dongchangxi
7 months ago
4 changed files with 577 additions and 2 deletions
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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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]) |
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XYZ *= np.array([100.0, 100.0, 100.0]) / [96.4221, 100.0, 82.5211] |
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epsilon = 0.008856 |
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kappa = 903.3 |
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XYZ_norm = np.where(XYZ > epsilon, |
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np.power(XYZ, 1 / 3), |
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(kappa * XYZ + 16) / 116) |
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L = 116 * XYZ_norm[..., 1] - 16 |
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a = 500 * (XYZ_norm[..., 0] - XYZ_norm[..., 1]) |
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b = 200 * (XYZ_norm[..., 1] - XYZ_norm[..., 2]) |
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return np.stack([L, a, b], axis=-1) |
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def photoshop_lab_color_range_optimized(bgr_img, target_lab, tolerance=59, anti_alias=True): |
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"""""" |
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rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) |
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lab_img = rgb2lab_image(rgb_img) |
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L, a, b = lab_img[:, :, 0], lab_img[:, :, 1], lab_img[:, :, 2] |
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target_L, target_a, target_b = target_lab |
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diff_L = np.abs(L - target_L) |
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diff_a = np.abs(a - target_a) |
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diff_b = np.abs(b - target_b) |
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dark_boost = np.ones_like(L) |
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dark_mask = L <40 |
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dark_boost[dark_mask] = 1.2 |
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weighted_diff = np.sqrt( |
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0.25 * (diff_L / 100) ** 2 + |
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0.75 * ((diff_a + diff_b) / 255) ** 2 |
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) * 100 |
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weighted_diff = weighted_diff / dark_boost |
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threshold = 1.6 * (100 - tolerance) / 100 * 23 |
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normalized_diff = weighted_diff / threshold |
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mask = 0.5 * (np.tanh(4 * (1 - normalized_diff)) + 1) |
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if anti_alias: |
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mask = cv2.GaussianBlur(mask, (5, 5), 0) |
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return mask |
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def photoshop_actions_emulation(input_path, output_path): |
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"""""" |
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original_img = cv2.imread(input_path) |
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target_lab = np.array([47.89, 20.31, 20.6], dtype=np.float32) |
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tol= 81 |
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mask = photoshop_lab_color_range_optimized(original_img, target_lab, tol) |
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mask_uint8 = (mask * 255).astype(np.uint8) |
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adjusted_img = adjust_levels_image(original_img) |
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result = photoshop_feather_blend(adjusted_img, original_img, mask_uint8, |
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feather_radius=150, brightness_factor=0.90) |
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cv2.imwrite(output_path, result) |
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|
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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 @@
@@ -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 |
||||
""" |
||||
|
||||
|
||||
|
||||
Loading…
Reference in new issue