def test_transform_shape(self):
input1 = mx.nd.random.uniform(0, 255, shape=(32, 32, 3))
output1 = image.transform_shape(input1)
assert output1.shape == (1, 3, 32, 32), "transform_shape method fail. Got %s shape." % (str(output1.shape))
input2 = mx.nd.random.uniform(0, 255, shape=(28, 28, 3))
output2 = image.transform_shape(input2, dim_order='NHWC')
assert output2.shape == (1, 28, 28, 3), "transform_shape method fail. Got %s shape." % (str(output2.shape))
def _preprocess(self, data):
input_shape = self.signature['inputs'][0]['data_shape']
height, width = input_shape[2:]
img_arr = image.read(data[0])
img_arr = image.resize(img_arr, width, height)
img_arr = image.color_normalize(img_arr, nd.array([127.5]),
nd.array([127.5]))
img_arr = image.transform_shape(img_arr)
return [img_arr]
def _preprocess(self, data):
img_list = []
for idx, img in enumerate(data):
input_shape = self.signature['inputs'][idx]['data_shape']
# We are assuming input shape is NCHW
[h, w] = input_shape[2:]
img_arr = image.read(img)
img_arr = image.resize(img_arr, w, h)
img_arr = image.transform_shape(img_arr)
img_list.append(img_arr)
return img_list
def _preprocess(self, data):
img_list = []
for idx, img in enumerate(data):
input_shape = self.signature['inputs'][idx][
'data_shape'] # Input shape is NCHW
[h, w] = input_shape[2:]
img_arr = image.read(
img, 0) #Set flag to 0 for reading grayscale images
img_arr = image.resize(img_arr, w, h)
img_arr = image.transform_shape(img_arr)
img_list.append(img_arr)
return img_list
def _preprocess(self, data):
img_list = []
for idx, img in enumerate(data):
input_shape = self.signature['inputs'][idx]['data_shape']
# We are assuming input shape is NCHW
img_arr = image.read(img)
img_arr, im_scale = rcnn_resize(img_arr,
SHORTER_SIZE,
MAX_SIZE,
stride=IMAGE_STRIDE)
rgb_mean = mx.nd.array([[[0, 0, 0]]])
img_arr = img_arr.astype('float32')
img_arr = img_arr - rgb_mean
img_arr = image.transform_shape(img_arr)
img_list.append(img_arr)
im_info = [[img_arr.shape[2], img_arr.shape[3], im_scale]]
self.scale = im_scale
img_list.append(mx.nd.array(im_info))
return img_list
def _preprocess(self, data):
img_list = []
for idx, img in enumerate(data):
input_shape = self.signature['inputs'][idx]['data_shape']
# We are assuming input shape is NCHW
[h, w] = input_shape[2:]
if input_shape[1] == 1:
img_arr = image.read(img, 0)
else:
img_arr = image.read(img)
img_arr = image.resize(img_arr, w, h)
rgb_mean = mx.nd.array([123.68, 116.779, 103.939])
rgb_std = mx.nd.array([58.395, 57.12, 57.375])
img_arr = img_arr.astype('float32')
img_arr = mx.image.color_normalize(img_arr,
mean=rgb_mean,
std=rgb_std)
img_arr = mx.nd.reshape(img_arr, (w, h, input_shape[1]))
img_arr = image.transform_shape(img_arr)
img_list.append(img_arr)
return img_list
