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354 lines
11 KiB
354 lines
11 KiB
import os.path |
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import shutil |
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import time |
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import argparse |
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import cv2 |
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import numpy as np |
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from scipy.interpolate import CubicSpline |
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import sys, os |
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from PIL import Image, ImageEnhance |
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sys.path.append(os.path.dirname(os.path.abspath(__file__))) |
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from ps_image_shadow_up_ag_two_d import photoshop_actions_emulation |
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def smootherstep(x): |
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"""五次平滑插值函数：更加平滑过渡""" |
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return x**3 * (x * (x * 6 - 15) + 10) |
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def perceptual_smooth_adjustment_color_blend(img, threshold=220, reduction=0.5, margin=10, saturation_sensitivity=0.3, blur_radius=5, color_blend_strength=0.5): |
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""" |
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更平滑、颜色融合感知亮度压制 |
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- threshold: 压制起始亮度（V 通道） |
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- reduction: 压制强度（0-1） |
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- margin: 阈值过渡区间（像素亮度差） |
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- saturation_sensitivity: 饱和度高时减弱压制 |
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- blur_radius: 用于颜色融合的模糊半径 |
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- color_blend_strength: 颜色融合程度（0~1） |
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""" |
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) |
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h, s, v = cv2.split(hsv) |
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v = v.astype(np.float32) |
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s = s.astype(np.float32) |
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# 饱和度感知压制减弱 |
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sat_weight = 1.0 - (s / 255.0 * saturation_sensitivity) |
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sat_weight = np.clip(sat_weight, 0.0, 1.0) |
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# 平滑压制权重计算 |
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delta = v - threshold |
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transition = np.zeros_like(v, dtype=np.float32) |
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in_range = (delta > 0) & (delta < margin) |
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transition[in_range] = smootherstep(delta[in_range] / margin) |
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transition[delta >= margin] = 1.0 |
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# 压制权重融合 |
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weight = reduction * transition * sat_weight |
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# 应用压制 |
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v_new = v - (v - threshold) * weight |
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v_new = np.clip(v_new, 0, 255).astype(np.uint8) |
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# 合成压制后的图像 |
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adjusted_hsv = cv2.merge([h, s.astype(np.uint8), v_new]) |
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adjusted = cv2.cvtColor(adjusted_hsv, cv2.COLOR_HSV2BGR) |
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# ------------------- |
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# 融合原图模糊版 → 减少颜色突兀 |
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# ------------------- |
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blurred = cv2.GaussianBlur(img, (blur_radius | 1, blur_radius | 1), 0) |
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# 构建融合权重 mask，仅对过渡区域起作用 |
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color_blend_mask = np.clip(weight, 0, 1) * color_blend_strength |
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color_blend_mask = color_blend_mask[..., None] # 扩展为 (H,W,1) 用于通道融合 |
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# 将融合区域混合模糊 |
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final = adjusted.astype(np.float32) * (1 - color_blend_mask) + blurred.astype(np.float32) * color_blend_mask |
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final = np.clip(final, 0, 255).astype(np.uint8) |
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return final |
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def process_image(input_path, output_path, threshold=210, reduction=0.6): |
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""" |
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""" |
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try: |
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img = cv2.imread(input_path) |
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if img is None: |
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raise ValueError("无法读取图像，请检查路径是否正确") |
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#result = perceptual_adjustment(img, threshold, reduction) |
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result = perceptual_smooth_adjustment_color_blend(img, threshold, reduction) |
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cv2.imwrite(output_path, result) |
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print(f"处理成功，结果已保存到: {output_path}") |
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return True |
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except Exception as e: |
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print(f"处理失败: {str(e)}") |
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return False |
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def sigmoid(x, center=0.0, slope=10.0): |
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return 1 / (1 + np.exp(-slope * (x - center))) |
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def reduce_highlights_lab_advanced_hsvmask( |
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img, |
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highlight_thresh=220, |
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strength=30, |
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sigma=15, |
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detail_boost=1.0, |
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preserve_local_contrast=True |
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): |
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""" |
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LAB高光压制 + HSV感知蒙版 + 细节保留 |
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""" |
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) |
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V = hsv[:, :, 2].astype(np.float32) |
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# 1. 生成高光 mask，过渡平滑 |
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mask = sigmoid(V, center=highlight_thresh, slope=0.05) |
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mask = np.clip(mask, 0, 1) |
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mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=2) |
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mask_vis = (mask * 255).astype(np.uint8) |
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# 2. LAB 空间亮度压制 |
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img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab) |
