1. Rescale :缩放图片 class Rescale(object): """将样本中的图像重新缩放到给定大小。. Args: output_size(tuple或int):所需的输出大小。 如果是元组,则输出为 与output_size匹配。 如果是int,则匹配较小的图像边缘到output_size保持纵横比相同。 """ def __init__(self, output_size): assert isinstance...
composed = transforms.Compose([Rescale(256), RandomCrop(224)]) # 对图片数据调用上述 3 种形式预处理方法,即单独使用 Rescale,RandomCrop,组合使用 Rescale和 RandomCrop fig = plt.figure() sample = face_dataset[65] for i, tsfrm in enumerate([scale, crop, composed]): transformed_sample = tsfrm...
我们需要将demo中使用的样本图片较短的边设置为256,之后将图片裁剪为244x244大小。为此我们需要组合Rescale和RandomCrop两种变换。可以通过torchvision.transforms.Compose实现。这是一个可调用类。 scale = Rescale(256) crop = RandomCrop(128) composed = transforms.Compose([Rescale(256), RandomCrop(224)]...
class Rescale(object): # 第一个类规范图像尺寸 """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same...
classRescale(object): # 第一个类规范图像尺寸"""Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched ...
Rescale:将图像重新缩放至所需尺寸。 RandomCrop:随机裁剪图像。 ToTensor:将numpy图片转换为torch图像。 代码语言:javascript 复制 # test out someofthese transforms rescale=Rescale(100)crop=RandomCrop(50)composed=transforms.Compose([Rescale(250),RandomCrop(224)])# apply the transforms to a sample image ...
class Rescale(object): """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same. """ def __init...
(image_path)# BGR orderh, w, c = img.shape# rescaleimg = cv.resize(img, (64,64))img = (np.float32(img) /255.0-0.5) /0.5# H, W C to C, H, Wimg = img.transpose((2,0,1))sample = {'image': torch.from_numpy(img),'age':self...
classRescale(object):"""Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same. ...
transformed_dataset = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv', root_dir='data/faces/', transform=transforms.Compose([ Rescale(256), RandomCrop(224), ToTensor() ]))foriinrange(len(transformed_dataset)): sample = transformed_dataset[i]print(i, sample['image'].size(...