labels.npy文件查看
1 import numpy as np 2 matrix = np.load('./V1/labels.npy') 3 4 print(matrix) 5 print(matrix.shape) 6 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
1 import numpy as np 2 import torchvision.transforms as transforms 3 from PIL import Image 4 5 depth_dataset = np.load('./V1/labels.npy') 6 # print(dataset.shape) 7 8 def __scale_width(img, target_high): 9 ow, oh = img.size 10 if (ow == target_high): 11 return img 12 h = target_high 13 w = int(target_high * ow / oh) 14 return img.resize((w, h), Image.BICUBIC) 15 16 for i in range(10): #dataset.shape[2] 17 depth = depth_dataset[:, :, i]#2284 18 19 #归一化 20 depth_min = depth.min() 21 depth_max = depth.max() 22 depth = ((depth-depth_min)/(depth_max-depth_min))*255 23 24 #还原图片 25 images = Image.fromarray(depth) 26 27 if images.mode != 'RGB': 28 images = images.convert('RGB') 29 30 # images.show() 31 #压缩图片 32 scale = __scale_width(images, 256) 33 # print(scale.size) 34 # scale.show() 35 36 #裁剪图片 37 crop = transforms.RandomCrop((256, 256)) 38 crop_img = crop(scale) 39 40 # crop_img.show() 41 #保存图片 42 crop_img.save('./V1Labels/%d.jpg' %i)