白色提纯版本处理

apps/fix_up_color_two.py

@@ -143,8 +143,11 @@ def find_last_x(image, slope_threshold = 1000):
                 max_interval = current_interval.copy()
                 max_x[x[i]] = slope
             current_interval = []
-
+    if not max_x:
+        # 尝试降低阈值重新计算
+        return find_last_x(image, slope_threshold=slope_threshold // 2)
     print(max_x)
+
     last_x = list(max_x)[-1]
     last_y = max_x[last_x]
     return last_x, last_y
@@ -157,7 +160,7 @@ def find_last_high(image, slope_threshold = 2500):
     #找到200以上的最高峰
     max_y = 0
     max_i = 254
-    for i in range(220, 255):
+    for i in range(240, 255):
         if hist[i] > max_y:
             max_y = hist[i]
             max_i = i
@@ -181,17 +184,17 @@ def find_last_high(image, slope_threshold = 2500):
                 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
+        #如果没有找到200以上很平，而且高度小于5000，就按240位置削平
-        if not find and hist[220] < 5000:
+        if not find and hist[240] < 5000:
-            max_x[220] = hist[220]
+            max_x[240] = hist[240]
 
 
     print(max_x)
     if len(max_x) > 0:
         last_x = list(max_x)[0]
         last_y = max_x[last_x]
-        if last_x < 220:
+        if last_x < 240:
-            last_x = 220
+            last_x = 240
             # 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')

apps/white_purification_v2.py

@@ -0,0 +1,163 @@
+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 suppress_highlights_keep_texture(image_bgr, v_thresh=225, target_v=215, sigma=1):
+    """"""
+
+    image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(image_hsv)
+    v = v.astype(np.float32)
+
+    v_blur = cv2.GaussianBlur(v, (0, 0), sigmaX=sigma)
+    detail = v - v_blur
+
+    # 构建 soft mask（0~1），用于动态压制
+    mask = (v_blur > v_thresh).astype(np.float32)
+    # weight 越大压得越狠
+    weight = np.clip((v_blur - v_thresh) / 20.0, 0, 1) * mask  # 20 是压制带宽
+    #weight =weight*1.2
+    # 将亮度压到 target_v 的线性混合：
+    v_compress = v_blur * (1 - weight) + target_v * weight
+
+    v_new = v_compress + detail
+    v_new = np.clip(v_new, 0, 255).astype(np.uint8)
+
+    hsv_new = cv2.merge([h, s, v_new])
+    result_bgr = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)
+
+    return result_bgr
+
+def correct_light_again_hsv(image_path):
+    img = cv2.imread(image_path)
+    result, mask_vis = reduce_highlights_lab_advanced_hsvmask(
+        img,
+        highlight_thresh=230,
+        strength=7,
+        sigma=3
+    )
+    result_bgr= suppress_highlights_keep_texture(result)
+    output_image_path = image_path.replace(".jpg", "_light02.jpg")
+    cv2.imwrite(
+        output_image_path,
+        result_bgr
+    )
+    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': 220,
+        '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:
+        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.image_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
+    """
+
+
+