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| author | alexander <alexander.efremov@arm.com> | 2021-07-16 11:30:56 +0100 |
|---|---|---|
| committer | Jim Flynn <jim.flynn@arm.com> | 2022-02-04 09:55:21 +0000 |
| commit | f42f56870c6201a876f025a423eb5540d7438e83 (patch) | |
| tree | e8e57e371c851cbb9a51a2f3ec35059addd2e93e /python/pyarmnn/examples/common/cv_utils.py | |
| parent | 9d74ba6e85a043e9603445e062315f5c4965fbd6 (diff) | |
| download | armnn-f42f56870c6201a876f025a423eb5540d7438e83.tar.gz | |
MLECO-2079 Adding the python KWS example
Signed-off-by: Eanna O Cathain <eanna.ocathain@arm.com>
Change-Id: Ie1463aaeb5e3cade22df8f560ae99a8e1c4a9c17
Diffstat (limited to 'python/pyarmnn/examples/common/cv_utils.py')
| -rw-r--r-- | python/pyarmnn/examples/common/cv_utils.py | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/python/pyarmnn/examples/common/cv_utils.py b/python/pyarmnn/examples/common/cv_utils.py index fd848b8b0f..e12ff50548 100644 --- a/python/pyarmnn/examples/common/cv_utils.py +++ b/python/pyarmnn/examples/common/cv_utils.py @@ -1,4 +1,4 @@ -# Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +# Copyright © 2020-2021 Arm Ltd and Contributors. All rights reserved. # SPDX-License-Identifier: MIT """ @@ -14,7 +14,7 @@ import numpy as np import pyarmnn as ann -def preprocess(frame: np.ndarray, input_binding_info: tuple): +def preprocess(frame: np.ndarray, input_binding_info: tuple, is_normalised: bool): """ Takes a frame, resizes, swaps channels and converts data type to match model input layer. The converted frame is wrapped in a const tensor @@ -23,6 +23,7 @@ def preprocess(frame: np.ndarray, input_binding_info: tuple): Args: frame: Captured frame from video. input_binding_info: Contains shape and data type of model input layer. + is_normalised: if the input layer expects normalised data Returns: Input tensor. @@ -34,7 +35,8 @@ def preprocess(frame: np.ndarray, input_binding_info: tuple): # Expand dimensions and convert data type to match model input if input_binding_info[1].GetDataType() == ann.DataType_Float32: data_type = np.float32 - resized_frame = resized_frame.astype("float32")/255 + if is_normalised: + resized_frame = resized_frame.astype("float32")/255 else: data_type = np.uint8 |