二维深度图像矩阵转回图片

labels.npy文件查看

import numpy as np
matrix = np.load('./V1/labels.npy')

print(matrix)
print(matrix.shape)
print(type(matrix))

1、print(matrix)

2、print(matrix.shape)

(480, 640, 2284)

3、print(type(matrix))

<class 'numpy.ndarray'>

转换、压缩、裁剪图片

import numpy as np
import  torchvision.transforms as transforms
from PIL import Image

depth_dataset = np.load('./V1/labels.npy')
# print(dataset.shape)

def __scale_width(img, target_high):
    ow, oh = img.size
    if (ow == target_high):
        return img
    h = target_high
    w = int(target_high * ow / oh)
    return img.resize((w, h), Image.BICUBIC)

for i in range(10):  #dataset.shape[2]
    depth = depth_dataset[:, :, i]#2284

    #归一化
    depth_min = depth.min()
    depth_max = depth.max()
    depth = ((depth-depth_min)/(depth_max-depth_min))*255

    #还原图片
    images = Image.fromarray(depth)

    if images.mode != 'RGB':
        images = images.convert('RGB')

    # images.show()
    #压缩图片
    scale = __scale_width(images, 256)
    # print(scale.size)
    # scale.show()

    #裁剪图片
    crop = transforms.RandomCrop((256, 256))
    crop_img = crop(scale)

    # crop_img.show()
    #保存图片
    crop_img.save('./V1Labels/%d.jpg' %i)