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@@ -3,7 +3,7 @@ import os, oss2, time, redis, requests, shutil, sys, subprocess, json, platform
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from PIL import Image, ImageDraw, ImageFont
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from retrying import retry
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import atexit,platform
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import white_purification
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import white_purification_v1
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if platform.system() == 'Windows':
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sys.path.append('e:\\libs\\')
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import common
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@@ -127,7 +127,9 @@ def team_check(r):
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#对文件进行白色提纯处理
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imagePath = os.path.join(workdir, 'print', pid,pid+"Tex1.jpg")
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print("开始处理白色提纯")
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white_purification.white_purification_utils(imagePath)
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#white_purification_v1.white_purification_utils(imagePath)
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os.system(f'python d:\\make2\\apps\white_purification_v1.py.py -i {imagePath}')
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print("贴图文件白色提纯完成",imagePath)
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#提纯完重新上传提纯图片
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ossImagePath = os.path.join("objs/print", pid,ossPath,"texture","process_"+pid+"Tex1.jpg")
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@@ -0,0 +1,242 @@
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import cv2, numpy as np, matplotlib.pyplot as plt, os, shutil, argparse, random, time, math
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from tqdm import tqdm
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def ps_color_scale_adjustment(image, shadow=0, highlight=255, midtones=1):
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'''
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模拟 PS 的色阶调整； 0 <= Shadow < Highlight <= 255
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:param image: 传入的图片
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:param shadow: 黑场(0-Highlight)
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:param highlight: 白场(Shadow-255)
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:param midtones: 灰场(9.99-0.01)
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:return: 图片
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'''
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if highlight >255:
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highlight = 255
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if shadow < 0:
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shadow = 0
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if shadow >= highlight:
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shadow = highlight - 2
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if midtones > 9.99:
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midtones = 9.99
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if midtones < 0.01:
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midtones = 0.01
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image = np.array(image, dtype=np.float16)
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# 计算白场 黑场离差
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Diff = highlight - shadow
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image = image - shadow
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image[image < 0] = 0
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image = (image / Diff) ** (1 / midtones) * 255
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image[image > 255] = 255
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image = np.array(image, dtype=np.uint8)
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return image
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# def show_histogram(image, image_id, save_hist_dir, min_threshold, max_threshold):
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# '''
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# 画出直方图展示
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# :param image: 导入图片
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# :param image_id: 图片id编号
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# :param save_hist_dir: 保存路径
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# :param min_threshold: 最小阈值
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# :param max_threshold: 最大阈值
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# :return: 原图image，和裁剪原图直方图高低阈值后的图片image_change
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# '''
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# plt.rcParams['font.family'] = 'SimHei'
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# plt.rcParams['axes.unicode_minus'] = False
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# plt.hist(image.ravel(), 254, range=(2, 256), density=False)
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# plt.hist(image.ravel(), 96, range=(2, 50), density=False) # 放大 range(0, 50),bins值最好是range的两倍，显得更稀疏，便于对比
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# plt.hist(image.ravel(), 110, range=(200, 255), density=False) # 放大 range(225, 255)
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# plt.annotate('thresh1=' + str(min_threshold), # 文本内容
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# xy=(min_threshold, 0), # 箭头指向位置 # 阈值设定值！
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# xytext=(min_threshold, 500000), # 文本位置 # 阈值设定值！
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# arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2)) # 箭头
