def display_anchors(fmap_w, fmap_h, s):
fmap = nd.zeros((1, 10, fmap_h, fmap_w)) # 前两维的取值不影响输出结果
anchors = contrib.nd.MultiBoxPrior(fmap, sizes=s, ratios=[1, 2, 0.5])
print('anchor',anchors)
bbox_scale = nd.array((w, h, w, h))
d2l.show_bboxes(d2l.plt.imshow(img.asnumpy()).axes,
anchors[0] * bbox_scale)
def display(img, output, threshold):
fig = d2l.plt.imshow(img.asnumpy())
for row in output:
score = row[1].asscalar()
if score < threshold:
continue
h, w = img.shape[0:2]
bbox = [row[2:6] * nd.array((w, h, w, h), ctx=row.context)]
d2l.show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')
def pikaqiu():
batch_size, edge_size = 32, 256
train_iter, _ = load_data_pikachu(batch_size, edge_size)
batch = train_iter.next()
print(batch.data[0].shape, batch.label[0].shape)
imgs = (batch.data[0][0:10].transpose((0, 2, 3, 1))) / 255
axes = d2l.show_images(imgs, 2, 5).flatten()
for ax, label in zip(axes, batch.label[0][0:10]):
d2l.show_bboxes(ax, [label[0][1:5] * edge_size], colors=['w'])
train_iter = image.ImageDeIter(
path_imgrec=os.path.join(data_dir, "train.rec"),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), shuffle=True, rand_crop=1,
min_object_covered=0.95, max_attempts=200)
val_iter = image.ImageDetIter(path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,
data_shape=(3, edge_size, edge_size), suhffle=False)
return train_iter, val_iter
# In[ ]:
batch_size, edge_size = 32, 256
train_iter, _ = load_data_pikachu(batch_size, edge_size)
batch = train_iter.next()
batch.data[0].shape, batch.label[0].shape
# In[ ]:
#画出10张图片和pikachu的bounding_box
imgs = (batch.data[0][0:10].transpose((0, 2, 3, 1))) / 255
axes = d2l.show_images(imgs, 2, 5).flatten()
for ax, label in zip (axes, batch.label[0][0:10]):
d2l.show_bboxes(ax, [label[0][1:5] * edge_size], colors=['w'])
l = ssdLoss(class_preds, bbox_preds, class_labels, bbox_labels,
bbox_mask)
l.backward()
trainer.step(batch_size)
class_acc += (class_preds.argmax(
axis=-1) == class_labels).sum().asscalar()
n += class_labels.size
bbox_acc += ((bbox_preds - bbox_labels) *
bbox_mask).abs().sum().asscalar()
m += bbox_labels.size
print('epoch %d,class acc is %.2e,bbox err is %.2e,time is %.2f' %
(epoch + 1, class_acc / n, bbox_acc / m, time.time() - start))
img = image.imread('D:\d2l-zh20200904\img\pikachu.jpg')
feature = image.imresize(img, 256, 256).astype('float32').transpose(
(2, 0, 1)).expand_dims(axis=0)
anchors, class_preds, bbox_preds = net(feature)
class_probs = class_preds.softmax(axis=-1).transpose((0, 2, 1))
output = contrib.nd.MultiBoxDetection(class_probs, bbox_preds, anchors)
idx = [i for i, row in enumerate(output[0]) if row[0].asscalar() != -1]
fig = plot.imshow(img.asnumpy())
nms_outputs = output[0, idx]
h, w = img.shape[0:2]
for nms_output in nms_outputs:
if nms_output[1] < 0.3:
continue
d2l.show_bboxes(fig.axes, [nms_output[2:] * nd.array((w, h, w, h))],
