176 lines
7.1 KiB
Python
176 lines
7.1 KiB
Python
import cv2
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import numpy as np
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# import rawpy
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# from PIL import Image
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# from tqdm import tqdm
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# import tkinter as tk
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#from tkinter import filedialog
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#from concurrent.futures import ThreadPoolExecutor
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#import argparse
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#import os
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#import torch
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#from depth_anything_v2.dpt import DepthAnythingV2
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#from utils import specify_name_group_blade,get_name
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#parser = argparse.ArgumentParser(description='Depth Anything V2')
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# parser.add_argument('--input-size', type=int, default=518)
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# parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
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# args = parser.parse_args()
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def extraction_win_lamina_mask(raw_image, depth_anything):
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# 前面读的都是RGB图像，深度估计需要BGR，即下面是把RGB→BGR
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raw_image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
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depth = depth_anything.infer_image(raw_image)
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth = depth.astype(np.uint8)
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_, otsu_mask = cv2.threshold(depth, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return otsu_mask
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def check_overexposure(image, lamina_mask,threshold):
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"""
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检查图像中是否有过曝区域
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:param image: 输入图像
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:param threshold: 亮度值的阈值，像素值超过该值则认为是过曝区域
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:return: 一个二值图像，过曝区域为白色，其它区域为黑色
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"""
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# 转换为浮动数据类型并归一化
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image_float = image.astype(np.float32)
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# 获取每个像素的亮度值（可以使用 YUV 或 RGB 亮度）
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gray_image = cv2.cvtColor(image_float, cv2.COLOR_BGR2GRAY)
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# 标记过曝区域，亮度大于阈值的像素为过曝区域
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overexposure_mask = gray_image > threshold
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overexposure_mask = (lamina_mask == 255) & overexposure_mask #只取叶片的亮光区mask
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overexposure_number = np.sum(overexposure_mask * 1)
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overexposed_pixels = gray_image[overexposure_mask]
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# 计算曝光区域的平均亮度
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if overexposed_pixels.size > 0:
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avg_overexposed_value = np.mean(overexposed_pixels)
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else:
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avg_overexposed_value = 0 # 如果没有曝光区域，返回 0
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return overexposure_mask, avg_overexposed_value,overexposure_number
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def check_shawn(image,lamina_mask, threshold):
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"""
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检查图像中是否有阴影区域
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:param image: 输入图像
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:param threshold: 亮度值的阈值，像素值小于该值则认为是过阴影域
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:return: 一个二值图像，过曝区域为白色，其它区域为黑色
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"""
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# 转换为浮动数据类型并归一化
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image_float = image.astype(np.float32)
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# 获取每个像素的亮度值（可以使用 YUV 或 RGB 亮度）
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gray_image = cv2.cvtColor(image_float, cv2.COLOR_BGR2GRAY)
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# 标记阴影区域，亮度小于阈值的像素为阴影区域
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shawn_mask = gray_image < threshold
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shawn_mask = (lamina_mask == 255) & shawn_mask
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shawn_number = np.sum(shawn_mask * 1)
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shawn_pixels = gray_image[shawn_mask]
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# 计算阴影区域的平均亮度
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if shawn_pixels.size > 0:
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avg_shawn_value = np.mean(shawn_pixels)
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else:
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avg_shawn_value = 0
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return shawn_mask, avg_shawn_value,shawn_number
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def smooth_overexposed_regions(image, overexposure_mask, kernel_size=(15, 15)):
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"""
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对过曝区域进行平滑处理，修复与周围区域的过渡
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:param image: 输入图像
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:param overexposure_mask: 过曝区域的掩码
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:param kernel_size: 高斯核大小
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:return: 平滑过曝区域后的图像