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L, a, b = cv2.split(img_lab) |
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L = L.astype(np.float32) |
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# 3. 模糊和细节 |
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L_blur = cv2.GaussianBlur(L, (0, 0), sigma) |
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L_detail = L - L_blur |
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# 4. 替代方案：压制 L，但融合方式更柔和 |
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L_target = L_blur - strength * mask |
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L_target = np.clip(L_target, 0, 255) |
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if preserve_local_contrast: |
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# 保留细节 + 局部对比度（避免过度平滑） |
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L_new = L_target + detail_boost * L_detail |
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else: |
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# 单纯压制亮度 |
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L_new = L_target |
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L_new = np.clip(L_new, 0, 255).astype(np.uint8) |
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# 5. 合成回去 |
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lab_new = cv2.merge([L_new, a, b]) |
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result = cv2.cvtColor(lab_new, cv2.COLOR_Lab2BGR) |
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return result, mask_vis |
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def suppress_highlights_keep_texture(image_bgr, v_thresh=225, target_v=215, sigma=1): |
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"""""" |
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image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV) |
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h, s, v = cv2.split(image_hsv) |
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v = v.astype(np.float32) |
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v_blur = cv2.GaussianBlur(v, (0, 0), sigmaX=sigma) |
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detail = v - v_blur |
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# 构建 soft mask（0~1），用于动态压制 |
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mask = (v_blur > v_thresh).astype(np.float32) |
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# weight 越大压得越狠 |
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weight = np.clip((v_blur - v_thresh) / 20.0, 0, 1) * mask # 20 是压制带宽 |
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#weight =weight*1.2 |
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# 将亮度压到 target_v 的线性混合： |
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v_compress = v_blur * (1 - weight) + target_v * weight |
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v_new = v_compress + detail |
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v_new = np.clip(v_new, 0, 255).astype(np.uint8) |
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hsv_new = cv2.merge([h, s, v_new]) |
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result_bgr = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR) |
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return result_bgr |
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def correct_light_again_hsv(image_path): |
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img = cv2.imread(image_path) |
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result, mask_vis = reduce_highlights_lab_advanced_hsvmask( |
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img, |
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highlight_thresh=225, |
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strength=15, |
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sigma=10, |
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detail_boost=1.2 |
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) |
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result_bgr= suppress_highlights_keep_texture(result) |
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output_image_path = image_path.replace(".jpg", "_light02.jpg") |
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cv2.imwrite( |
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output_image_path, |
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result_bgr |
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) |
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return output_image_path |
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def generate_curve_lut(x_points, y_points): |
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""" |
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输入采样点，生成 256 长度的查找表（LUT） |
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""" |
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cs = CubicSpline(x_points, y_points, bc_type='natural') |
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x = np.arange(256) |
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y = cs(x) |
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y = np.clip(y, 0, 255).astype(np.uint8) |
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return y |
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def apply_curve(img, lut): |
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""" |
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对图像的每个通道应用曲线 LUT（复合通道） |
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""" |
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result = cv2.LUT(img, lut) |
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return result |
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def apply_curve_up_image(image_path,image_cache_dir): |
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"""提亮""" |
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x_points = [0, 124, 255] |
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y_points = [0, 131, 255] |
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lut = generate_curve_lut(x_points, y_points) |
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#adjusted = apply_curve(img, lut) |
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image_name_result = image_path.split("/")[-1].replace(".jpg", "_up.jpg") |
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result_path= os.path.join(image_cache_dir,image_name_result) |
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image_bgr = cv2.imread(image_path) |
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image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) |
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image_hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV).astype(np.float32) |
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h, s, v = cv2.split(image_hsv) |
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v_mean = np.mean(v) |
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print(f"v_mean{v_mean}") |
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if v_mean < 60: |
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adjusted = apply_curve(image_bgr, lut) |
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adjusted2 = apply_curve(adjusted, lut) |
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cv2.imwrite(result_path, adjusted2) |
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return result_path |
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else: |
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image_name_result = image_path.split("/")[-1].replace(".jpg", "_o.jpg") |
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result_original_path = os.path.join(image_cache_dir, image_name_result) |
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shutil.copy(image_path,result_original_path) |
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return result_original_path |
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def apply_curve_down_image(image_path,image_cache_dir): |
