Machine Learning Project Help - Anchor Boxes

Object detection algorithms in machine learning

This is an object detection technology that is used for image classification.

First, import the packages or modules required for this section.


#Import all the libraries
%matplotlib inline
import d2l
from mxnet import contrib, gluon, image, np, npx

np.set_printoptions(2)
npx.set_np()

Now reshape the object

#reshape the box
boxes = Y.reshape(h, w, 5, 4)
boxes[250, 250, 0, :]

Now draw multiple boxes on object


# Saved in the d2l package for later use
def show_bboxes(axes, bboxes, labels=None, colors=None):
    """Show bounding boxes."""
    def _make_list(obj, default_values=None):
        if obj is None:
            obj = default_values
        elif not isinstance(obj, (list, tuple)):
            obj = [obj]
        return obj
    labels = _make_list(labels)
    colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])
    for i, bbox in enumerate(bboxes):
        color = colors[i % len(colors)]
        rect = d2l.bbox_to_rect(bbox.asnumpy(), color)
        axes.add_patch(rect)
        if labels and len(labels) > i:
            text_color = 'k' if color == 'w' else 'w'
            axes.text(rect.xy[0], rect.xy[1], labels[i],
                      va='center', ha='center', fontsize=9, color=text_color,
                      bbox=dict(facecolor=color, lw=0))

Reshape the all the boxes


#reshape all the boxes in different size
d2l.set_figsize((3.5, 2.5))
bbox_scale = np.array((w, h, w, h))
fig = d2l.plt.imshow(img)
show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,
            ['s=0.75, r=1', 's=0.5, r=1', 's=0.25, r=1', 's=0.75, r=2',
             's=0.75, r=0.5'])

Output:

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