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383 lines
12 KiB
383 lines
12 KiB
import os.path |
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import numpy as np |
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from scipy.interpolate import CubicSpline |
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import cv2 |
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import argparse |
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from ps_image_white_add_white_d import photoshop_add_white |
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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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# plt.figure(figsize=(10, 8)) |
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# plt.imshow(mask, cmap='gray') |
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# plt.title("Feathered Mask") |
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# plt.axis('off') |
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# plt.colorbar(label='Opacity') |
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# plt.show() |
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feathered_mask = photoshop_style_feather(original_img, mask, feather_radius) |
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# plt.figure(figsize=(10, 8)) |
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# plt.imshow(feathered_mask, cmap='gray') |
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# plt.title("Feathered Mask") |
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# plt.axis('off') |
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# plt.colorbar(label='Opacity') |
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# plt.show() |
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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 generate_curve_lut(x_points, y_points, smooth_factor=20): |
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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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if smooth_factor > 0: |
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new_x, new_y = [], [] |
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for i in range(len(x_points) - 1): |
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x0, x1 = x_points[i], x_points[i + 1] |
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y0, y1 = y_points[i], y_points[i + 1] |
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new_x.append(x0) |
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new_y.append(y0) |
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# 在每对原始控制点之间插入平滑点 |
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steps = max(1, int(smooth_factor * (x1 - x0) / 256)) |
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for j in range(1, steps): |
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alpha = j / steps |
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new_x.append(int(x0 + alpha * (x1 - x0))) |
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new_y.append(y0 + alpha * (y1 - y0)) |
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# 添加最后一个点 |
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new_x.append(x_points[-1]) |
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new_y.append(y_points[-1]) |
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x_points, y_points = new_x, new_y |
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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 add_color_image(img): |
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"""""" |
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# x_points = [0, 131, 255] |
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# y_points = [0, 124, 255] |
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x_points = [6, 184, 255] |
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y_points = [0, 191, 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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return adjusted |
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def unsharp_mask(img_bgr,amount=0.47, radius=3, threshold=0): |
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"""""" |
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img = img_bgr.astype(np.float32) / 255.0 |
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img_ycc = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) |
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y, cr, cb = cv2.split(img_ycc) |
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# 模糊亮度通道 |
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blurred_y = cv2.GaussianBlur(y, (0, 0), sigmaX=radius, sigmaY=radius) |
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# 差值（高频图像） |
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high_pass = y - blurred_y |
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# 阈值处理：模仿 PS 的 threshold（在 0~1 范围内） |
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if threshold > 0: |
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mask = np.abs(high_pass) >= (threshold / 255.0) |
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high_pass = high_pass * mask |
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# 关键：亮边增强 + 暗边压制（模拟 PS 更自然的边缘） |
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sharp_y = y + amount * high_pass |
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sharp_y = np.clip(sharp_y, 0, 1) |
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# 合并回图像 |
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merged_ycc = cv2.merge([sharp_y, cr, cb]) |
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final_img = cv2.cvtColor(merged_ycc, cv2.COLOR_YCrCb2BGR) |
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final_img = np.clip(final_img * 255.0, 0, 255).astype(np.uint8) |
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#cv2.imwrite(output_path, final_img) |
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return final_img |
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def add_shadow_image(img): |
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"""""" |
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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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return adjusted |
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def create_red_mask(img): |
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"""使用 Lab 空间中的 A 通道提取红色区域，返回 (h, w, 1) 掩码""" |
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lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab) |
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a = lab[..., 1].astype(np.float32) |
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red_score = np.clip((a - 128) / 50.0, 0, 1) # A 通道 > 128 表示偏红 |
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red_score = cv2.GaussianBlur(red_score, (9, 9), sigmaX=4) |
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return red_score[..., np.newaxis] # 变成 (h, w, 1) |
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def cmyk_to_rgb(cmyk): |
