matplotlib.pyplot鐢ㄦ硶_matplotlib.pyplot.imshow?

matplotlib.pyplot.imread(path)用于读取一张图片，将图像数据变成数组array.

要读取的图像文件路径。

如果是灰度图：返回(M,N)形状的数组，M表示高度，N表示宽度。

如果是RGB图像，返回(M, N, 3) 形状的数组，M表示高度，N表示宽度。

如果是RGBA图像，返回(M, N, 4) 形状的数组，M表示高度，N表示宽度。

此外，PNG 图像以浮点数组 (0-1) 的形式返回，所有其他格式都作为 int 型数组返回，位深由具体图像决定。

因此，在pytorch中如果需要读取文件，则需要通过 .permute(2, 0, 1) 将图像由HWC->CHW

		>>> file_path="D:/Scientific Research/BRDF/Dataset/2021/subtrain/0000007;metal_lead_roughXconcrete_010;1X4.png"
		>>> import matplotlib.pyplot as plt
		>>> import torch
		>>> plt.imread(file_path)
		array([[[0.02352941, 0.02352941, 0.02352941],
		        [0.01960784, 0.01960784, 0.01960784],
		        [0.01568628, 0.01568628, 0.01568628],
		        ...,
		       
		        [0.04313726, 0.04313726, 0.04313726],
		        [0.04313726, 0.04313726, 0.04313726],
		        [0.04313726, 0.03921569, 0.03921569]]], dtype=float32)
		
		>>> full_image   = torch.Tensor(plt.imread(file_path)).permute(2, 0, 1)
		>>> print(full_image[0])
		tensor([[0.0235, 0.0196, 0.0157,  ..., 0.0510, 0.0588, 0.0549],
		        [0.0235, 0.0196, 0.0157,  ..., 0.0588, 0.0549, 0.0471],
		        [0.0431, 0.0353, 0.0235,  ..., 0.0510, 0.0549, 0.0549],
		        ...,
		        [0.0745, 0.0784, 0.0706,  ..., 0.0431, 0.0431, 0.0431],
		        [0.0745, 0.0784, 0.0784,  ..., 0.0431, 0.0431, 0.0431],
		        [0.0667, 0.0667, 0.0745,  ..., 0.0431, 0.0431, 0.0431]])
		>>> print(full_image[1])
		tensor([[0.0235, 0.0196, 0.0157,  ..., 0.0510, 0.0588, 0.0549],
		        [0.0235, 0.0196, 0.0157,  ..., 0.0549, 0.0549, 0.0471],
		        [0.0431, 0.0353, 0.0235,  ..., 0.0510, 0.0549, 0.0549],
		        ...,
		        [0.0667, 0.0706, 0.0667,  ..., 0.0431, 0.0431, 0.0392],
		        [0.0667, 0.0706, 0.0745,  ..., 0.0431, 0.0431, 0.0392],
		        [0.0588, 0.0627, 0.0706,  ..., 0.0431, 0.0431, 0.0392]])
		>>> print(full_image[2])
		tensor([[0.0235, 0.0196, 0.0157,  ..., 0.0471, 0.0549, 0.0510],
		        [0.0196, 0.0196, 0.0157,  ..., 0.0510, 0.0549, 0.0471],
		        [0.0392, 0.0314, 0.0196,  ..., 0.0471, 0.0510, 0.0510],
		        ...,
		        [0.0627, 0.0667, 0.0588,  ..., 0.0431, 0.0431, 0.0392],
		        [0.0627, 0.0667, 0.0667,  ..., 0.0431, 0.0431, 0.0392],
		        [0.0549, 0.0549, 0.0627,  ..., 0.0431, 0.0431, 0.0392]])
		>>> print(full_image.shape)
		torch.Size([3, 288, 1440])
		>>> torch.max(full_image)
		tensor(1.)
		>>> torch.min(full_image)
		tensor(0.)
		>>>