init datamate

runtime/ops/mapper/img_shadow_remove/process.py

@@ -0,0 +1,72 @@
+# -- encoding: utf-8 --
+
+"""
+Description: 图片去阴影插件
+Create: 2025/01/16
+"""
+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 ImgShadowRemove(Mapper):
+    """图片阴影去除"""
+
+    def __init__(self, *args, **kwargs):
+        super(ImgShadowRemove, self).__init__(*args, **kwargs)
+        self.iter_nums = 9  # 闭运算循环次数(不作为参数传入)。
+        self.k_size = 3  # kernel size大小。
+        self.clip_limit = 2  # 对比度限制阈值, 数值越大，效果越强。
+        self.tile_grid = 8  # 图像划分的网格大小, 数值越小，局部效果越明显。
+
+    def shadow_removed(self, image_data: np.ndarray):
+        '''
+        阴影去除。
+
+        Args:
+            image_data: nd.array 格式图片
+        Returns:
+            阴影去除后的图片
+        '''
+        # 设置kernel大小，进行闭运算
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (self.k_size, self.k_size))
+        closing = cv2.morphologyEx(image_data, cv2.MORPH_CLOSE, kernel, iterations=self.iter_nums)
+
+        # 进行~（closing - original）操作
+        cv2.bitwise_not(closing - image_data, dst=closing)
+        cv2.cvtColor(closing, cv2.COLOR_BGR2Lab, dst=closing)
+
+        # 获取处理后图像的亮度通道
+        img_l = cv2.split(closing)[0]
+        del closing
+
+        # 对img_l进行调节后，替换原图的亮度通道
+        cv2.cvtColor(image_data, cv2.COLOR_BGR2Lab, dst=image_data)
+        # 创建 CLAHE 对象
+        clahe = cv2.createCLAHE(clipLimit=self.clip_limit, tileGridSize=(self.tile_grid, self.tile_grid))
+        # 进行 CLAHE 处理
+        image_data[:, :, 0] = clahe.apply(img_l)
+        del img_l
+
+        cv2.cvtColor(image_data, cv2.COLOR_Lab2BGR, 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:
+            # 进行阴影去除
+            img_data = bytes_transform.bytes_to_numpy(img_bytes)
+            img_data = self.shadow_removed(img_data)
+            sample[self.data_key] = bytes_transform.numpy_to_bytes(img_data, file_type)
+        logger.info(f"fileName: {file_name}, method: ImageShadowRemove costs {time.time() - start:6f} s")
+        return sample