Object Detection and Bounding Boxes¶
Bounding Boxes¶
To represent a box, we need four numbers. We provide 2 ways of representing a box and their transferring functions here:
def box_corner_to_center(boxes):
"""Convert from (upper-left, lower-right) to (center, width, height).
input: boxes m x 4 list, every row is a box and every column is a number.
"""
x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
cx = (x1 + x2) / 2
cy = (y1 + y2) / 2
w = x2 - x1
h = y2 - y1
boxes = torch.stack((cx, cy, w, h), axis=-1)
return boxes
def box_center_to_corner(boxes):
"""Convert from (center, width, height) to (upper-left, lower-right)."""
cx, cy, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
x1 = cx - 0.5 * w
y1 = cy - 0.5 * h
x2 = cx + 0.5 * w
y2 = cy + 0.5 * h
boxes = torch.stack((x1, y1, x2, y2), axis=-1)
return boxes
def bbox_to_rect(bbox, color):
"""Convert bounding box to matplotlib format."""
# Convert the bounding box (upper-left x, upper-left y, lower-right x,
# lower-right y) format to the matplotlib format: ((upper-left x,
# upper-left y), width, height)
return d2l.plt.Rectangle(
xy=(bbox[0], bbox[1]),
width=bbox[2] - bbox[0],
height=bbox[3] - bbox[1],
fill=False,
edgecolor=color,
linewidth=2)
fig = d2l.plt.imshow(img)
fig.axes.add_patch(bbox_to_rect(dog_bbox, 'blue'))
fig.axes.add_patch(bbox_to_rect(cat_bbox, 'red'));
Anchor Boxes¶
Used to train ground-truth bounding box.