defgaussian_filter(data,sigma):""" 对一维数据应用高斯滤波 :param data: 输入的原始数据 :param sigma: 高斯核的标准差 :return: 滤波后的数据 """returngaussian_filter1d(data,sigma) 1. 2. 3. 4. 5. 6. 7. 8. sigma参数决定高斯核的宽度,值越大,滤波效果越
importnumpyasnpimportmatplotlib.pyplotaspltdefgaussian_filter_1d(sigma,size):radius=size//2x=np.arange(-radius,radius+1,1)gauss=np.exp(-0.5*(x**2/sigma**2))returngauss/gauss.sum()# 示例数据data=np.random.rand(100)# 生成随机信号filtered_data=np.convolve(data,gaussian_filter_1d(1,5),mo...
解决方法:调整gaussian_filter1d函数中的sigma参数,增加平滑程度。 日期格式不正确: 问题:日期格式不符合预期。 解决方法:使用mdates.DateFormatter设置正确的日期格式。 示例代码 代码语言:txt 复制 import matplotlib.pyplot as plt import matplotlib.dates as mdates import pandas as pd import numpy as ...
window_size=9) coordinates_warped_subpix = corner_subpix(image_warped_gray, coordinates_warped, window_size=9) def gaussian_weights(window_ext, sigma=1): y, x = np.mgrid[-window_ext:window_ext+1, -window_ext:window_ext+1] g_w = np.zeros(y.shape, dtype = np.double) g_w[:] ...
= 1, # Sigma value for the Qt in Equation (1) the Gaussian distribution observation_covariance=1, # Sigma value for the Rt in Equation (2) the Gaussian distribution transition_covariance=.01) # A small turbulence in the random walk parameter 1.0 # Get the Kalman smoothing state_means, _...
im = rgb2gray(imread('../images/tiger3.jpg')) im = ndimage.gaussian_filter(im, 4) im += 0.05 * np.random.random(im.shape) edges1 = feature.canny(im) edges2 = feature.canny(im, sigma=3) fig, (axes1, axes2, axes3) = pylab.subplots(nrows=1, ncols=3, figsize=(30, 12), ...
接下来,我们将填补 CUDA-C 编程中的一些空白,以便我们可以直接在 NVIDIA Nsight IDE 中编写 CUDA 程序,这将允许我们为我们一直在编写的一些代码创建 CUDA-C 的测试用例。我们将看看如何使用nvcc命令行编译 CUDA-C 程序,以及如何在 Nsight IDE 中进行编译。然后,我们将看看如何在 Nsight 中进行调试,并使用 Nsight...
im = rgb2gray(imread('../images/tiger3.jpg')) im = ndimage.gaussian_filter(im, 4) im += 0.05 * np.random.random(im.shape) edges1 = feature.canny(im) edges2 = feature.canny(im, sigma=3) fig, (axes1, axes2, axes3) = pylab.subplots(nrows=1, ncols=3, figsize=(30, 12), ...
This subpackage contains various functions for multi-dimensional image processing, including convolution and assorted linear and nonlinear filters (Gaussian filter, median filter, Sobel filter and others); interpolation; region labeling and processing; and mathematical morphology functions. ...
importtorchimporttorch.nn.functionalasFimportnumpyasnpfromtorch.autogradimportVariablefrommathimportexpfromPILimportImagedefgaussian(window_size,sigma):gauss=torch.Tensor([exp(-(x-window_size//2)**2/float(2*sigma**2))forxinrange(window_size)])returngauss/gauss.sum()defcreate_window(window_size,...