Added PresentImages process to show images in GUI

examples/cuda_convnet/cifar10_spiking_cnn.py

@@ -7,7 +7,7 @@
 
 from nengo_extras.data import load_cifar10
 from nengo_extras.cuda_convnet import CudaConvnetNetwork, load_model_pickle
-from nengo_extras.gui import image_display_function
+from nengo_extras.gui import PresentImages
 
 (X_train, y_train), (X_test, y_test), label_names = load_cifar10(label_names=True)
 X_train = X_train.reshape(-1, 3, 32, 32).astype('float32')
@@ -30,19 +30,15 @@
 
 model = nengo.Network()
 with model:
-    u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
+    u = nengo.Node(PresentImages(X_test, presentation_time))
+    u.output.configure_display(offset=data_mean, scale=1.)
+
     ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.005))
     nengo.Connection(u, ccnet.inputs['data'], synapse=None)
 
     input_p = nengo.Probe(u)
     output_p = nengo.Probe(ccnet.output)
 
-    # --- image display
-    image_shape = X_test.shape[1:]
-    display_f = image_display_function(image_shape, scale=1, offset=data_mean)
-    display_node = nengo.Node(display_f, size_in=u.size_out)
-    nengo.Connection(u, display_node, synapse=None)
-
     # --- output spa display
     vocab_names = [s.upper() for s in label_names]
     vocab_vectors = np.eye(len(vocab_names))

examples/cuda_convnet/imagenet_spiking_cnn.py

@@ -9,7 +9,7 @@
 
 from nengo_extras.data import load_ilsvrc2012, spasafe_names
 from nengo_extras.cuda_convnet import CudaConvnetNetwork, load_model_pickle
-from nengo_extras.gui import image_display_function
+from nengo_extras.gui import PresentImages
 
 # retrieve from https://figshare.com/s/cdde71007405eb11a88f
 filename = 'ilsvrc-2012-batches-test3.tar.gz'
@@ -34,18 +34,15 @@
 
 model = nengo.Network()
 with model:
-    u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
+    u = nengo.Node(PresentImages(X_test, presentation_time))
+    u.output.configure_display(offset=data_mean, scale=1.)
+
     ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.001))
     nengo.Connection(u, ccnet.inputs['data'], synapse=None)
 
     # input_p = nengo.Probe(u)
     output_p = nengo.Probe(ccnet.output)
 
-    # --- image display
-    display_f = image_display_function(image_shape, scale=1., offset=data_mean)
-    display_node = nengo.Node(display_f, size_in=u.size_out)
-    nengo.Connection(u, display_node, synapse=None)
-
     # --- output spa display
     vocab_names = spasafe_names(label_names)
     vocab_vectors = np.eye(len(vocab_names))

examples/keras/mnist_spiking_cnn.py

@@ -15,7 +15,7 @@
 import nengo
 from nengo_extras.keras import (
     load_model_pair, save_model_pair, SequentialNetwork, SoftLIF)
-from nengo_extras.gui import image_display_function
+from nengo_extras.gui import PresentImages
 
 filename = 'mnist_spiking_cnn'
 
@@ -88,19 +88,13 @@
 
 model = nengo.Network()
 with model:
-    u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
+    u = nengo.Node(PresentImages(X_test, presentation_time))
     seq = SequentialNetwork(kmodel, synapse=nengo.synapses.Alpha(0.005))
     nengo.Connection(u, seq.input, synapse=None)
 
     input_p = nengo.Probe(u)
     output_p = nengo.Probe(seq.output)
 
-    # --- image display
-    image_shape = kmodel.input_shape[1:]
-    display_f = image_display_function(image_shape)
-    display_node = nengo.Node(display_f, size_in=u.size_out)
-    nengo.Connection(u, display_node, synapse=None)
-
     # --- output spa display
     vocab_names = ['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR',
                    'FIVE', 'SIX', 'SEVEN', 'EIGHT', 'NINE']

examples/vision/mnist_single_layer_gui.py

@@ -8,7 +8,7 @@
 
 from nengo_extras.data import load_mnist
 from nengo_extras.vision import Gabor, Mask
-from nengo_extras.gui import image_display_function
+from nengo_extras.gui import PresentImages
 
 
 def one_hot(labels, c=None):
@@ -57,20 +57,17 @@ def one_hot(labels, c=None):
 presentation_time = 0.1
 
 with nengo.Network(seed=3) as model:
-    u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
+    u = nengo.Node(PresentImages(X_test.reshape((-1, 1, 28, 28)),
+                                 presentation_time))
+    u.output.configure_display(offset=1., scale=128.)
+
     a = nengo.Ensemble(n_hid, n_vis, **ens_params)
     v = nengo.Node(size_in=n_out)
     nengo.Connection(u, a, synapse=None)
     conn = nengo.Connection(
         a, v, synapse=None,
         eval_points=X_train, function=train_targets, solver=solver)
 