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# plt.annotate('thresh2=' + str(max_threshold), # 文本内容
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# xy=(max_threshold, 0), # 箭头指向位置 # 阈值设定值！
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# xytext=(max_threshold, 500000), # 文本位置 # 阈值设定值！
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# arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2)) # 箭头
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# # 在y轴上绘制一条直线
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# # plt.axhline(y=10000, color='r', linestyle='--', linewidth=0.5)
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# plt.title(str(image_id))
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# # plt.show()
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# # 保存直方图
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# save_hist_name = os.path.join(save_hist_dir, f'{image_id}_{min_threshold}&{max_threshold}.jpg')
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# plt.savefig(save_hist_name)
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# # 清空画布， 防止重叠展示
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# plt.clf()
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# def low_find_histogram_range(image, target_frequency):
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# '''
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# 循环查找在 target_frequency (y)频次限制下的直方图区间值(x)
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# :param image: 导入图片
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# :param target_frequency: 直方图 y 频次限制条件
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# :return: 直方图区间 x，和 该区间频次 y
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# '''
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# # 计算灰度直方图
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# hist, bins = np.histogram(image, bins=256, range=[0, 256])
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# # 初始化区间和频次
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# interval = 2
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# frequency = hist[255]
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# while frequency < target_frequency:
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# # 更新区间和频次
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# interval += 1
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# # 检查直方图的频次是否为None，如果频次是None，则将其设为0，这样可以避免将None和int进行比较报错。
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# frequency = hist[interval] if hist[interval] is not None else 0
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# frequency += hist[interval] if hist[interval] is not None else 0
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# # 如果频次接近10000则停止循环
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# if target_frequency - 2000 <= frequency <= target_frequency + 1000:
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# break
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#
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# return interval, frequency
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# def high_find_histogram_range(image, target_frequency):
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# '''
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# 循环查找在 target_frequency (y)频次限制下的直方图区间值(x)
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# :param image: 导入图片
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# :param target_frequency: 直方图 y 频次限制条件
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# :return: 直方图区间 x，和 该区间频次 y
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# '''
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# # 计算灰度直方图
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# hist, bins = np.histogram(image, bins=256, range=[0, 256])
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# # 初始化区间和频次
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# interval = 255
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# frequency = hist[255]
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# while frequency < target_frequency:
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# # 更新区间和频次
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# interval -= 1
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# # 检查直方图的频次是否为None，如果频次是None，则将其设为0，这样可以避免将None和int进行比较报错。
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# frequency = hist[interval] if hist[interval] is not None else 0
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# frequency += hist[interval] if hist[interval] is not None else 0
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# # 如果频次接近10000则停止循环
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# if target_frequency - 2000 <= frequency <= target_frequency + 2000:
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# break
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#
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# return interval, frequency
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def find_last_x(image, slope_threshold = 1000):
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x = []
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y = []
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hist, bins = np.histogram(image, bins=256, range=[0, 256])
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#找到50以内的最高峰
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max_y = 0
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max_i = 5
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for i in range(5, 25):
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if hist[i] > max_y:
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max_y = hist[i]
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max_i = i
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print(f'50以内最高峰值y：{max_y}，最高峰位置x：{max_i}')
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for i in range(2, max_i):
