"""1. Getting Started with Pre-trained Model on CIFAR10
=======================================================

`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`__ is a
dataset of tiny (32x32) images with labels, collected by Alex Krizhevsky,
Vinod Nair, and Geoffrey Hinton. It is widely used as benchmark in
computer vision research.

|image-cifar10|

.. |image-cifar10| image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/datasets/cifar10.png

In this tutorial, we will demonstrate how to load a pre-trained model from :ref:`gluoncv-model-zoo`
and classify images from the Internet or your local disk.

Step by Step
------------------

Let's first try out a pre-trained cifar model with a few lines of python code.

First, please follow the `installation guide <../../index.html#installation>`__
to install ``MXNet`` and ``GluonCV`` if you haven't done so yet.
"""

import matplotlib.pyplot as plt

from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
from gluoncv import utils
from gluoncv.model_zoo import get_model

################################################################
#
# Then, we download and show the example image:

url = 'https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/plane-draw.jpeg'
im_fname = utils.download(url)

img = image.imread(im_fname)

plt.imshow(img.asnumpy())
plt.show()

################################################################
# In case you don't recognize it, the image is a poorly-drawn airplane :)
#
# Now we define transformations for the image.

transform_fn = transforms.Compose([
    transforms.Resize(32),
    transforms.CenterCrop(32),
    transforms.ToTensor(),
    transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])

################################################################
# This transformation function does three things:
# resize and crop the image to 32x32 in size,
# transpose it to `num_channels*height*width`,
# and normalize with mean and standard deviation calculated across all CIFAR10 images.
#
# What does the transformed image look like?

img = transform_fn(img)
plt.imshow(nd.transpose(img, (1,2,0)).asnumpy())
plt.show()

################################################################
# Can't recognize anything? *Don't panic!* Neither do I.
# The transformation makes it more "model-friendly", instead of "human-friendly".
#
# Next, we load a pre-trained model.

net = get_model('cifar_resnet110_v1', classes=10, pretrained=True)

################################################################
#
# Finally, we prepare the image and feed it to the model

pred = net(img.expand_dims(axis=0))

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']
ind = nd.argmax(pred, axis=1).astype('int')
print('The input picture is classified as [%s], with probability %.3f.'%
      (class_names[ind.asscalar()], nd.softmax(pred)[0][ind].asscalar()))

################################################################
# Play with the scripts
# ---------------------
#
# Here is a script that does all the previous steps in one go.
#
# :download:`Download demo_cifar10.py<../../../scripts/classification/cifar/demo_cifar10.py>`
#
# Feed in your own image to see how well it does the job.
# Keep in mind that ``CIFAR10`` is a small dataset with only 10
# classes. Models trained on ``CIFAR10`` only recognize objects from those
# 10 classes. Thus, it may surprise you if we feed one image to the model
# which doesn't belong to any of the 10 classes
#
# For instance we can test it with the following photo of Mt. Baker:
#
# |image-mtbaker|
#
# ::
#
#     python demo_cifar10.py --model cifar_resnet110_v1 --input-pic mt_baker.jpg
#
# The result is:
#
# ::
#
#     The input picture is classified to be [ship], with probability 0.949.
#
# Next Step
# ---------
#
# Congratulations! You’ve just finished reading the first tutorial.
# There are a lot more to help you learn GluonCV.
#
# If you would like to dive deeper into training on ``CIFAR10``,
# feel free to read the next `tutorial on CIFAR10 <dive_deep_cifar10.html>`__.
#
# Or, if you would like to try a larger scale dataset with 1000 classes of common objects
# please read `Getting Started with ImageNet Pre-trained Models <demo_imagenet.html>`__.
#
# .. |image-mtbaker| image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/mt_baker.jpg