65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
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import numpy as np
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from numpy.fft import fft2, ifft2, fftshift, ifftshift
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import matplotlib.pyplot as plt
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from PIL import Image
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def modify_amplitude_only(image_path, u, v, factor):
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"""
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修改图像B通道在(u, v)位置的频率分量的幅度，并保持相位不变。
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参数:
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image_path: 输入图像的路径。
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u, v: 要修改的频率分量的位置。
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factor: 幅度修改因子。
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返回:
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original_amplitude: 原始幅度。
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modified_amplitude: 修改后的预期幅度。
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current_amplitude: 实际当前幅度。
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"""
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# 读取图像
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image = np.array(Image.open(image_path))
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B_channel = image[:, :, 2] # 提取B通道
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N, M = B_channel.shape
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F = fftshift(fft2(B_channel))
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# 获取原始的幅度和相位
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original_amplitude = np.abs(F[u, v])
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# 修改幅度
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amplitude = np.abs(F)
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phase = np.angle(F)
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amplitude[u, v] *= factor
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amplitude[N - u, M - v] *= factor # 确保共轭对称
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modified_amplitude = amplitude[u, v]
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# 重新构造频域数据
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F_new = amplitude * np.exp(1j * phase)
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modified_B_channel = np.real(ifft2(ifftshift(F_new))) # 执行逆FFT并取实部
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# 合并修改后的B通道回原图
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modified_image = image.copy()
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modified_image[:, :, 2] = modified_B_channel.astype(np.float64)
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# 从修改后的完整图像中提取B通道并进行DFT
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modified_B_channel_from_image = modified_image[:, :, 2]
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F_check = fftshift(fft2(modified_B_channel_from_image))
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current_amplitude = np.abs(F_check[u, v])
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return original_amplitude, modified_amplitude, current_amplitude
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# 示例使用
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image_path = 'demo_border.png' # 你的图像路径
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u, v = 128, 128 # 选择要修改的频点位置
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factor = 20 # 幅度增加因子
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# 修改图像并获取数据
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original_amplitude, modified_amplitude, current_amplitude = modify_amplitude_only(image_path, u, v, factor)
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# 输出幅度数据对比
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print(f"Original Amplitude at ({u}, {v}): {original_amplitude}")
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print(f"Modified Amplitude (expected) at ({u}, {v}): {modified_amplitude}")
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print(f"Current Amplitude (after modification and IFFT) at ({u}, {v}): {current_amplitude}")
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