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x.append(i)
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y.append(hist[i])
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slopes = [abs(y[i + 1] - y[i]) for i in range(len(x) - 1)]
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current_interval = []
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max_interval = []
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max_x = {}
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for i, slope in enumerate(slopes):
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current_interval.append(slope)
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if slope >= slope_threshold:
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if len(current_interval) > len(max_interval):
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max_interval = current_interval.copy()
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max_x[x[i]] = slope
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current_interval = []
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print(max_x)
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last_x = list(max_x)[-1]
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last_y = max_x[last_x]
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return last_x, last_y
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def find_last_high(image, slope_threshold = 2500):
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x = []
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y = []
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hist, bins = np.histogram(image, bins=255, range=[2, 255])
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#找到200以上的最高峰
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max_y = 0
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max_i = 254
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for i in range(220, 255):
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if hist[i] > max_y:
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max_y = hist[i]
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max_i = i
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print(f'200以上的最高峰值y：{max_y}，最高峰位置x：{max_i}')
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for i in range(max_i, 255):
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x.append(i)
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y.append(hist[i])
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slopes = [abs(y[i + 1] - y[i]) for i in range(len(x) - 1)]
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current_interval = []
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max_interval = []
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max_x = {}
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find = False
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for i in range(len(slopes) - 1, -1, -1):
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slope = slopes[i]
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current_interval.append(slope)
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if slope >= slope_threshold:
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find = True
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if len(current_interval) > len(max_interval):
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max_interval = current_interval.copy()
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max_x[x[i]] = slope
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current_interval = []
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#如果没有找到200以上很平，而且高度小于5000，就按220位置削平
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if not find and hist[220] < 5000:
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max_x[220] = hist[220]
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print(max_x)
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if len(max_x) > 0:
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last_x = list(max_x)[0]
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last_y = max_x[last_x]
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if last_x < 220:
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last_x = 220
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# last_y = max_x[last_x]
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else:
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print(f'找不到200以上曲线较平的区间，使用254作为最高峰')
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last_x = 254
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last_y = hist[254]
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return last_x, last_y
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def remove_gray_and_sharpening(jpg_path,save_hist_dir):
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input_image = cv2.imread(jpg_path)
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filename = os.path.basename(jpg_path)
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# low_x_thresh, low_y_frequency = low_find_histogram_range(input_image, low_y_limit)
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low_x_thresh, low_y_frequency = find_last_x(input_image)
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# high_x_thresh, high_y_frequency = high_find_histogram_range(input_image, high_y_limit)
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high_x_thresh, high_y_frequency = find_last_high(input_image)
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# print(f"{low_x_thresh} 区间, {low_y_frequency} 频次")
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# print(f"{high_x_thresh} 区间, {high_y_frequency} 频次")
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output_filename = os.path.join(save_hist_dir, filename).replace('.jpg', f'_{low_x_thresh}_{high_x_thresh}.jpg')
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high_output_image = ps_color_scale_adjustment(input_image, shadow=low_x_thresh, highlight=high_x_thresh, midtones=1)
