128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
import os
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import numpy as np
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from PIL import Image, ImageFilter, ImageChops
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def split_cloud_smart():
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input_path = os.path.join(os.path.dirname(__file__), 'origin', 'cloud.jpg')
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output_dir = os.path.join(os.path.dirname(__file__), 'clouds')
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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print(f"Processing {input_path}...")
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try:
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img = Image.open(input_path).convert("RGBA")
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except Exception as e:
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print(f"Error opening image: {e}")
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return
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# 1. 智能采样背景色 (Smart Sampling)
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# 取图像四周边缘的像素来计算背景特征，比只取一个角更稳健
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width, height = img.size
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# 提取上、下、左、右四条边的像素
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top_edge = np.array(img.crop((0, 0, width, 1)))
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bottom_edge = np.array(img.crop((0, height-1, width, height)))
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left_edge = np.array(img.crop((0, 0, 1, height)))
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right_edge = np.array(img.crop((width-1, 0, width, height)))
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# 合并边缘像素
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edges = np.concatenate([
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top_edge.reshape(-1, 4),
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bottom_edge.reshape(-1, 4),
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left_edge.reshape(-1, 4),
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right_edge.reshape(-1, 4)
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])
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# 计算背景色的平均值和标准差，用于确定容差范围
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bg_mean = np.mean(edges, axis=0)
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print(f"Smart sampled background color (RGBA): {bg_mean}")
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# 2. HSV 色彩空间分离 (HSV Separation)
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# 将图片转为 HSV，利用饱和度(S)和亮度(V)来区分云(通常S低V高)和深色背景(通常S高V低)
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hsv_img = img.convert("HSV")
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hsv_data = np.array(hsv_img)
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rgb_data = np.array(img)
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# 提取通道
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H, S, V = hsv_data[:,:,0], hsv_data[:,:,1], hsv_data[:,:,2]
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R, G, B = rgb_data[:,:,0], rgb_data[:,:,1], rgb_data[:,:,2]
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# 计算 RGB 欧氏距离 (针对平均背景色)
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# 只比较 RGB 前三个通道
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diff_r = R.astype(float) - bg_mean[0]
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diff_g = G.astype(float) - bg_mean[1]
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diff_b = B.astype(float) - bg_mean[2]
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rgb_distance = np.sqrt(diff_r**2 + diff_g**2 + diff_b**2)
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# 定义阈值
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# RGB 容差：允许背景有一定的颜色波动
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rgb_tolerance = 60.0
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# HSV 辅助判断：
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# 背景通常是深紫色：需要保护云朵(白色/灰色)，云朵的特征是低饱和度(Low S)
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# 如果一个像素离背景色有点远，但它饱和度很高且偏紫，那它可能还是背景(渐变区)
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# 如果一个像素离背景色很近，但它饱和度极低(它是灰色的云边缘)，那应该保留
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# 创建 Alpha Mask (0 为完全透明/背景，255 为完全不透明/云)
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# 初始 Mask：距离背景色越近，Alpha 越小
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alpha_mask = np.zeros_like(H, dtype=np.float32)
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# 核心逻辑：
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# 1. 主要是背景：RGB 距离 < 容差
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# 2. 渐变增强：对于边缘区域，使用 Sigmoid 函数做软过渡，而不是硬切
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# 归一化距离，距离越小越接近背景
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normalized_dist = np.clip(rgb_distance / rgb_tolerance, 0, 1)
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# 简单的线性映射翻转：距离越小(背景)，Alpha越小(透明)
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# 使用平滑函数 (Smoothstep) 让过渡更自然: 3x^2 - 2x^3
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alpha_mask = np.clip((normalized_dist - 0.2) / 0.6, 0, 1) # 0.2到0.8之间过渡
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alpha_mask = alpha_mask * alpha_mask * (3 - 2 * alpha_mask)
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# 3. 保护云朵核心 (Cloud Core Protection)
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# 如果像素很亮(V高)且饱和度很低(S低)，强制认为是云，设为不透明
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# 假设云是白色的，背景是深色的
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is_cloud_core = (V > 150) & (S < 60)
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alpha_mask[is_cloud_core] = 1.0
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# 4. 转换回 0-255 并应用羽化
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final_alpha = (alpha_mask * 255).astype(np.uint8)
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# 创建蒙版图像
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mask_img = Image.fromarray(final_alpha, mode='L')
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# 边缘羽化 (Matte Refinement)
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# 对蒙版进行轻微模糊，消除锯齿
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mask_img = mask_img.filter(ImageFilter.GaussianBlur(radius=1.5))
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# 将处理好的 Alpha 通道应用回原图
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r, g, b, a = img.split()
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img_transparent = Image.merge('RGBA', (r, g, b, mask_img))
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# 切割逻辑保持不变
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width, height = img.size
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cell_width = width // 3
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cell_height = height // 3
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count = 0
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for r in range(3):
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for c in range(3):
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left = c * cell_width
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top = r * cell_height
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right = left + cell_width
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bottom = top + cell_height
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cell = img_transparent.crop((left, top, right, bottom))
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output_filename = f"cloud_{count}.png"
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output_path = os.path.join(output_dir, output_filename)
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cell.save(output_path)
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print(f"Saved {output_path}")
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count += 1
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print("Smart split done!")
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if __name__ == "__main__":
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split_cloud_smart()
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