111

apps/auto_convert3d_new.py

@@ -3,7 +3,7 @@ import os, oss2, time, redis, requests, shutil, sys, subprocess, json, platform
 from PIL import Image, ImageDraw, ImageFont
 from retrying import retry
 import atexit,platform
-import white_purification
+import white_purification_v1
 if platform.system() == 'Windows':
     sys.path.append('e:\\libs\\')
 import common
@@ -127,7 +127,9 @@ def team_check(r):
     #对文件进行白色提纯处理
     imagePath = os.path.join(workdir, 'print', pid,pid+"Tex1.jpg")
     print("开始处理白色提纯")
-    white_purification.white_purification_utils(imagePath)    
+    #white_purification_v1.white_purification_utils(imagePath)  
+    os.system(f'python d:\\make2\\apps\white_purification_v1.py.py -i {imagePath}')
+
     print("贴图文件白色提纯完成",imagePath)
     #提纯完重新上传提纯图片
     ossImagePath = os.path.join("objs/print", pid,ossPath,"texture","process_"+pid+"Tex1.jpg")

apps/fix_up_color_two.py

@@ -0,0 +1,242 @@
+import cv2, numpy as np, matplotlib.pyplot as plt, os, shutil, argparse, random, time, math
+from tqdm import tqdm
+
+def ps_color_scale_adjustment(image, shadow=0, highlight=255, midtones=1):
+    '''
+    模拟 PS 的色阶调整； 0 <= Shadow < Highlight <= 255
+    :param image: 传入的图片
+    :param shadow: 黑场(0-Highlight)
+    :param highlight: 白场(Shadow-255)
+    :param midtones: 灰场(9.99-0.01)
+    :return: 图片
+    '''
+    if highlight >255:
+        highlight = 255
+    if shadow < 0:
+        shadow = 0
+    if shadow >= highlight:
+        shadow = highlight - 2
+    if midtones > 9.99:
+        midtones = 9.99
+    if midtones < 0.01:
+        midtones = 0.01
+    image = np.array(image, dtype=np.float16)
+    # 计算白场 黑场离差
+    Diff = highlight - shadow
+    image = image - shadow
+    image[image < 0] = 0
+    image = (image / Diff) ** (1 / midtones) * 255
+    image[image > 255] = 255
+    image = np.array(image, dtype=np.uint8)
+
+    return image
+
+# def show_histogram(image, image_id, save_hist_dir, min_threshold, max_threshold):
+#     '''
+#     画出直方图展示
+#     :param image: 导入图片
+#     :param image_id: 图片id编号
+#     :param save_hist_dir: 保存路径
+#     :param min_threshold: 最小阈值
+#     :param max_threshold: 最大阈值
+#     :return: 原图image，和裁剪原图直方图高低阈值后的图片image_change
+#     '''
+#     plt.rcParams['font.family'] = 'SimHei'
+#     plt.rcParams['axes.unicode_minus'] = False
+#     plt.hist(image.ravel(), 254, range=(2, 256), density=False)
+#     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
+#     plt.hist(image.ravel(), 110, range=(200, 255), density=False)  # 放大 range(225, 255)
+#     plt.annotate('thresh1=' + str(min_threshold),  # 文本内容
+#                  xy=(min_threshold, 0),  # 箭头指向位置   # 阈值设定值！
+#                  xytext=(min_threshold, 500000),  # 文本位置   # 阈值设定值！
+#                  arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2))  # 箭头
+#     plt.annotate('thresh2=' + str(max_threshold),  # 文本内容
+#                  xy=(max_threshold, 0),  # 箭头指向位置  # 阈值设定值！
+#                  xytext=(max_threshold, 500000),  # 文本位置  # 阈值设定值！
+#                  arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2))  # 箭头
+#     # 在y轴上绘制一条直线
+#     # plt.axhline(y=10000, color='r', linestyle='--', linewidth=0.5)
+#     plt.title(str(image_id))
+#     # plt.show()
+#     # 保存直方图