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"""压暗""" |
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x_points = [0, 131, 255] |
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y_points = [0, 124, 255] |
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lut = generate_curve_lut(x_points, y_points) |
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# adjusted = apply_curve(img, lut) |
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image_name_result = image_path.split("/")[-1].replace(".jpg", "_down.jpg") |
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result_path= os.path.join(image_cache_dir,image_name_result) |
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image_bgr = cv2.imread(image_path) |
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image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) |
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image_hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV).astype(np.float32) |
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h, s, v = cv2.split(image_hsv) |
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v_mean = np.mean(v) |
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print(f"v_mean{v_mean}") |
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if v_mean > 110: |
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adjusted = apply_curve(image_bgr, lut) |
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adjusted2 = apply_curve(adjusted, lut) |
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cv2.imwrite(result_path, adjusted2) |
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return result_path |
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else: |
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image_name_result = image_path.split("/")[-1].replace(".jpg", "_o.jpg") |
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result_original_path = os.path.join(image_cache_dir, image_name_result) |
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shutil.copy(image_path, result_original_path) |
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return result_original_path |
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def sharpen_image(image_path, output_path): |
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""" |
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修复颜色问题的锐化处理函数 |
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""" |
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# 1. 读取图片并确保RGB格式 |
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image = cv2.imread(image_path) |
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if image is None: |
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raise ValueError("无法读取图片，请检查路径是否正确") |
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# 2. 转换为RGB并保持一致性 |
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rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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# 3. 使用PIL处理时不再转换 |
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pil_img = Image.fromarray(rgb_image) |
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# 3.1 锐化处理 |
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enhancer = ImageEnhance.Sharpness(pil_img) |
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sharpened = enhancer.enhance(2.0) |
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# 3.2 对比度增强 |
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contrast_enhancer = ImageEnhance.Contrast(sharpened) |
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final_image = contrast_enhancer.enhance(1.2) |
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# 4. 转换回numpy数组 |
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cv_image = np.array(final_image) |
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# 5. 修复颜色问题的非锐化掩蔽 |
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# 先分离通道，分别处理，再合并 |
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b, g, r = cv2.split(cv_image) |
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def unsharp_channel(channel): |
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blurred = cv2.GaussianBlur(channel, (0, 0), 3) |
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return cv2.addWeighted(channel, 1.5, blurred, -0.5, 0) |
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b_sharp = unsharp_channel(b) |
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g_sharp = unsharp_channel(g) |
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r_sharp = unsharp_channel(r) |
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# 合并通道 |
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sharpened_cv = cv2.merge([b_sharp, g_sharp, r_sharp]) |
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# 6. 保存结果(保持BGR格式) |
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cv2.imwrite(output_path, cv2.cvtColor(sharpened_cv, cv2.COLOR_RGB2BGR)) |
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def correct_texture_image(input_path,image_result_dir,output_path): |
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"""""" |
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image_cache_dir= os.path.join(image_result_dir,"cache") |
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os.makedirs(image_cache_dir, exist_ok=True) |
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input_path_cure_up = apply_curve_up_image(input_path,image_cache_dir) |
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input_path_cure_down_result = apply_curve_down_image(input_path_cure_up,image_cache_dir) |
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print("input_path_correct", input_path_cure_down_result) |
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shadow_up_path = input_path_cure_down_result.replace(".jpg", "_shadow_shadow_add_color_white_unsharp.jpg") |
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photoshop_actions_emulation(input_path_cure_down_result, shadow_up_path) |
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shutil.copy(shadow_up_path,output_path) |
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time.sleep(1) |
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try: |
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shutil.rmtree(image_cache_dir) |
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except: |
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print("删除文件错误") |
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return shadow_up_path |
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if __name__ == "__main__": |
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arg = argparse.ArgumentParser() |
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arg.add_argument('-input_path', type=str, default=f"") |
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arg.add_argument('-output_path', type=str, default=f"") |
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args = arg.parse_args() |
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image_result_dir=os.path.dirname(args.output_path) |
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os.makedirs(image_result_dir, exist_ok=True) |
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start_time= time.time() |
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correct_texture_image(args.input_path,image_result_dir,args.output_path) |
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end_time = time.time() |
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total_time = round(end_time - start_time, 2) |
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""" |
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DreamTech，PS动作F7两次+Shift F7一次 |
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F7：：：加暗*2 |
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Shift F7 公仔*1 |
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公仔： 加暗，加暗，加饱和度上色，白位加白，锐化 |
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""" |
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