.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "build/examples_detection/demo_ssd.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_build_examples_detection_demo_ssd.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_build_examples_detection_demo_ssd.py:

01. Predict with pre-trained SSD models
==========================================

This article shows how to play with pre-trained SSD models with only a few
lines of code.

First let's import some necessary libraries:

.. GENERATED FROM PYTHON SOURCE LINES 9-13

.. code-block:: default


    from gluoncv import model_zoo, data, utils
    from matplotlib import pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 14-22

Load a pretrained model
-------------------------

Let's get an SSD model trained with 512x512 images on Pascal VOC
dataset with ResNet-50 V1 as the base model. By specifying
``pretrained=True``, it will automatically download the model from the model
zoo if necessary. For more pretrained models, please refer to
:doc:`../../model_zoo/index`.

.. GENERATED FROM PYTHON SOURCE LINES 22-25

.. code-block:: default


    net = model_zoo.get_model('ssd_512_resnet50_v1_voc', pretrained=True)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
            data: None
      input_sym_arg_type = in_param.infer_type()[0]
    Downloading /root/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...

      0%|          | 0/132723 [00:00<?, ?KB/s]
      0%|          | 102/132723 [00:00<02:50, 776.13KB/s]
      0%|          | 515/132723 [00:00<01:01, 2153.95KB/s]
      2%|1         | 2184/132723 [00:00<00:18, 6872.72KB/s]
      6%|5         | 7459/132723 [00:00<00:05, 21937.22KB/s]
     10%|9         | 13000/132723 [00:00<00:03, 32519.11KB/s]
     16%|#5        | 20998/132723 [00:00<00:02, 47353.06KB/s]
     21%|##        | 27272/132723 [00:00<00:02, 52010.10KB/s]
     26%|##5       | 34107/132723 [00:00<00:01, 57003.10KB/s]
     31%|###1      | 41248/132723 [00:01<00:01, 61167.62KB/s]
     37%|###6      | 48817/132723 [00:01<00:01, 65551.47KB/s]
     42%|####2     | 55878/132723 [00:01<00:01, 66907.14KB/s]
     48%|####7     | 63675/132723 [00:01<00:00, 70230.01KB/s]
     54%|#####3    | 71044/132723 [00:01<00:00, 71082.41KB/s]
     59%|#####9    | 78794/132723 [00:01<00:00, 73003.15KB/s]
     65%|######4   | 86212/132723 [00:01<00:00, 73073.51KB/s]
     71%|#######   | 93875/132723 [00:01<00:00, 74135.73KB/s]
     76%|#######6  | 101304/132723 [00:01<00:00, 73462.10KB/s]
     82%|########2 | 109276/132723 [00:01<00:00, 75321.61KB/s]
     88%|########8 | 116819/132723 [00:02<00:00, 73789.12KB/s]
     94%|#########4| 124842/132723 [00:02<00:00, 75683.59KB/s]
    100%|#########9| 132424/132723 [00:02<00:00, 74238.46KB/s]
    100%|##########| 132723/132723 [00:02<00:00, 59593.56KB/s]




.. GENERATED FROM PYTHON SOURCE LINES 26-42

Pre-process an image
--------------------

Next we download an image, and pre-process with preset data transforms. Here we
specify that we resize the short edge of the image to 512 px. But you can
feed an arbitrarily sized image.

You can provide a list of image file names, such as ``[im_fname1, im_fname2,
...]`` to :py:func:`gluoncv.data.transforms.presets.ssd.load_test` if you
want to load multiple image together.

This function returns two results. The first is a NDArray with shape
`(batch_size, RGB_channels, height, width)`. It can be fed into the
model directly. The second one contains the images in numpy format to
easy to be plotted. Since we only loaded a single image, the first dimension
of `x` is 1.

.. GENERATED FROM PYTHON SOURCE LINES 42-49

.. code-block:: default


    im_fname = utils.download('https://github.com/dmlc/web-data/blob/master/' +
                              'gluoncv/detection/street_small.jpg?raw=true',
                              path='street_small.jpg')
    x, img = data.transforms.presets.ssd.load_test(im_fname, short=512)
    print('Shape of pre-processed image:', x.shape)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Downloading street_small.jpg from https://github.com/dmlc/web-data/blob/master/gluoncv/detection/street_small.jpg?raw=true...

      0%|          | 0/116 [00:00<?, ?KB/s]
    117KB [00:00, 36875.08KB/s]            
    Shape of pre-processed image: (1, 3, 512, 512)




.. GENERATED FROM PYTHON SOURCE LINES 50-60

Inference and display
---------------------

The forward function will return all detected bounding boxes, and the
corresponding predicted class IDs and confidence scores. Their shapes are
`(batch_size, num_bboxes, 1)`, `(batch_size, num_bboxes, 1)`, and
`(batch_size, num_bboxes, 4)`, respectively.

We can use :py:func:`gluoncv.utils.viz.plot_bbox` to visualize the
results. We slice the results for the first image and feed them into `plot_bbox`:

.. GENERATED FROM PYTHON SOURCE LINES 60-66

.. code-block:: default


    class_IDs, scores, bounding_boxes = net(x)

    ax = utils.viz.plot_bbox(img, bounding_boxes[0], scores[0],
                             class_IDs[0], class_names=net.classes)
    plt.show()



.. image-sg:: /build/examples_detection/images/sphx_glr_demo_ssd_001.png
   :alt: demo ssd
   :srcset: /build/examples_detection/images/sphx_glr_demo_ssd_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  5.051 seconds)


.. _sphx_glr_download_build_examples_detection_demo_ssd.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: demo_ssd.py <demo_ssd.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: demo_ssd.ipynb <demo_ssd.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_