+#     save_hist_name = os.path.join(save_hist_dir, f'{image_id}_{min_threshold}&{max_threshold}.jpg')
+#     plt.savefig(save_hist_name)
+#     # 清空画布， 防止重叠展示
+#     plt.clf()
+
+
+# def low_find_histogram_range(image, target_frequency):
+#     '''
+#     循环查找在 target_frequency (y)频次限制下的直方图区间值(x)
+#     :param image: 导入图片
+#     :param target_frequency: 直方图 y 频次限制条件
+#     :return: 直方图区间 x，和 该区间频次 y
+#     '''
+#     # 计算灰度直方图
+#     hist, bins = np.histogram(image, bins=256, range=[0, 256])
+#     # 初始化区间和频次
+#     interval = 2
+#     frequency = hist[255]
+#     while frequency < target_frequency:
+#         # 更新区间和频次
+#         interval += 1
+#         # 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。
+#         frequency = hist[interval] if hist[interval] is not None else 0
+#         frequency += hist[interval] if hist[interval] is not None else 0
+#         # 如果频次接近10000则停止循环
+#         if target_frequency - 2000 <= frequency <= target_frequency + 1000:
+#             break
+#
+#     return interval, frequency
+
+
+# def high_find_histogram_range(image, target_frequency):
+#     '''
+#     循环查找在 target_frequency (y)频次限制下的直方图区间值(x)
+#     :param image: 导入图片
+#     :param target_frequency: 直方图 y 频次限制条件
+#     :return: 直方图区间 x，和 该区间频次 y
+#     '''
+#     # 计算灰度直方图
+#     hist, bins = np.histogram(image, bins=256, range=[0, 256])
+#     # 初始化区间和频次
+#     interval = 255
+#     frequency = hist[255]
+#     while frequency < target_frequency:
+#         # 更新区间和频次
+#         interval -= 1
+#         # 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。
+#         frequency = hist[interval] if hist[interval] is not None else 0
+#         frequency += hist[interval] if hist[interval] is not None else 0
+#         # 如果频次接近10000则停止循环
+#         if target_frequency - 2000 <= frequency <= target_frequency + 2000:
+#             break
+#
+#     return interval, frequency
+
+def find_last_x(image, slope_threshold = 1000):
+    x = []
+    y = []
+    hist, bins = np.histogram(image, bins=256, range=[0, 256])
+
+    #找到50以内的最高峰
+    max_y = 0
+    max_i = 5
+    for i in range(5, 25):
+        if hist[i] > max_y:
+            max_y = hist[i]
+            max_i = i
+    print(f'50以内最高峰值y：{max_y}，最高峰位置x：{max_i}')
+
+    for i in range(2, max_i):
+        x.append(i)
+        y.append(hist[i])
+    slopes = [abs(y[i + 1] - y[i]) for i in range(len(x) - 1)]
+
+    current_interval = []
+    max_interval = []
+    max_x = {}
+    for i, slope in enumerate(slopes):
+        current_interval.append(slope)
+        if slope >= slope_threshold:
+            if len(current_interval) > len(max_interval):
+                max_interval = current_interval.copy()
+                max_x[x[i]] = slope
+            current_interval = []
+
+    print(max_x)
+    last_x = list(max_x)[-1]
+    last_y = max_x[last_x]
+    return last_x, last_y
+
+def find_last_high(image, slope_threshold = 2500):
+    x = []
+    y = []
+    hist, bins = np.histogram(image, bins=255, range=[2, 255])
+
+    #找到200以上的最高峰
+    max_y = 0
+    max_i = 254
+    for i in range(220, 255):
+        if hist[i] > max_y:
+            max_y = hist[i]
+            max_i = i
+    print(f'200以上的最高峰值y：{max_y}，最高峰位置x：{max_i}')
+
+    for i in range(max_i, 255):
+        x.append(i)
+        y.append(hist[i])
