def single_image_upload():
# Access the user-sent image from the request object.
fname = request.files['input-file'].filename
fpath = os.path.join("imgs", fname)
request.files['input-file'].save(fpath)
# Create a dataset to contain the image.
ds = Dataset(name='dataset_' + str(int(random() * 1000000)),
prefix='./imgs',
batch_size=1,
images=True)
ds.initialize(sources=[fname])
ds_id = STATE.add_dataset(ds)
return json.dumps({'datasetId': ds_id})
def test_keras_feature_extractor_extract_features():
ext = KerasFeatureExtractor(TEST_NET_ID, ckpt_path=TEST_CKPT_PATH)
ds = Dataset(name=TEST_NAME, prefix=TEST_PREFIX, batch_size=8)
ds.initialize(fp=TEST_SOURCES)
ds.load_images()
imgs = [e.image for e in ds.elements]
prepro = Preprocessor()
imgs = prepro.preprocess_images(imgs)
result = ext.extract_features(images=imgs)
assert isinstance(result, np.ndarray) == True
assert len(result) == ds.count
def test_prepro():
from data import Dataset
ds = Dataset(name='ds', prefix='/home/sam/Pictures', batch_size=1)
ds.initialize(sources=['puppy.jpg'])
p1 = Preprocessor(224, 224, PreproMode.RescaleHeightPadOrCropWidth)
p2 = Preprocessor(224, 224, PreproMode.RescaleWidthPadOrCropHeight)
p3 = Preprocessor(224, 224, PreproMode.RescaleWidthRescaleHeight)
p4 = Preprocessor(224, 224, PreproMode.AspectRatioCrop)
p5 = Preprocessor(224, 224, PreproMode.AspectRatioPad)
i1 = p1.preprocess(ds)[0]
i2 = p2.preprocess(ds)[0]
i3 = p3.preprocess(ds)[0]
i4 = p4.preprocess(ds)[0]
i5 = p5.preprocess(ds)[0]
i1.show()
input()
i2.show()
input()
i3.show()
input()
i4.show()
input()
i5.show()
return
def make_initialized_test_dataset():
ds = Dataset(name=TEST_NAME, prefix=TEST_PREFIX, batch_size=8)
ds.initialize(fp=TEST_SOURCES)
return ds
