def display_anchors(fmap_w, fmap_h, s):
d2l.set_figsize()
# The values from the first two dimensions will not affect the output
fmap = np.zeros((1, 10, fmap_h, fmap_w))
anchors = multibox_prior(fmap, sizes=s, ratios=[1, 2, 0.5])
print(anchors.shape)
bbox_scale = np.array((w, h, w, h))
d2l.show_bboxes(
d2l.plt.imshow(img.asnumpy()).axes, anchors[0] * bbox_scale)
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))
d2l.set_figsize()
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'
])
# d2l.plt.show()
print(boxes[250, 250, :, :] * bbox_scale, "djai")
def box_iou(boxes1, boxes2):
print(boxes1)
print(boxes2)
"""Compute IOU between two sets of boxes of shape (N,4) and (M,4)."""
# Compute box areas
box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) *
from d2l import mxnet as d2l
from mxnet import np, npx
npx.set_np()
fair_probs = [1.0 / 6] * 6
counts = np.random.multinomial(10, fair_probs, size=500)
cum_counts = counts.astype(np.float32).cumsum()
estimates = cum_counts / cum_counts.sum(axis=1, keepdims=True)
d2l.set_figsize((6, 4.5))
for i in range(6):
d2l.plt.plot(estimates[:, i].asnumpy(),
label=("P(die=" + str(i + 1) + ")"))
d2l.plt.axhline(y=0.167, color='black', linestyle='dashed')
d2l.plt.gca().set_xlabel('Groups of experiments')
d2l.plt.gca().set_ylabel('Estimated probability')
d2l.plt.legend()