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# high_output_image = ps_color_scale_adjustment(low_ouput_image, shadow=0, highlight=high_x_thresh, midtones=1)
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# # 人体贴图和黑色背景交界处不进行锐化
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# gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
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# _, thresh = cv2.threshold(gray, 2, 255, cv2.THRESH_BINARY)
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
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# gradient = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel)
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# roi_gradient = cv2.bitwise_and(high_output_image, high_output_image, mask=gradient)
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# # 锐化滤波器
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# # sharpened_image = sharpening_filter(high_output_image)
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# sharpened_image = reduce_sharpness(high_output_image, factor=4)
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# # 将原图边界替换锐化后的图片边界
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# sharpened_image[gradient != 0] = roi_gradient[gradient != 0]
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# 直方图标记并保存
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# show_histogram(input_image, img_id, low_x_thresh, high_x_thresh)
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# cv2.imwrite(jpg_path, high_output_image, [cv2.IMWRITE_JPEG_QUALITY, 95]) # 保存图片的质量是原图的 95%
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cv2.imwrite(output_filename, high_output_image, [cv2.IMWRITE_JPEG_QUALITY, 95]) # 保存图片的质量是原图的 95%
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return output_filename
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if __name__ == '__main__':
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arg = argparse.ArgumentParser()
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arg.add_argument('--jpg_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/input')
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arg.add_argument('--save_hist_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/output')
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args = arg.parse_args()
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for img_id in tqdm(os.listdir(args.jpg_dir)):
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jpg_path = os.path.join(args.jpg_dir, img_id)
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remove_gray_and_sharpening(jpg_path,args.save_hist_dir)
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@@ -0,0 +1,192 @@
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import os.path
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import numpy as np
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import cv2
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import argparse
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def adjust_levels_image(img):
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""""""
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img = img.astype(np.float32)
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img = 255 * ((img - 20) / (241 - 20))
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img[img < 0] = 0
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img[img > 255] = 255
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img = 255 * np.power(img / 255.0, 1.0 / 1.34)
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img = (img / 255) * (255- 0) + 0
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img[img < 0] = 0
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img[img > 255] = 255
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img = img.astype(np.uint8)
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return img
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def photoshop_style_feather(image, mask, radius=150):
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"""
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"""
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if mask.dtype != np.uint8:
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mask = (mask * 255).astype(np.uint8)
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if len(mask.shape) > 2:
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mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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kernel_size = max(3, int(2 * np.ceil(2 * radius) + 1))
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expanded_size = (mask.shape[0] + 2 * radius, mask.shape[1] + 2 * radius)
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expanded_mask = np.zeros(expanded_size, dtype=np.uint8)
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center_y, center_x = radius, radius
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expanded_mask[center_y:center_y + mask.shape[0],
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center_x:center_x + mask.shape[1]] = mask
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blurred_expanded_mask = cv2.GaussianBlur(
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expanded_mask,
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(kernel_size, kernel_size),
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sigmaX=radius,
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sigmaY=radius,
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borderType=cv2.BORDER_REFLECT_101
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)
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feathered_mask = blurred_expanded_mask[center_y:center_y + mask.shape[0],
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center_x:center_x + mask.shape[1]]
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feathered_mask = feathered_mask.astype(np.float32) / 255.0
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feathered_mask = np.power(feathered_mask, 1.1) # 轻微增强对比度
