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82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
# -- encoding: utf-8 --
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"""
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Description: 图片饱和度自适应增强
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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 datamate.common.utils import bytes_transform
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from datamate.core.base_op import Mapper
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class ImgSaturation(Mapper):
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"""图片饱和度自适应增强"""
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def __init__(self, *args, **kwargs):
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super(ImgSaturation, 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 = 130 # 图片增强后的平均饱和度(不作为参数传入)。
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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)。
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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。
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self.red_channel_threshold = 140 # 图片红色通道阈值，用于抑制饱和度增强因子
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def enhance_saturation(self, image_data: np.ndarray, file_name):
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"""饱和度自适应增强方法"""
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# 打开图像并转换为HSV颜色空间
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image_hsv = cv2.cvtColor(image_data, cv2.COLOR_BGR2HSV)
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s_channel = image_hsv[:, :, 1].copy()
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del image_hsv
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# 提取饱和度通道
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# 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
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zero_s_ratio = np.count_nonzero(s_channel == 0) / s_channel.size
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if zero_s_ratio <= self.zeros_ratio_threshold:
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saturation_channel = s_channel
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# 灰度图片转成的RGB图片，转为HSV后，S通道值全为0
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else:
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return image_data
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# 计算饱和度的统计信息
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saturation_mean = np.mean(saturation_channel)
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saturation_factor = self.standard_mean / (saturation_mean + self.eps)
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# 图片饱和度较高，不需要增强饱和度
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if saturation_factor <= 1:
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logger.info(f"fileName: ｛file_name｝, method: ImgSaturation not need enhancement")
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return image_data
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# 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。
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red_channel_mean = np.mean(image_data[:, :, 2])
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if red_channel_mean >= self.red_channel_threshold:
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saturation_factor = min(saturation_factor, 1.5)
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else:
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saturation_factor = max(saturation_factor, self.factor_threshold)
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degrade_image = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)
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degrade_image = cv2.cvtColor(degrade_image, cv2.COLOR_GRAY2BGR)
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cv2.addWeighted(image_data, saturation_factor, degrade_image, 1 - saturation_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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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_saturation(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: ImgSaturation costs {time.time() - start:6f} s")
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return sample
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