'%.2f' % nms_output[1].asscalar())
plot.show()
exit()
from mxnet import image,gluon,nd,autograd,contrib
import d2lzh as d2l
img=image.imread('D:\d2l-zh-0925\img\catdog.jpg').asnumpy()
h,w=img.shape[0:2]
img_random = nd.random.uniform(shape=(1,3,h,w))
anchors=contrib.ndarray.MultiBoxPrior(img_random,sizes=[0.75,0.5,0.25],ratios=[1,2,0.5])
boxes=anchors.reshape(shape=(h,w,5,4))
box_scale=nd.array((w,h,w,h))
fig=d2l.plt.imshow(img)
d2l.show_bboxes(fig.axes,boxes[123,123,:,:]*box_scale)
ground_truth = nd.array([[0, 0.1, 0.08, 0.52, 0.92],
[1, 0.55, 0.2, 0.9, 0.88]])
labels=contrib.nd.MultiBoxTarget(anchors,ground_truth.expand_dims(axis=0),nd.zeros((1,3,2042040)))
anchors = nd.array([[0.1, 0.08, 0.52, 0.92], [0.08, 0.2, 0.56, 0.95],
[0.15, 0.3, 0.62, 0.91], [0.55, 0.2, 0.9, 0.88]])
offset_preds = nd.array([0] * anchors.size)
cls_probs = nd.array([[0] * 4, # 背景的预测概率
[0.9, 0.8, 0.7, 0.1], # 狗的预测概率
[0.1, 0.2, 0.3, 0.9]]) # 猫的预测概率
output = contrib.ndarray.MultiBoxDetection(
cls_probs.expand_dims(axis=0), offset_preds.expand_dims(axis=0),
anchors.expand_dims(axis=0), nms_threshold=0.5)
output
def display_anchors(fmap_w, fmap_h, s):
fmap = nd.zeros((1, 10, fmap_w, fmap_h)) # 1 sample, 10 channels
anchors = contrib.nd.MultiBoxPrior(fmap, sizes=s, ratios=[1, 2, 0.5])
bbox_scale = nd.array((w, h, w, h))
d2l.show_bboxes(
d2l.plt.imshow(img.asnumpy()).axes, anchors[0] * bbox_scale)
import d2lzh as d2l
from mxnet import image
from matplotlib import pyplot as plt
batch_size=32
img_size=512
train_iter=image.ImageDetIter(path_imgrec='D:\d2l-zh20200904\data\pikachu\\train.rec',
path_imgidx='D:\d2l-zh20200904\data\pikachu\\train.idx',
batch_size=batch_size,data_shape=(3,img_size,img_size),shuffle=True,rand_crop=1,
min_object_covered=0.95,max_attempts=200)
test_iter=image.ImageDetIter(path_imgrec='D:\d2l-zh20200904\data\pikachu\\val.rec',batch_size=batch_size,data_shape=(3,img_size,img_size),shuffle=False)
batch=train_iter.next()
imgs=batch.data[0].transpose((0,2,3,1))/255
axes=d2l.show_images(imgs,4,8).flatten()
# imgs = (batch.data[0][0:10].transpose((0, 2, 3, 1))) / 255
# axes = d2l.show_images(imgs, 2, 5).flatten()
# for ax, label in zip(axes, batch.label[0][0:10]):
# d2l.show_bboxes(ax, [label[0][1:5] * 256], colors=['w'])
labels=batch.label[0]
for ax,label in zip(axes,labels):
d2l.show_bboxes(ax,[label[0][1:5]*img_size])
plt.show()
import d2lzh as d2l
from mxnet import contrib, gluon, image, nd
import numpy as np
np.set_printoptions(2)
img = image.imread('../img/catdog.jpg').asnumpy()
h, w = img.shape[0:2]
print(h, w)
X = nd.random.uniform(shape=(1, 3, h, w))
Y = contrib.nd.MultiBoxPrior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])
boxes = Y.reshape((h, w, 5, 4))
d2l.set_figsize()
bbox_scale = nd.array((w, h, w, h))
fig = d2l.plt.imshow(img)
d2l.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()