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feathered_mask = np.clip(feathered_mask * 255, 0, 255).astype(np.uint8)
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return feathered_mask
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def calculate_luminance(img):
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""""""
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if len(img.shape) == 3:
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ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
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return ycrcb[:, :, 0].astype(np.float32) / 255.0
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else:
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return img.astype(np.float32) / 255.0
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def photoshop_feather_blend(adjusted_img, original_img, mask, feather_radius=150, brightness_factor=0.95):
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"""
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"""
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if mask.dtype != np.uint8:
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mask = (mask * 255).astype(np.uint8)
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if len(mask.shape) > 2:
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mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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feathered_mask = photoshop_style_feather(original_img, mask, feather_radius)
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feathered_mask_float = feathered_mask.astype(np.float32) / 255.0
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if len(original_img.shape) == 3 and len(feathered_mask_float.shape) == 2:
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feathered_mask_float = np.stack([feathered_mask_float] * 3, axis=-1)
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def to_linear(img):
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img_linear = img.astype(np.float32) / 255.0
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return np.where(img_linear <= 0.04045,
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img_linear / 12.92,
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((img_linear + 0.055) / 1.055) ** 2.4)
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def to_srgb(img_linear):
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return np.where(img_linear <= 0.0031308,
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img_linear * 12.92,
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1.055 * (img_linear ** (1 / 2.4)) - 0.055)
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adjusted_linear = to_linear(adjusted_img)
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original_linear = to_linear(original_img)
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luminance_adjustment = np.mean(original_linear, axis=-1, keepdims=True) * (1.0 - brightness_factor)
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adjusted_linear_corrected = adjusted_linear - luminance_adjustment
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blended_linear = (adjusted_linear_corrected * feathered_mask_float +
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original_linear * (1 - feathered_mask_float))
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blended_srgb = to_srgb(blended_linear)
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blended_img = np.clip(blended_srgb * 255, 0, 255).astype(np.uint8)
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return blended_img
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def rgb2lab_image(rgb_img):
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""""""
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rgb = rgb_img.astype(np.float32) / 255.0
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mask = rgb > 0.04045
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rgb = np.where(mask,
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np.power((rgb + 0.055) / 1.055, 2.4),
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rgb / 12.92)
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XYZ = np.dot(rgb, [
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[0.436052025, 0.222491598, 0.013929122],
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[0.385081593, 0.716886060, 0.097097002],
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[0.143087414, 0.060621486, 0.714185470]
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])
|
||||
|
||||
XYZ *= np.array([100.0, 100.0, 100.0]) / [96.4221, 100.0, 82.5211]
|
||||
|
||||
epsilon = 0.008856
|
||||
kappa = 903.3
|
||||
|
||||
XYZ_norm = np.where(XYZ > epsilon,
|
||||
np.power(XYZ, 1 / 3),
|
||||
(kappa * XYZ + 16) / 116)
|
||||
|
||||
L = 116 * XYZ_norm[..., 1] - 16
|
||||
a = 500 * (XYZ_norm[..., 0] - XYZ_norm[..., 1])
|
||||
b = 200 * (XYZ_norm[..., 1] - XYZ_norm[..., 2])
|
||||
|
||||
return np.stack([L, a, b], axis=-1)
|
||||
|
||||
|
||||
def photoshop_lab_color_range_optimized(bgr_img, target_lab, tolerance=59, anti_alias=True):
|
||||
""""""
|
||||
|
||||
rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
|
||||
lab_img = rgb2lab_image(rgb_img)
|
||||
|
||||
L, a, b = lab_img[:, :, 0], lab_img[:, :, 1], lab_img[:, :, 2]
|
||||
target_L, target_a, target_b = target_lab
|
||||
|
||||
diff_L = np.abs(L - target_L)
|
||||
diff_a = np.abs(a - target_a)
|
||||
diff_b = np.abs(b - target_b)
|
||||
|
||||
dark_boost = np.ones_like(L)
|
||||
dark_mask = L <40
|
||||
dark_boost[dark_mask] = 1.2
|
||||
|
||||
weighted_diff = np.sqrt(
|
||||
0.25 * (diff_L / 100) ** 2 +
|
||||
0.75 * ((diff_a + diff_b) / 255) ** 2
|
||||
) * 100
|
||||
|
||||
weighted_diff = weighted_diff / dark_boost
|
||||
|
||||
threshold = 1.6 * (100 - tolerance) / 100 * 23
|
||||
|
||||
normalized_diff = weighted_diff / threshold
|
||||
mask = 0.5 * (np.tanh(4 * (1 - normalized_diff)) + 1)
|
||||
|
||||
if anti_alias:
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 0)