-    # --- image display
-    image_shape = (1, 28, 28)
-    display_f = image_display_function(image_shape, offset=1, scale=128)
-    display_node = nengo.Node(display_f, size_in=u.size_out)
-    nengo.Connection(u, display_node, synapse=None)
-
     # --- output spa display
     vocab_names = ['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR',
                    'FIVE', 'SIX', 'SEVEN', 'EIGHT', 'NINE']

nengo_extras/gui.py

@@ -1,48 +1,113 @@
-def preprocess_display(x, image_shape,
-                       transpose=(1, 2, 0), scale=255., offset=0.):
+from __future__ import absolute_import
+
+import nengo
+
+from .convnet import ShapeParam
+
+
+def preprocess_display(x, transpose=(1, 2, 0), scale=255., offset=0.):
     """Basic preprocessing that reshapes, transposes, and scales an image"""
-    y = x.reshape(image_shape)
-    y = (y + offset) * scale
+    x = (x + offset) * scale
     if transpose is not None:
-        y = y.transpose(transpose)  # color channel last
-    if y.shape[-1] == 1:
-        y = y[..., 0]
-    return y.clip(0, 255).astype('uint8')
+        x = x.transpose(transpose)  # color channel last
+    if x.shape[-1] == 1:
+        x = x[..., 0]
+    return x.clip(0, 255).astype('uint8')
 
 
-def image_display_function(image_shape, preprocess=preprocess_display,
-                           **preprocess_args):
-    """Make a function to display images in Nengo GUI
+def image_html_function(image_shape, preprocess=preprocess_display,
+                         **preprocess_args):
+    """Make a function to turn an image into HTML to display as an SVG.
 
-    Examples
-    --------
-    >>> u = nengo.Node(nengo.processes.PresentInput(images, 0.1))
-    >>> display_f = nengo_extras.gui.image_display_function(image_shape)
-    >>> display_node = nengo.Node(display_f, size_in=u.size_out)
-    >>> nengo.Connection(u, display_node, synapse=None)
+    Parameters
+    ----------
+    image_shape : array_like (3,)
+        The shape of the image: (channels, height, width)
+    preprocess : callable
+        Callable that takes an image and preprocesses it to be displayed.
+    preprocess_args : dict
+        Optional dictionary of keyword arguments for ``preprocess``.
 
-    Requirements
-    ------------
-    pillow (provides PIL, `pip install pillow`)
+    Returns
+    -------
+    html_function : callable (t, x)
+        A function that takes time and a flattened image, and returns a string
+        that defines an SVG object in HTML to display the image.
     """
     import base64
     import PIL.Image
     import cStringIO
 
     assert len(image_shape) == 3
 
-    def display_func(t, x):
-        y = preprocess(x, image_shape, **preprocess_args)
+    def html_function(x):
+        x = x.reshape(image_shape)
+        y = preprocess(x, **preprocess_args)
         png = PIL.Image.fromarray(y)
         buffer = cStringIO.StringIO()
         png.save(buffer, format="PNG")
         img_str = base64.b64encode(buffer.getvalue())
-
-        display_func._nengo_html_ = '''
+        return '''
             <svg width="100%%" height="100%%" viewbox="0 0 100 100">
             <image width="100%%" height="100%%"
                    xlink:href="data:image/png;base64,%s"
                    style="image-rendering: pixelated;">
             </svg>''' % (''.join(img_str))
 
+    return html_function
+
+
+def image_display_function(image_shape, preprocess=preprocess_display,
+                           **preprocess_args):
+    """Make a function to display images in Nengo GUI
+
+    Examples
+    --------
+    >>> u = nengo.Node(nengo.processes.PresentInput(images, 0.1))
+    >>> display_f = nengo_extras.gui.image_display_function(image_shape)
+    >>> display_node = nengo.Node(display_f, size_in=u.size_out)
+    >>> nengo.Connection(u, display_node, synapse=None)
+
+    Requirements
+    ------------
+    pillow (provides PIL, `pip install pillow`)
+    """
+    html_function = image_html_function(
+        image_shape, preprocess=preprocess, **preprocess_args)
+
+    def display_func(t, x):
+        display_func._nengo_html_ = html_function(x)
+
     return display_func
+
+
+class PresentImages(nengo.processes.PresentInput):
+    """PresentInput process whose inputs are displayed as images in nengo_gui.
+    """
+
+    image_shape = ShapeParam('image_shape', length=3, low=1)
+
+    def __init__(self, images, presentation_time, **kwargs):
+        self.image_shape = images.shape[1:]
+        super(PresentImages, self).__init__(
+            images, presentation_time, **kwargs)
+        self.configure_display()
+
+    def configure_display(self, preprocess=preprocess_display,
+                          **preprocess_args):
+        """Configure display parameters for images
+
+        Parameters
+        ----------
+        preprocess : callable
+            Callable that takes an image and preprocesses it to be displayed.
+        preprocess_args : dict
+            Optional dictionary of keyword arguments for ``preprocess``.
+        """
+        html_function = image_html_function(
+            self.image_shape, preprocess=preprocess, **preprocess_args)
+
+        def _nengo_html_(t, x):
+            return html_function(x)
+
+        self._nengo_html_ = _nengo_html_