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