|
||||
|
||||
return mask
|
||||
|
||||
def photoshop_actions_emulation(input_path, output_path):
|
||||
""""""
|
||||
original_img = cv2.imread(input_path)
|
||||
target_lab = np.array([47.89, 20.31, 20.6], dtype=np.float32)
|
||||
tol= 81
|
||||
mask = photoshop_lab_color_range_optimized(original_img, target_lab, tol)
|
||||
mask_uint8 = (mask * 255).astype(np.uint8)
|
||||
adjusted_img = adjust_levels_image(original_img)
|
||||
result = photoshop_feather_blend(adjusted_img, original_img, mask_uint8,
|
||||
feather_radius=150, brightness_factor=0.90)
|
||||
cv2.imwrite(output_path, result)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
arg = argparse.ArgumentParser()
|
||||
arg.add_argument('--image_name', type=str, default='274351Tex1_adjusted060518_2_221.jpg')
|
||||
arg.add_argument('--image_name_new', type=str, default='274351Tex1_adjusted060518_2_221_999999.jpg')
|
||||
arg.add_argument('--in_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/output/')
|
||||
arg.add_argument('--out_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/shadow_up/')
|
||||
args = arg.parse_args()
|
||||
os.makedirs(args.out_dir,exist_ok=True)
|
||||
input_path = os.path.join(args.in_dir,args.image_name)
|
||||
output_path = os.path.join(args.out_dir,args.image_name_new)
|
||||
photoshop_actions_emulation(input_path, output_path)
|
||||
|
||||
|
||||
@@ -0,0 +1,139 @@
|
||||
import os.path
|
||||
import shutil
|
||||
import time
|
||||
import argparse
|
||||
import cv2
|
||||
import numpy as np
|
||||
from fix_up_color_two import remove_gray_and_sharpening
|
||||
from ps_image_shadow_up_ag import photoshop_actions_emulation
|
||||
|
||||
def perceptual_adjustment(img, threshold=220, reduction=0.5):
|
||||
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
h, s, v = cv2.split(hsv)
|
||||
|
||||
saturation_weights = 1 - (s.astype(np.float32) / 255 * 0.01)
|
||||
|
||||
adjusted_v = np.where(
|
||||
v > threshold,
|
||||
threshold + (v - threshold) * (1 - reduction * saturation_weights),
|
||||
v
|
||||
)
|
||||
|
||||
return cv2.cvtColor(cv2.merge([h, s, adjusted_v.astype(np.uint8)]), cv2.COLOR_HSV2BGR)
|
||||
|
||||
|
||||
def process_image(input_path, output_path, threshold=210, reduction=0.6):
|
||||
"""
|
||||
"""
|
||||
try:
|
||||
img = cv2.imread(input_path)
|
||||
if img is None:
|
||||
raise ValueError("无法读取图像，请检查路径是否正确")
|
||||
|
||||
result = perceptual_adjustment(img, threshold, reduction)
|
||||
cv2.imwrite(output_path, result)
|
||||
print(f"处理成功，结果已保存到: {output_path}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"处理失败: {str(e)}")
|
||||
return False
|
||||
|
||||
def sigmoid(x, center=0.0, slope=10.0):
|
||||
return 1 / (1 + np.exp(-slope * (x - center)))
|
||||
|
||||
|
||||
def reduce_highlights_lab_advanced_hsvmask(img, highlight_thresh=220, strength=30, sigma=15):
|
||||
"""
|
||||
"""
|
||||
|
||||
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
V = hsv[:, :, 2].astype(np.float32)
|
||||
|
||||
mask = sigmoid(V, center=highlight_thresh, slope=0.05)
|
||||
mask = np.clip(mask, 0, 1)
|
||||
mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=2)
|
||||
|
||||
mask_vis = (mask * 255).astype(np.uint8)
|
||||
|
||||
img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
|
||||
L, a, b = cv2.split(img_lab)
|
||||
L = L.astype(np.float32)
|
||||
|
||||
L_blur = cv2.GaussianBlur(L, (0, 0), sigma)
|
||||
L_detail = L - L_blur
|
||||
|
||||
L_dark = np.clip(L_blur - strength * mask, 0, 255)
|
||||
L_new = np.clip(L_dark + L_detail, 0, 255).astype(np.uint8)
|
||||
|
||||
lab_new = cv2.merge([L_new, a, b])
|
||||
result = cv2.cvtColor(lab_new, cv2.COLOR_Lab2BGR)
|
||||
|
||||
return result, mask_vis
|
||||
|
||||
|
||||
def correct_light_again_hsv(image_path):
|
||||
img = cv2.imread(image_path)
|
||||
result, mask_vis = reduce_highlights_lab_advanced_hsvmask(
|
||||
img,
|
||||
highlight_thresh=210,
|
||||
strength=10,
|
||||
sigma=3
|
||||
)
|
||||
output_image_path = image_path.replace(".jpg", "_light02.jpg")
|
||||
cv2.imwrite(
|
||||
output_image_path,
|
||||
result
|
||||
)
|
||||
return output_image_path
|
||||
|
||||
def correct_texture_image(input_path,image_result_dir,output_path):
|
||||
""""""
|
||||
#input_path = os.path.join(image_in_dir, image_name)
|
||||
image_name= input_path.split("/")[-1]
|
||||
params = {
|
||||
'threshold': 200,
|
||||
'reduction': 0.6
|
||||
}
|
||||
image_cache_dir= os.path.join(image_result_dir,"cache")
|
||||
os.makedirs(image_cache_dir, exist_ok=True)
|
||||
out_put_path = os.path.join(image_cache_dir, image_name)
|
||||
shadow_up_path = out_put_path.replace(".jpg", "_shadow_up.jpg")
|
||||
photoshop_actions_emulation(input_path, shadow_up_path)
|
||||
image_light_down_fix_up_path = remove_gray_and_sharpening(shadow_up_path, image_cache_dir)
|
||||
output_light_up_path = image_light_down_fix_up_path.replace(".jpg", "_light_down.jpg")
|
||||
process_image(image_light_down_fix_up_path, output_light_up_path, **params)
|
||||
output_result_image_path=correct_light_again_hsv(output_light_up_path)
|
||||
shutil.copy(output_result_image_path,output_path)
|
||||
time.sleep(1)
|
||||
try:
|
||||
shutil.rmtree(image_cache_dir)
|
||||
# os.remove(shadow_up_path)
|
||||
# os.remove(image_light_down_fix_up_path)
|
||||
# os.remove(output_light_up_path)
|
||||
# os.remove(output_result_image_path)
|
||||
except:
|
||||
print("删除文件错误")
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
arg = argparse.ArgumentParser()
|
||||
arg.add_argument("-i","--image_path", type=str, default="")
|
||||
args = arg.parse_args()
|
||||
image_result_dir=os.path.dirname(args.image_path)
|
||||
os.makedirs(image_result_dir, exist_ok=True)
|
||||
|
||||
start_time= time.time()
|
||||
correct_texture_image(args.input_path,image_result_dir,args.image_path)
|
||||
end_time = time.time()
|
||||
total_time = round(end_time - start_time, 2)
|
||||
print(f"处理成功，耗时 {total_time} 秒，")
|
||||
"""
|
||||
1、暗部提亮->白色提纯(220)->高光压暗->二次亮度调整
|
||||
"""
|
||||
|
||||
|
||||
|
||||
Reference in new issue
Block a user