init datamate

runtime/ops/mapper/img_denoise/__init__.py

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+# -*- coding: utf-8 -*-
+
+from datamate.core.base_op import OPERATORS
+
+OPERATORS.register_module(module_name='ImgDenoise',
+                          module_path="ops.mapper.img_denoise.process")

runtime/ops/mapper/img_denoise/metadata.yml

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+name: '图片噪点去除'
+name_en: 'Image Noise Removal'
+description: '去除图片中的噪点，主要适用于自然场景。'
+description_en: 'Removes noises from images, which is mainly applicable to natural
+  scenery image scenarios.'
+language: 'python'
+vendor: 'huawei'
+raw_id: 'ImgDenoise'
+version: '1.0.0'
+types:
+  - 'cleanse'
+modal: 'image'
+effect:
+  before: ''
+  after: ''
+inputs: 'image'
+outputs: 'image'

runtime/ops/mapper/img_denoise/process.py

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