+    slopes = [abs(y[i + 1] - y[i]) for i in range(len(x) - 1)]
+
+    current_interval = []
+    max_interval = []
+    max_x = {}
+    find = False
+    for i in range(len(slopes) - 1, -1, -1):
+        slope = slopes[i]
+        current_interval.append(slope)
+        if slope >= slope_threshold:
+            find = True
+            if len(current_interval) > len(max_interval):
+                max_interval = current_interval.copy()
+                max_x[x[i]] = slope
+            current_interval = []
+        #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
+        if not find and hist[220] < 5000:
+            max_x[220] = hist[220]
+
+
+    print(max_x)
+    if len(max_x) > 0:
+        last_x = list(max_x)[0]
+        last_y = max_x[last_x]
+        if last_x < 220:
+            last_x = 220
+            # last_y = max_x[last_x]
+    else:
+        print(f'找不到200以上曲线较平的区间，使用254作为最高峰')
+        last_x = 254
+        last_y = hist[254]
+    return last_x, last_y
+
+def remove_gray_and_sharpening(jpg_path,save_hist_dir):
+    input_image = cv2.imread(jpg_path)
+    filename = os.path.basename(jpg_path)
+    # low_x_thresh, low_y_frequency = low_find_histogram_range(input_image, low_y_limit)
+    low_x_thresh, low_y_frequency = find_last_x(input_image)
+    # high_x_thresh, high_y_frequency = high_find_histogram_range(input_image, high_y_limit)
+    high_x_thresh, high_y_frequency = find_last_high(input_image)
+    # print(f"{low_x_thresh} 区间, {low_y_frequency} 频次")
+    # print(f"{high_x_thresh} 区间, {high_y_frequency} 频次")
+    output_filename = os.path.join(save_hist_dir, filename).replace('.jpg', f'_{low_x_thresh}_{high_x_thresh}.jpg')
+    high_output_image = ps_color_scale_adjustment(input_image, shadow=low_x_thresh, highlight=high_x_thresh, midtones=1)
+    # high_output_image = ps_color_scale_adjustment(low_ouput_image, shadow=0, highlight=high_x_thresh, midtones=1)
+
+    # # 人体贴图和黑色背景交界处不进行锐化
+    # gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
+    # _, thresh = cv2.threshold(gray, 2, 255, cv2.THRESH_BINARY)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
+    # gradient = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel)
+    # roi_gradient = cv2.bitwise_and(high_output_image, high_output_image, mask=gradient)
+
+    # # 锐化滤波器
+    # # sharpened_image = sharpening_filter(high_output_image)
+    # sharpened_image = reduce_sharpness(high_output_image, factor=4)
+    # # 将原图边界替换锐化后的图片边界
+    # sharpened_image[gradient != 0] = roi_gradient[gradient != 0]
+
+    # 直方图标记并保存
+    # show_histogram(input_image, img_id, low_x_thresh, high_x_thresh)
+    # cv2.imwrite(jpg_path, high_output_image, [cv2.IMWRITE_JPEG_QUALITY, 95])  # 保存图片的质量是原图的 95%
+    cv2.imwrite(output_filename, high_output_image, [cv2.IMWRITE_JPEG_QUALITY, 95])  # 保存图片的质量是原图的 95%
+    return output_filename
+
+if __name__ == '__main__':
+    arg = argparse.ArgumentParser()
+    arg.add_argument('--jpg_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/input')
+    arg.add_argument('--save_hist_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/output')
+    args = arg.parse_args()
+
+    for img_id in tqdm(os.listdir(args.jpg_dir)):
+        jpg_path = os.path.join(args.jpg_dir, img_id)
+        remove_gray_and_sharpening(jpg_path,args.save_hist_dir)

