我想在以下这段代码中，添加显示标有特征点的图像的功能。def cnn_feature_extract(image,scales=[.25, 0.50, 1.0], nfeatures = 1000): if len(image.shape) == 2: image = image[:, :, np.newaxis] image = np.repeat(image, 3, -1) # TODO: switch to PIL.Image due to deprecation of scipy.misc.imresize. resized_image = image if max(resized_image.shape) > max_edge: resized_image = scipy.misc.imresize( resized_image, max_edge / max(resized_image.shape) ).astype('float') if sum(resized_image.shape[: 2]) > max_sum_edges: resized_image = scipy.misc.imresize( resized_image, max_sum_edges / sum(resized_image.shape[: 2]) ).astype('float') fact_i = image.shape[0] / resized_image.shape[0] fact_j = image.shape[1] / resized_image.shape[1] input_image = preprocess_image( resized_image, preprocessing="torch" ) with torch.no_grad(): if multiscale: keypoints, scores, descriptors = process_multiscale( torch.tensor( input_image[np.newaxis, :, :, :].astype(np.float32), device=device ), model, scales ) else: keypoints, scores, descriptors = process_multiscale( torch.tensor( input_image[np.newaxis, :, :, :].astype(np.float32), device=device ), model, scales ) # Input image coordinates keypoints[:, 0] *= fact_i keypoints[:, 1] *= fact_j # i, j -> u, v keypoints = keypoints[:, [1, 0, 2]] if nfeatures != -1: #根据scores排序 scores2 = np.array([scores]).T res = np.hstack((scores2, keypoints)) res = res[np.lexsort(-res[:, ::-1].T)] res = np.hstack((res, descriptors)) #取前几个 scores = res[0:nfeatures, 0].copy() keypoints = res[0:nfeatures, 1:4].copy() descriptors = res[0:nfeatures, 4:].copy() del res return keypoints, scores, descriptors

可以使用OpenCV库中的cv2.drawKeypoints()函数来显示标有特征点的图像。具体实现如下：

1. 导入OpenCV库：import cv2

2. 在函数中添加以下代码，绘制特征点：

img_with_keypoints = cv2.drawKeypoints(image, keypoints, np.array([]), (255,0,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("Image with Keypoints", img_with_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码将在窗口中显示标有特征点的图像。注意，要在函数中添加完整的代码，包括导入OpenCV库等。

相关问题

import pySST def nsst_decomposition(image, num_scales=4): # 将图像转换为灰度图像（如果不是灰度图像） if len(image.shape) > 2: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 执行NSST分解 nsst = pySST.SST() nsst.setScales(num_scales) coeffs = nsst.forward(image) return coeffs def nsst_reconstruction(coeffs): # 执行NSST逆变换 nsst = pySST.SST() reconstructed_image = nsst.inverse(coeffs) return reconstructed_image这里面的pySST模块是什么

根据代码，`pySST`是一个用于执行NSST分解和逆变换的Python模块。NSST是非对称时-频分解（Nonsubsampled Shearlet Transform）的缩写，是一种多尺度变换技术，可以用于图像处理和分析。该模块可能是基于NSST算法实现的，提供了用于执行NSST分解和逆变换的函数。

import numpy as np import pywt from scipy.fftpack import dct def nsst(image): # 将图像转换为灰度图像 def nsst(image): # 将图像转换为灰度图像 if len(image.shape) == 3: image = np.mean(image, axis=2) # 设置NSST参数 scales = 3 shearlet_system = pywt.ShearletSystem2D(image.shape, scales) # 计算图像的NSST分解系数 coeffss = [] for scale in range(scales): for shear in range(shearlet_system.shear_count): shearlet = shearlet_system.shearlets[scale][shear] shearlet_fft = np.fft.fft2(shearlet, image.shape) shearlet_fft_conj = np.conj(shearlet_fft) image_fft = np.fft.fft2(image) shearlet_coeff = np.fft.ifft2(shearlet_fft_conj * image_fft) coeffss.append(shearlet_coeff) # 将NSST分解系数组合成一个张量 coeffs = np.stack(coeffss, axis=-1) return coeffs coeffs1 = nsst_decomposition(image1, num_scales=4) coeffs2 = nsst_decomposition(image2, num_scales=4)

根据你提供的代码，错误信息提示 'nsst_decomposition' is not defined，说明你的代码中不存在名为 'nsst_decomposition' 的函数。你需要定义这个函数或导入包含这个函数的模块。在你的代码中，应该将函数名 'nsst' 替换为 'nsst_decomposition'，以便正确地调用函数。你可以尝试修改代码如下：

import numpy as np 
import pywt 
from scipy.fftpack import dct 

def nsst_decomposition(image):    
    # 将图像转换为灰度图像     
    if len(image.shape) == 3:         
        image = np.mean(image, axis=2)          

    # 设置NSST参数         
    scales = 3         
    shearlet_system = pywt.ShearletSystem2D(image.shape, scales)          

    # 计算图像的NSST分解系数         
    coeffss = []         
    for scale in range(scales):             
        for shear in range(shearlet_system.shear_count):                 
            shearlet = shearlet_system.shearlets[scale][shear]                 
            shearlet_fft = np.fft.fft2(shearlet, image.shape)                 
            shearlet_fft_conj = np.conj(shearlet_fft)                 
            image_fft = np.fft.fft2(image)                 
            shearlet_coeff = np.fft.ifft2(shearlet_fft_conj * image_fft)                 
            coeffss.append(shearlet_coeff)          

    # 将NSST分解系数组合成一个张量         
    coeffs = np.stack(coeffss, axis=-1)          

    return coeffs  

coeffs1 = nsst_decomposition(image1) 
coeffs2 = nsst_decomposition(image2) 

这样应该可以解决该错误。