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