Initial commit

01split.py

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+import cv2
+import numpy as np
+import os
+
+# 读取输入图像
+image = cv2.imread('input.jpg')
+
+# 检查图像是否读取成功
+if image is None:
+    print("无法读取 'input.jpg'，请确保文件存在且路径正确。")
+    exit()
+
+# 获取图像的尺寸
+height, width = image.shape[:2]
+
+# 创建九个掩码，初始化为全黑
+masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)]
+
+# 定义曲线参数，增加弯曲程度
+amplitude_h = height / 15  # 横向曲线的最大幅度
+amplitude_v = width / 15  # 纵向曲线的最大幅度
+
+# 定义 x 和 y 坐标
+x = np.arange(width)
+y = np.arange(height)
+
+# 定义横向曲线（沿着 x 轴变化），在每个区域的中间进行一次弯曲
+y_h1 = np.zeros_like(x, dtype=float)
+y_h2 = np.zeros_like(x, dtype=float)
+
+# 定义横向曲线的三个段
+segment_width = width / 3
+
+for i in range(3):
+    start_x = i * segment_width
+    end_x = (i + 1) * segment_width
+    center_x = (start_x + end_x) / 2
+
+    mask = (x >= start_x) & (x < end_x)
+    x_relative = x[mask] - center_x
+    amplitude_modifier = np.cos(np.pi * x_relative / (end_x - start_x)) ** 2
+
+    y_h1[mask] = (height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x))
+    y_h2[mask] = (2 * height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x))
+
+# 确保 y 值在图像范围内
+y_h1 = np.clip(y_h1, 0, height - 1).astype(int)
+y_h2 = np.clip(y_h2, 0, height - 1).astype(int)
+
+# 定义纵向曲线的三个段
+segment_height = height / 3
+
+x_v1 = np.zeros_like(y, dtype=float)
+x_v2 = np.zeros_like(y, dtype=float)
+
+for i in range(3):
+    start_y = i * segment_height
+    end_y = (i + 1) * segment_height
+    center_y = (start_y + end_y) / 2
+
+    mask = (y >= start_y) & (y < end_y)
+    y_relative = y[mask] - center_y
+    amplitude_modifier = np.cos(np.pi * y_relative / (end_y - start_y)) ** 2
+
+    x_v1[mask] = (width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y))
+    x_v2[mask] = (2 * width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y))
+
+# 确保 x 值在图像范围内
+x_v1 = np.clip(x_v1, 0, width - 1).astype(int)
+x_v2 = np.clip(x_v2, 0, width - 1).astype(int)
+
+# 创建网格
+Y, X = np.meshgrid(np.arange(height), np.arange(width), indexing='ij')
+
+# 获取对应的曲线值
+y_curve1 = y_h1[X]
+y_curve2 = y_h2[X]
+x_curve1 = x_v1[Y]
+x_curve2 = x_v2[Y]
+
+# 计算区域索引
+region = np.zeros((height, width), dtype=int)
+
+# 定义区域编号如下：
+# 0 | 1 | 2
+# 3 | 4 | 5
+# 6 | 7 | 8
+
+# 上区域
+upper = Y <= y_curve1
+middle = (Y > y_curve1) & (Y <= y_curve2)
+lower = Y > y_curve2
+
+# 左、中、右区域
+left = X <= x_curve1
+center = (X > x_curve1) & (X <= x_curve2)
+right = X > x_curve2
+
+# 分配区域
+region[upper & left] = 0
+region[upper & center] = 1
+region[upper & right] = 2
+region[middle & left] = 3
+region[middle & center] = 4
+region[middle & right] = 5
+region[lower & left] = 6
+region[lower & center] = 7
+region[lower & right] = 8
+
+# 创建掩码
+for idx in range(9):
+    masks[idx][region == idx] = 255
+
+# 确保输出目录存在
+output_dir = 'output'
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# 应用掩码并保存带透明度的图片
+for idx, mask in enumerate(masks):
+    # 提取 RGB 部分
+    img_part = cv2.bitwise_and(image, image, mask=mask)
+
+    # 转换为 BGRA
+    img_part = cv2.cvtColor(img_part, cv2.COLOR_BGR2BGRA)
+
+    # 设置 alpha 通道
+    img_part[:, :, 3] = mask
+
+    # 保存为 PNG 格式以保留透明度
+    cv2.imwrite(os.path.join(output_dir, f'output{idx + 1}.png'), img_part)
+
+print("图片已成功切割成9张，并保存到 'output' 目录下，带有透明背景。")