratio_threshold = 0.1 # saturation通道 零值占比率，防止对近似灰度图的图像进行处理。
|
|
self.red_channel_threshold = 140 # 图片红色通道阈值，用于抑制饱和度增强因子
|
|
|
|
def enhance_saturation(self, image_data: np.ndarray, file_name):
|
|
"""饱和度自适应增强方法"""
|
|
# 打开图像并转换为HSV颜色空间
|
|
image_hsv = cv2.cvtColor(image_data, cv2.COLOR_BGR2HSV)
|
|
s_channel = image_hsv[:, :, 1].copy()
|
|
del image_hsv
|
|
|
|
# 提取饱和度通道
|
|
# 正常的RGB图片，零值占比率比应当小于0.1, 如果高于0.1，可以认为这张图片近似于灰度图
|
|
zero_s_ratio = np.count_nonzero(s_channel == 0) / s_channel.size
|
|
if zero_s_ratio <= self.zeros_ratio_threshold:
|
|
saturation_channel = s_channel
|
|
# 灰度图片转成的RGB图片，转为HSV后，S通道值全为0
|
|
else:
|
|
return image_data
|
|
|
|
# 计算饱和度的统计信息
|
|
saturation_mean = np.mean(saturation_channel)
|
|
saturation_factor = self.standard_mean / (saturation_mean + self.eps)
|
|
|
|
# 图片饱和度较高，不需要增强饱和度
|
|
if saturation_factor <= 1:
|
|
logger.info(f"fileName: ｛file_name｝, method: ImgSaturation not need enhancement")
|
|
return image_data
|
|
|
|
# 计算图片红色通道均值， 如果过大，需要限制saturation factor大小，否则图片会泛红, 产生色彩畸变。
|
|
red_channel_mean = np.mean(image_data[:, :, 2])
|
|
if red_channel_mean >= self.red_channel_threshold:
|
|
saturation_factor = min(saturation_factor, 1.5)
|
|
else:
|
|
saturation_factor = max(saturation_factor, self.factor_threshold)
|
|
|
|
degrade_image = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)
|
|
degrade_image = cv2.cvtColor(degrade_image, cv2.COLOR_GRAY2BGR)
|
|
cv2.addWeighted(image_data, saturation_factor, degrade_image, 1 - saturation_factor, 0, dst=image_data)
|
|
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_saturation(img_data, file_name)
|
|
sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
|
|
logger.info(f"fileName: ｛file_name｝, method: ImgSaturation costs {time.time() - start:6f} s")
|
|
return sample
|