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70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
# -- encoding: utf-8 --
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"""
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Description: 图片锐度自适应增强
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Version:
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Create: 2025/01/13
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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 ImgSharpness(Mapper):
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"""图片锐度自适应增强"""
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def __init__(self, *args, **kwargs):
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super(ImgSharpness, self).__init__(*args, **kwargs)
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# 自适应增强参数
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self.factor_threshold = 1.1 # 图片增强因子下限(不作为参数传入)。
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self.standard_mean = 100 # 图片增强后的平均锐度(不作为参数传入)。
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self.kernel = self._init_kernel()
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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)。
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@classmethod
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def _init_kernel(cls):
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kernel = np.array([[1, 1, 1],
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[1, 5, 1],
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[1, 1, 1]])
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# 对卷积核进行归一化
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kernel = kernel / np.sum(kernel)
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return kernel
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def enhance_sharpness(self, image_data: np.ndarray, file_name):
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"""锐度自适应增强方法"""
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# 打开图像并转换为灰度图像
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image_gray = cv2.cvtColor(image_data, cv2.COLOR_BGR2GRAY)
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sharpness = np.abs(cv2.Laplacian(image_gray, cv2.CV_8U)).mean()
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sharpness_factor = self.standard_mean / (sharpness + self.eps)
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# 图片锐度较高，不需要增强锐度
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if sharpness_factor <= 1:
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logger.info(f"fileName: {file_name}, method: ImgSharpness not need enhancement")
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return image_data
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filtered_img = cv2.filter2D(image_data, -1, self.kernel)
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cv2.addWeighted(image_data, sharpness_factor, filtered_img, 1.0 - sharpness_factor, 0, 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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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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# 进行图片增强
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img_data = bytes_transform.bytes_to_numpy(img_bytes)
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img_data = self.enhance_sharpness(img_data, file_name)
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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: ImgSharpness costs {time.time() - start:6f} s")
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return sample
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