# 对图像的三个通道进行DWT变换 coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar')这串代码输出的是元祖类型
时间: 2024-02-20 07:58:24 浏览: 25
根据你提供的代码, coeffs_r、coeffs_g 和 coeffs_b 变量是由 pywt.dwt2 函数返回的元组类型,其中包含了多个数组。因此,在计算阈值时,需要对 coeffs_r[0]、coeffs_g[0] 和 coeffs_b[0] 这三个数组进行操作,而不是 coeffs_r[1]、coeffs_g[1] 和 coeffs_b[1]。
你可以尝试将计算阈值的代码修改为以下形式:
```
threshold_r = np.sort(np.abs(coeffs_r[0].ravel()))[::-1][int(compress_ratio * len(coeffs_r[0].ravel()))]
threshold_g = np.sort(np.abs(coeffs_g[0].ravel()))[::-1][int(compress_ratio * len(coeffs_g[0].ravel()))]
threshold_b = np.sort(np.abs(coeffs_b[0].ravel()))[::-1][int(compress_ratio * len(coeffs_b[0].ravel()))]
```
这样就可以从 coeffs_r、coeffs_g 和 coeffs_b 元组中取出正确的数组,并使用 ravel 函数将它们转换为一维数组,然后计算阈值。
相关问题
将coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar')改成输出数组类型
如果你想直接将 coeffs_r、coeffs_g 和 coeffs_b 变量转换为数组类型,可以使用 numpy.array 函数,将它们转换为 numpy 数组类型。你可以尝试在代码中添加以下语句:
```
coeffs_r = np.array(pywt.dwt2(img[:, :, 0], 'haar'))
coeffs_g = np.array(pywt.dwt2(img[:, :, 1], 'haar'))
coeffs_b = np.array(pywt.dwt2(img[:, :, 2], 'haar'))
```
这样就可以将 coeffs_r、coeffs_g 和 coeffs_b 直接转换为 numpy 数组类型,并且不需要再使用 numpy.asarray 函数进行转换。之后,你就可以使用 ravel 函数将它们转换为一维数组,进行计算阈值等操作。
import numpy as np import matplotlib.pyplot as plt import pywt from skimage import io, color # 读取灰度图像并转换为RGB图像 img_gray = io.imread('lena.png', as_gray=True) img = color.gray2rgb(img_gray) # 对图像的三个通道进行DWT变换 coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar') # 设置压缩比率 compress_ratio = 0.5 # 计算阈值 threshold_r = np.sort(np.abs(coeffs_r[1].ravel()))[::-1][int(compress_ratio * len(coeffs_r[1].ravel()))] threshold_g = np.sort(np.abs(coeffs_g[1].ravel()))[::-1][int(compress_ratio * len(coeffs_g[1].ravel()))] threshold_b = np.sort(np.abs(coeffs_b[1].ravel()))[::-1][int(compress_ratio * len(coeffs_b[1].ravel()))] # 对小于阈值的系数进行置零 coeffs_r = list(coeffs_r) coeffs_r[0] = np.round(coeffs_r[0]) coeffs_r[1] = np.where(np.abs(coeffs_r[1]) < threshold_r, 0, coeffs_r[1]) coeffs_r[2] = np.where(np.abs(coeffs_r[2]) < threshold_r, 0, coeffs_r[2]) coeffs_g = list(coeffs_g) coeffs_g[0] = np.round(coeffs_g[0]) coeffs_g[1] = np.where(np.abs(coeffs_g[1]) < threshold_g, 0, coeffs_g[1]) coeffs_g[2] = np.where(np.abs(coeffs_g[2]) < threshold_g, 0, coeffs_g[2]) coeffs_b = list(coeffs_b) coeffs_b[0] = np.round(coeffs_b[0]) coeffs_b[1] = np.where(np.abs(coeffs_b[1]) < threshold_b, 0, coeffs_b[1]) coeffs_b[2] = np.where(np.abs(coeffs_b[2]) < threshold_b, 0, coeffs_b[2]) # 合并三个通道的系数 coeffs = [np.stack([coeffs_r[i], coeffs_g[i], coeffs_b[i]], axis=-1) for i in range(len(coeffs_r))] # 对图像进行IDWT反变换 img_dwt = pywt.idwt2(coeffs, 'haar') # 显示原始图像和压缩后的图像 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) ax = axes.ravel() ax[0].imshow(img) ax[0].set_title("Original image") ax[1].imshow(img_dwt) ax[1].set_title("Compressed image (DWT)") ax[1].set_xlabel("Compression ratio: {:.2f}".format(compress_ratio)) plt.tight_layout() plt.show()
这段代码缺少了两行,分别是导入numpy和matplotlib.pyplot两个模块的语句。请在代码的开头添加以下两行语句:
```
import numpy as np
import matplotlib.pyplot as plt
```
这样就可以解决代码中的报错。