def load_test(filename, short, max_size, mean, std):
# read and transform image
im_orig = imdecode(filename)
im, im_scale = resize(im_orig, short, max_size)
height, width = im.shape[:2]
im_info = mx.nd.array([height, width, im_scale])
# transform into tensor and normalize
im_tensor = transform(im, mean, std)
# for 1-batch inference purpose, cannot use batchify (or nd.stack) to expand dims
im_tensor = mx.nd.array(im_tensor).expand_dims(0)
im_info = mx.nd.array(im_info).expand_dims(0)
# transform cv2 BRG image to RGB for matplotlib
im_orig = im_orig[:, :, (2, 1, 0)]
return im_tensor, im_info, im_orig
def load_test(filename, short, max_size, mean, std):
# read and transform image
im_orig = imdecode(filename)
im, im_scale = resize(im_orig, short, max_size)
height, width = im.shape[:2]
im_info = mx.nd.array([height, width, im_scale])
# transform into tensor and normalize
im_tensor = transform(im, mean, std)
# for 1-batch inference purpose, cannot use batchify (or nd.stack) to expand dims
im_tensor = mx.nd.array(im_tensor).expand_dims(0)
im_info = mx.nd.array(im_info).expand_dims(0)
# transform cv2 BRG image to RGB for matplotlib
im_orig = im_orig[:, :, (2, 1, 0)]
return im_tensor, im_info, im_orig