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"""
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# 使用高斯模糊平滑过曝区域和周围区域
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smoothed_image = cv2.GaussianBlur(image, kernel_size, 0)
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# 将平滑后的图像和原始图像合成，修复过曝区域
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fixed_image = np.where(overexposure_mask[..., None] == 255, image,smoothed_image)
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return fixed_image
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def bilateral_filter_adjustment(image, high_light_mask, d=15, sigma_color=75, sigma_space=75):
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"""
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使用双边滤波器平滑高光区域与周围区域的过渡
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:param image: 输入图像
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:param high_light_mask: 高光区域的掩码
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:param d: 邻域的直径
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:param sigma_color: 颜色空间的标准差
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:param sigma_space: 坐标空间的标准差
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:return: 平滑后的图像
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"""
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# 对整个图像应用双边滤波
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filtered_image = cv2.bilateralFilter(image, d, sigma_color, sigma_space)
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# 合成平滑后的图像和原图，保留非高光部分
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final_image = np.where(high_light_mask[..., None] == 0, image, filtered_image)
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return final_image
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def adjust_highlights_shadows(image, threshold_shawn,threshold, depth_anything):
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lamina_mask = extraction_win_lamina_mask(image, depth_anything)
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shawn_mask,avg_shawn_value,shawn_number = check_shawn(image,lamina_mask,threshold_shawn)
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# 调整亮度，gamma < 1 时变亮，gamma > 1 时变暗
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gamma = 1 - (threshold_shawn-avg_shawn_value)/255.0
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# print('阴影区调整的gama: '+str(gamma))#+str('\n'))
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lookup_table = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype('uint8')
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# 应用 gamma 校正
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if shawn_number !=0:
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image[shawn_mask == True] = cv2.LUT(image[shawn_mask == True], lookup_table)
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gamma_corrected_image = image
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else:
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gamma_corrected_image = image
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#gamma_corrected_image = cv2.LUT(image, lookup_table)
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#寻找过爆区域
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overexposure_mask,avg_overexposed_value,overexposure_number = check_overexposure(gamma_corrected_image,lamina_mask,threshold)
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reduction_factor = 1-(avg_overexposed_value-threshold) / 255
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#reduction_factor = (avg_overexposed_value/255)*scale
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# print("降低曝光区比例：" + str(reduction_factor))
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# 调整亮度，gamma < 1 时变亮（越小越亮），gamma > 1 时变暗(越大越暗)，
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print((1+reduction_factor))
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lookup_table = np.array([((i / 255.0) ** (1+reduction_factor)) * 255 for i in range(256)]).astype('uint8')
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# 应用 gamma 校正
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if overexposure_number !=0:
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gamma_corrected_image[overexposure_mask == True] = cv2.LUT(gamma_corrected_image[overexposure_mask == True], lookup_table)
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#gamma_corrected_image[overexposure_mask == True] = np.clip(gamma_corrected_image[overexposure_mask == True] * reduction_factor, 0, 255)
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#smoothed_image = smooth_overexposed_regions(gamma_corrected_image, overexposure_mask)
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#smoothed_image = bilateral_filter_adjustment(gamma_corrected_image, overexposure_mask, d=15, sigma_color=75, sigma_space=75)
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return gamma_corrected_image
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# path=r'/home/dtyx/下载/1_涂层损伤_叶尖3m_2m_一般_紧急_一般_尽快打磨维修.jpg'
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# from time import time
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# if __name__ == "__main__":
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# # input_dir = input("请输入处理图片路径: ")
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# # output_dir = input("请输入保存图片路径: ")
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# time_start = time()
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# imgpath = path
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# img = cv2.imdecode(np.fromfile(imgpath, dtype=np.uint8), cv2.IMREAD_COLOR)
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# cv2.imshow('img',cv2.resize(img,(800,600)))
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# mask=adjust_highlights_shadows(img,180,253)
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# mask=cv2.resize(mask,(800,600))
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# time_end = time()
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# print("Time used:", time_end - time_start)
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# cv2.imshow('mask',mask)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# # gamma = float(input("请输入 gamma 值: ")) |