init datamate

runtime/ops/mapper/img_enhanced_sharpness/process.py

@@ -0,0 +1,69 @@
+# -- encoding: utf-8 --
+
+"""
+Description: 图片锐度自适应增强
+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 ImgSharpness(Mapper):
+    """图片锐度自适应增强"""
+
+    def __init__(self, *args, **kwargs):
+        super(ImgSharpness, 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 = 100  # 图片增强后的平均锐度(不作为参数传入)。
+        self.kernel = self._init_kernel()
+        self.eps = 1  # 小值，计算图像锐度增强因子的时候，防止全黑图片导致的除零错(不作为参数传入)。
+
+    @classmethod
+    def _init_kernel(cls):
+        kernel = np.array([[1, 1, 1],
+                           [1, 5, 1],
+                           [1, 1, 1]])
+        # 对卷积核进行归一化
+        kernel = kernel / np.sum(kernel)
+        return kernel
+
+    def enhance_sharpness(self, image_data: np.ndarray, file_name):
+        """锐度自适应增强方法"""
+
+        # 打开图像并转换为灰度图像
+        image_gray = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)
+        sharpness = np.abs(cv2.Laplacian(image_gray, cv2.CV_8U)).mean()
+        sharpness_factor = self.standard_mean / (sharpness + self.eps)
+
+        # 图片锐度较高，不需要增强锐度
+        if sharpness_factor <= 1:
+            logger.info(f"fileName: {file_name}, method: ImgSharpness not need enhancement")
+            return image_data
+
+        filtered_img = cv2.filter2D(image_data, -1, self.kernel)
+        cv2.addWeighted(image_data, sharpness_factor, filtered_img, 1.0 - sharpness_factor, 0, 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_sharpness(img_data, file_name)
+            sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
+        logger.info(f"fileName: {file_name}, method: ImgSharpness costs {time.time() - start:6f} s")
+        return sample