importnumpyasnp pic=imageio.imread('F:/demo_2.jpg') fig,ax=plt.subplots(nrows=1,ncols=3,figsize=(15,5)) forc,axinzip(range(3),ax): # create zero matrix split_img=np.zeros(pic.shape,dtype="uint8")# 'dtype' by default: 'numpy.float64' # assing each channel split_img[:,:...
Create Multi-dimensional Arrays with Zeros When working with data science projects, I often need arrays with more than one dimension. To create multi-dimensional arrays with zeros, simplypass a tuplewith the desired dimensions: # Create a 2D array (matrix) with 3 rows and 4 columns matrix = ...
fig,ax=plt.subplots(nrows=1,ncols=3,figsize=(15,5)) forc,axinzip(range(3),ax):# create zero matrixsplit_img=np.zeros(pic.shape,dtype="uint8")#'dtype'by default:'numpy.float64'# assing each channel split_img[:,:,c]=pic[:,:,c]# display each channelax.imshow(split_img) 灰度...
Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) 创建一个8*8矩阵,并用棋盘图案填充 import numpy as npz = np.zeros((8,8),dtype=int)z[1::2,::2] = 1 # 从第 2 行开始,每隔一行,第 0 列开始,每隔一列赋值为 1z[::2,1::2] = 1 # 从第 0 行开始,每隔一行,...
dot(sepia_matrix.T) # Using matrix multiplication # Ensure values are within valid range [0, 255] sepia_img = np.clip(sepia_img, 0, 255) return sepia_img.astype(np.uint8) # Apply sepia effect M_sepia = Image.fromarray(apply_sepia(reduced_M)) display(M_sepia) 7、灰度化 灰度化可以...
importnumpyasnp# 创建单位矩阵identity_matrix=np.eye(4)print(identity_matrix) Python Copy Output: 7. 使用np.random生成随机数数组 Numpy 提供了多种生成随机数数组的方法。这些方法可以用来创建具有随机元素的数组。 示例代码 10:创建随机浮点数数组
same typeas`a`isreturned unless `a`isa `matrix`,inwhichcasea1-D array rather than a (2-D) `matrix`isreturnedinordertomaintain backward compatibility.If``a.ndim >2``,thenthe dimensions specifiedby`axis1`and`axis2` are removed,andanewaxis inserted at theendcorrespondingtothe ...
matrix=diamonds.corr() mask=np.triu(np.ones_like(matrix,dtype=bool)) sns.heatmap(matrix,square=True, mask=mask,annot=True, fmt=".2f",center=0); 如你所见,用 triu 创建的掩码可以用在相关矩阵上,去掉不必要的上三角形和对角线。这使得热图更加紧凑,可读性更强。
Non-negative matrix factorization Preprocessing Discrete Fourier transform (1D signals) Discrete cosine transform (type-II) (1D signals) Bilinear interpolation (2D signals) Nearest neighbor interpolation (1D and 2D signals) Autocorrelation (1D signals) Signal windowing Text tokenization Feature hashing Fea...
A = np.array([[1, 1], [0, 1]]) B = np.array([[2, 0], [3, 4]]) A * B # elementwise product array([[2, 0], [0, 4]]) A @ B # matrix product array([[5, 4], [3, 4]]) A.dot(B) # another matrix product array([[5, 4], [3, 4]]) ...