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* 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
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
# -- encoding: utf-8 --
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"""
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Description:
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Create: 2025/01/17
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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 ImgDenoise(Mapper):
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def __init__(self, *args, **kwargs):
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super(ImgDenoise, self).__init__(*args, **kwargs)
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self._denoise_threshold = kwargs.get("denoise_threshold", 8)
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@staticmethod
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def _denoise_image(data: object):
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"""降噪处理"""
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return cv2.medianBlur(data, 3)
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def execute(self, sample: 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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data = bytes_transform.bytes_to_numpy(img_bytes)
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denoise_images = self._denoise_images_filter(data, file_name)
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sample[self.data_key] = bytes_transform.numpy_to_bytes(denoise_images, file_type)
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logger.info(f"fileName: {file_name}, method: ImgDenoise costs {time.time() - start:6f} s")
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return sample
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def _denoise_images_filter(self, ori_img, file_name):
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# 获取原始图片的去噪图片
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clean_data = self._denoise_image(ori_img)
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# 为方便与其他图片比较可以将图片resize到同一个大小
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ori = cv2.resize(ori_img, (112, 112))
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dst = cv2.resize(clean_data, (112, 112))
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# 计算未降噪图片的灰度值的集合
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signal = np.sum(ori ** 2)
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# 计算未降噪图片的灰度值与去噪图片灰度值的差值的集合
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noise = np.sum((ori - dst) ** 2)
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# 根据未去噪图片和差值计算snr (图片信噪比)
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snr = 10 * np.log10(signal / noise)
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# 对于小于阈值的图片，进行降噪处理
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if snr < self._denoise_threshold:
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logger.info(f"The image denoise is {self._denoise_threshold}, "
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f"which exceeds the threshold of {snr}. {file_name} is filtered out.")
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return clean_data
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return ori_img
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