apps/ps_image_shadow_up_ag.py

@@ -0,0 +1,192 @@
+import os.path
+import numpy as np
+import cv2
+import argparse
+
+
+def adjust_levels_image(img):
+    """"""
+    img = img.astype(np.float32)
+    img = 255 * ((img - 20) / (241 - 20))
+    img[img < 0] = 0
+    img[img > 255] = 255
+    img = 255 * np.power(img / 255.0, 1.0 / 1.34)
+    img = (img / 255) * (255- 0) + 0
+    img[img < 0] = 0
+    img[img > 255] = 255
+    img = img.astype(np.uint8)
+    return img
+
+
+def photoshop_style_feather(image, mask, radius=150):
+    """
+    """
+    if mask.dtype != np.uint8:
+        mask = (mask * 255).astype(np.uint8)
+    if len(mask.shape) > 2:
+        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+    kernel_size = max(3, int(2 * np.ceil(2 * radius) + 1))
+    expanded_size = (mask.shape[0] + 2 * radius, mask.shape[1] + 2 * radius)
+    expanded_mask = np.zeros(expanded_size, dtype=np.uint8)
+    center_y, center_x = radius, radius
+    expanded_mask[center_y:center_y + mask.shape[0],
+    center_x:center_x + mask.shape[1]] = mask
+
+    blurred_expanded_mask = cv2.GaussianBlur(
+        expanded_mask,
+        (kernel_size, kernel_size),
+        sigmaX=radius,
+        sigmaY=radius,
+        borderType=cv2.BORDER_REFLECT_101
+    )
+
+    feathered_mask = blurred_expanded_mask[center_y:center_y + mask.shape[0],
+                     center_x:center_x + mask.shape[1]]
+
+    feathered_mask = feathered_mask.astype(np.float32) / 255.0
+    feathered_mask = np.power(feathered_mask, 1.1)  # 轻微增强对比度
+    feathered_mask = np.clip(feathered_mask * 255, 0, 255).astype(np.uint8)
+
+    return feathered_mask
+
+def calculate_luminance(img):
+    """"""
+    if len(img.shape) == 3:
+        ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+        return ycrcb[:, :, 0].astype(np.float32) / 255.0
+    else:
+        return img.astype(np.float32) / 255.0
+
+
+def photoshop_feather_blend(adjusted_img, original_img, mask, feather_radius=150, brightness_factor=0.95):
+    """
+    """
+    if mask.dtype != np.uint8:
+        mask = (mask * 255).astype(np.uint8)
+    if len(mask.shape) > 2:
+        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+    feathered_mask = photoshop_style_feather(original_img, mask, feather_radius)
+    feathered_mask_float = feathered_mask.astype(np.float32) / 255.0
+
+    if len(original_img.shape) == 3 and len(feathered_mask_float.shape) == 2:
+        feathered_mask_float = np.stack([feathered_mask_float] * 3, axis=-1)
+
+    def to_linear(img):
+        img_linear = img.astype(np.float32) / 255.0
+        return np.where(img_linear <= 0.04045,
+                        img_linear / 12.92,
+                        ((img_linear + 0.055) / 1.055) ** 2.4)
+
+    def to_srgb(img_linear):
+        return np.where(img_linear <= 0.0031308,
+                        img_linear * 12.92,
+                        1.055 * (img_linear ** (1 / 2.4)) - 0.055)
+
+    adjusted_linear = to_linear(adjusted_img)
+    original_linear = to_linear(original_img)
+
+    luminance_adjustment = np.mean(original_linear, axis=-1, keepdims=True) * (1.0 - brightness_factor)
+    adjusted_linear_corrected = adjusted_linear - luminance_adjustment
+
+    blended_linear = (adjusted_linear_corrected * feathered_mask_float +
+                      original_linear * (1 - feathered_mask_float))
+
+    blended_srgb = to_srgb(blended_linear)
+    blended_img = np.clip(blended_srgb * 255, 0, 255).astype(np.uint8)
+
+    return blended_img
+
+
+def rgb2lab_image(rgb_img):
+    """"""
+    rgb = rgb_img.astype(np.float32) / 255.0
+
+    mask = rgb > 0.04045
+    rgb = np.where(mask,
+                   np.power((rgb + 0.055) / 1.055, 2.4),
+                   rgb / 12.92)
+
+    XYZ = np.dot(rgb, [
+        [0.436052025, 0.222491598, 0.013929122],
+        [0.385081593, 0.716886060, 0.097097002],
+        [0.143087414, 0.060621486, 0.714185470]
+    ])
+
+    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)
+
+

apps/white_purification_v1.py

@@ -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
+    """
+
+
+