def show(self, ax=None, figsize:tuple=(3,3), title=None, hide_axis:bool=True,
cmap=None, alpha:float=0.5, **kwargs):
"Show the `ImageSegment` on `ax`."
if is_no_color(self.color_mapping):
## This condition will not be true.
ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap="tab20", figsize=figsize,
interpolation='nearest', alpha=alpha, vmin=0, **kwargs)
else:
color_mapping = torch.tensor(list(self.color_mapping.values()))
color_mapping = torch.cat((color_mapping.float()/255, torch.tensor([float(alpha)] * len(color_mapping)).view(-1, 1)), dim=1)
color_mapping = torch.cat((torch.tensor([0., 0., 0., 0.]).view(1, -1), color_mapping), dim=0)
ax = show_image(color_mapping[self.data[0]].permute(2, 0, 1), ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
interpolation='nearest', alpha=alpha, vmin=0, **kwargs)
if title: ax.set_title(title)
def show(self,
ax=None,
figsize: tuple = (3, 3),
title=None,
hide_axis: bool = True,
cmap='tab20',
alpha: float = 0.5,
**kwargs):
"Show the `ImageSegment` on `ax`."
ax = show_image(self,
ax=ax,
hide_axis=hide_axis,
cmap=self.cmap,
figsize=figsize,
interpolation='nearest',
alpha=alpha,
vmin=0,
norm=self.mplnorm,
**kwargs)
if title: ax.set_title(title)
from fastai.docs import untar_data, image_data_from_folder, rand_pad, DOGS_PATH, MNIST_PATH, accuracy
from fastai.vision import ConvLearner, get_transforms, imagenet_norm
from fastai.vision.image import show_image
from fastai.vision import tvm
from matplotlib import pyplot as plt
arch = tvm.resnet34
sz = 224 # image size
# 下载数据集
untar_data(DOGS_PATH)
data = image_data_from_folder(DOGS_PATH,
ds_tfms=get_transforms(),
tfms=imagenet_norm,
size=sz)
# 显示一张图片
img, label = data.train_ds[0]
show_image(img)
plt.show()
# 训练第一个fastai的模型，使用预训练的模型
learner = ConvLearner(data, arch, metrics=accuracy)
learner.fit(1)