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"""CMYK → RGB，模拟Photoshop近似""" |
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c, m, y, k = cmyk[..., 0], cmyk[..., 1], cmyk[..., 2], cmyk[..., 3] |
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r = (1 - c) * (1 - k) |
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g = (1 - m) * (1 - k) |
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b = (1 - y) * (1 - k) |
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return np.clip(np.stack([r, g, b], axis=-1) * 255, 0, 255) |
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def rgb_to_cmyk(rgb): |
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"""RGB → CMYK，模拟Photoshop近似""" |
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r, g, b = rgb[..., 0] / 255.0, rgb[..., 1] / 255.0, rgb[..., 2] / 255.0 |
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k = 1 - np.maximum.reduce([r, g, b]) |
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k_safe = np.where(k == 1, 1, k) |
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c = np.where(k == 1, 0, (1 - r - k) / (1 - k_safe)) |
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m = np.where(k == 1, 0, (1 - g - k) / (1 - k_safe)) |
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y = np.where(k == 1, 0, (1 - b - k) / (1 - k_safe)) |
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return np.stack([c, m, y, k], axis=-1) |
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def selective_color_adjustment(img, target_color, cmyk_adjustments, relative=True): |
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if target_color != 'red': |
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raise NotImplementedError("当前只支持 red") |
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) |
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mask = create_red_mask(img) |
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cmyk = rgb_to_cmyk(img_rgb) |
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# 应用 CMYK 调整 |
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for channel, value in cmyk_adjustments.items(): |
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idx = {'cyan': 0, 'magenta': 1, 'yellow': 2, 'black': 3}.get(channel) |
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if idx is None: |
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continue |
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original = cmyk[..., idx] |
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v = value / 100.0 |
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if relative: |
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# 非线性相对调整，更接近 Photoshop 曲线（经验公式） |
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adjusted = original * (1 + v) ** 1.35 # gamma 可调 |
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else: |
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adjusted = original + v |
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cmyk[..., idx] = np.clip(adjusted, 0, 1) |
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# 转换回 RGB 并混合 |
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adjusted_rgb = cmyk_to_rgb(cmyk) |
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output_rgb = img_rgb * (1 - mask) + adjusted_rgb * mask |
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output_rgb = np.clip(output_rgb, 0, 255).astype(np.uint8) |
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return cv2.cvtColor(output_rgb, cv2.COLOR_RGB2BGR) |
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def reduce_red_black_relative(img): |
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"""模拟 Photoshop：红色 → 黑色 -8%，相对模式""" |
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return selective_color_adjustment( |
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img, |
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target_color='red', |
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cmyk_adjustments={'black': -8}, |
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relative=True |
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) |
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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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# 加暗 |
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shadow_image1=add_shadow_image(original_img) |
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shadow_image2 = add_shadow_image(shadow_image1) |
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# output_down_path= output_path.replace(".jpg","down.jpg") |
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# cv2.imwrite(output_down_path, shadow_image2) |
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original_img_color=add_color_image(shadow_image2) |
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# output_color_path= output_path.replace(".jpg","add_color.jpg") |
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# cv2.imwrite(output_color_path, original_img_color) |
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#白位加白 |
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result_white_image= photoshop_add_white(original_img_color) |
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# output_white_path= output_color_path.replace(".jpg","white.jpg") |
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# cv2.imwrite(output_white_path, result_white_image) |
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#锐化 |
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result_usm = unsharp_mask(result_white_image,amount=0.47, radius=3, threshold=0) |
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cv2.imwrite(output_path, result_usm) |
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if __name__ == '__main__': |
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arg = argparse.ArgumentParser() |
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arg.add_argument('--image_name', type=str, default='274351Tex1_adjusted060518_2_221.jpg') |
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arg.add_argument('--image_name_new', type=str, default='274351Tex1_adjusted060518_2_221_999999.jpg') |
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arg.add_argument('--in_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/output/') |
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arg.add_argument('--out_dir', type=str, default='/data/datasets_20t/fsdownload/image_color_timing/shadow_up/') |
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args = arg.parse_args() |
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os.makedirs(args.out_dir,exist_ok=True) |
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input_path = os.path.join(args.in_dir,args.image_name) |
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output_path = os.path.join(args.out_dir,args.image_name_new) |
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photoshop_actions_emulation(input_path, output_path) |
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