def test_transforms_presets_ssd():
im_fname = gcv.utils.download('https://github.com/dmlc/web-data/blob/master/' +
'gluoncv/detection/biking.jpg?raw=true', path='biking.jpg')
x, orig_img = ssd.load_test(im_fname, short=512)
x1, orig_img1 = ssd.transform_test(mx.image.imread(im_fname), short=512)
np.testing.assert_allclose(x.asnumpy(), x1.asnumpy())
np.testing.assert_allclose(orig_img, orig_img1)
if not osp.isdir(osp.expanduser('~/.mxnet/datasets/voc')):
return
train_dataset = VOCDetectionTiny()
val_dataset = VOCDetectionTiny(splits=[('tiny_motorbike', 'test')])
width, height = (512, 512)
net = gcv.model_zoo.get_model('ssd_512_resnet50_v1_voc', pretrained=False, pretrained_base=False)
net.initialize()
num_workers = 0
batch_size = 4
with autograd.train_mode():
_, _, anchors = net(mx.nd.zeros((1, 3, height, width)))
batchify_fn = Tuple(Stack(), Stack(), Stack()) # stack image, cls_targets, box_targets
train_loader = gluon.data.DataLoader(
train_dataset.transform(ssd.SSDDefaultTrainTransform(width, height, anchors)),
batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(ssd.SSDDefaultValTransform(width, height)),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=num_workers)
train_loader2 = gluon.data.DataLoader(
train_dataset.transform(ssd.SSDDefaultTrainTransform(width, height)),
batch_size, True, batchify_fn=val_batchify_fn, last_batch='rollover', num_workers=num_workers)
for loader in [train_loader, val_loader, train_loader2]:
for i, batch in enumerate(loader):
if i > 1:
break
pass
def test_transforms_presets_ssd():
im_fname = gcv.utils.download('https://github.com/dmlc/web-data/blob/master/' +
'gluoncv/detection/biking.jpg?raw=true', path='biking.jpg')
x, orig_img = ssd.load_test(im_fname, short=512)
x1, orig_img1 = ssd.transform_test(mx.image.imread(im_fname), short=512)
np.testing.assert_allclose(x.asnumpy(), x1.asnumpy())
np.testing.assert_allclose(orig_img, orig_img1)
if not osp.isdir(osp.expanduser('~/.mxnet/datasets/voc')):
return
train_dataset = gcv.data.VOCDetection(splits=((2007, 'trainval'), (2012, 'trainval')))
val_dataset = gcv.data.VOCDetection(splits=[(2007, 'test')])
width, height = (512, 512)
net = gcv.model_zoo.get_model('ssd_512_resnet50_v1_voc', pretrained=False, pretrained_base=False)
net.initialize()
num_workers = 0
batch_size = 4
with autograd.train_mode():
_, _, anchors = net(mx.nd.zeros((1, 3, height, width)))
batchify_fn = Tuple(Stack(), Stack(), Stack()) # stack image, cls_targets, box_targets
train_loader = gluon.data.DataLoader(
train_dataset.transform(ssd.SSDDefaultTrainTransform(width, height, anchors)),
batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(ssd.SSDDefaultValTransform(width, height)),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=num_workers)
train_loader2 = gluon.data.DataLoader(
train_dataset.transform(ssd.SSDDefaultTrainTransform(width, height)),
batch_size, True, batchify_fn=val_batchify_fn, last_batch='rollover', num_workers=num_workers)
for loader in [train_loader, val_loader, train_loader2]:
for i, batch in enumerate(loader):
if i > 1:
break
pass
def detect(event, context):
# get the url
data = json.loads(event['body'])
if 'url' not in data:
response = {"statusCode": 500, "body": "Please specify a url"}
return response
url = data['url']
# download the image
urlSplit = url.split('/')
fileName = urlSplit[-1]
filePath = wget.download(url, out="/tmp/{0}".format(fileName))
# classify the image
x, img = load_test(filePath, short=512)
classes, scores, bbox = ssdnet(x)
results = []
# for each result, we'll take the each
# them if their score is greater than a given threshold
for i in range(len(scores[0])):
if float(scores[0][i].asnumpy().tolist()[0]) > score_threshold:
results.append({
"class":
ssdnet.classes[int(classes[0][i].asnumpy().tolist()[0])],
"score":
float(scores[0][i].asnumpy().tolist()[0]),
"bbox":
bbox[0][i].asnumpy().tolist()
})
# plot the box of the image and then store it in S3
plot_bbox(img, bbox[0], scores[0], classes[0], class_names=ssdnet.classes)
tmpOutPath = "/tmp/detect_{0}".format(fileName)
plt.savefig(tmpOutPath)
s3_key = "images/detect_{0}".format(fileName)
s3_bucket_name = "gudongfeng.me"
s3.upload_file(tmpOutPath, s3_bucket_name, s3_key)
body = {
"bounding_boxes": results,
"s3_url": getS3Url(s3_bucket_name, s3_key)
}
response = {
"statusCode": 200,
"body": json.dumps(body),
"headers": {
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True,
'Content-Type': 'application/json'
},
}
return response
def detect(event, context):
# get the url
url = event.get('url', None)
if not url:
response = {
"statusCode": 500,
"body": "Please specify a url"
}
return response
# download the image
filename = wget.download(url, out="/tmp/image.jpg")
# classify the image
x, _ = load_test(filename, short=512)
classes, scores, bbox = ssdnet(x)
results = []
# for each result, we'll take the each
# them if their score is greater than a given threshold
for i in range(len(scores[0])):
if float(scores[0][i].asnumpy().tolist()[0]) > score_threshold:
results.append({
"class": ssdnet.classes[int(classes[0][i].asnumpy().tolist()[0])],
"score": float(scores[0][i].asnumpy().tolist()[0]),
"bbox": bbox[0][i].asnumpy().tolist()
})
body = {
"bounding_boxes": results
}
response = {
"statusCode": 200,
"body": body
}
return response