129 lines
4.2 KiB
Python
129 lines
4.2 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，增加弯曲幅度
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amplitude_h = height / 15 # 横向曲线的最大幅度（修改为height / 15，增加弯曲）
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amplitude_v = width / 15 # 纵向曲线的最大幅度（修改为width / 15，增加弯曲）
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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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# 定义纵向曲线（沿着 y 轴变化），在每个区域的中间进行一次弯曲
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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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# 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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# 定义每个段的起始和结束位置
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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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# 创建掩码，确定在哪个段上
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mask = (x >= start_x) & (x < end_x)
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# 计算在该段上的相对位置
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x_relative = x[mask] - center_x
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# 计算幅度调节函数，使得在段的边界处为零，在中心为一
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amplitude_modifier = np.cos(np.pi * x_relative / (end_x - start_x)) ** 2
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# 计算曲线 y 值
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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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for i in range(3):
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# 定义每个段的起始和结束位置
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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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# 创建掩码，确定在哪个段上
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mask = (y >= start_y) & (y < end_y)
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# 计算在该段上的相对位置
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y_relative = y[mask] - center_y
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# 计算幅度调节函数，使得在段的边界处为零，在中心为一
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amplitude_modifier = np.cos(np.pi * y_relative / (end_y - start_y)) ** 2
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# 计算曲线 x 值
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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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# 创建掩码
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for i in range(height):
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for j in range(width):
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y_curve1 = y_h1[j]
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y_curve2 = y_h2[j]
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x_curve1 = x_v1[i]
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x_curve2 = x_v2[i]
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# 判断所在区域
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if i <= y_curve1:
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if j <= x_curve1:
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masks[0][i, j] = 255 # 左上角
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elif j <= x_curve2:
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masks[1][i, j] = 255 # 上中
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else:
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masks[2][i, j] = 255 # 右上角
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elif i <= y_curve2:
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if j <= x_curve1:
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masks[3][i, j] = 255 # 中左
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elif j <= x_curve2:
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masks[4][i, j] = 255 # 中间
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else:
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masks[5][i, j] = 255 # 中右
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else:
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if j <= x_curve1:
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masks[6][i, j] = 255 # 左下角
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elif j <= x_curve2:
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masks[7][i, j] = 255 # 下中
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else:
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masks[8][i, j] = 255 # 右下角
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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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# 应用掩码并保存图片
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for idx, mask in enumerate(masks):
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img_part = cv2.bitwise_and(image, image, mask=mask)
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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。")
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