refactor code with pre-commit

Author: xmba15

.clang-format

@@ -1,10 +1,10 @@
 ---
-Language:        Cpp
+Language: Cpp
 # BasedOnStyle:  LLVM
 AccessModifierOffset: -3
 AlignAfterOpenBracket: true
 AlignEscapedNewlinesLeft: false
-AlignOperands:   true
+AlignOperands: true
 AlignTrailingComments: true
 AllowAllParametersOfDeclarationOnNextLine: true
 AllowShortBlocksOnASingleLine: false
@@ -20,7 +20,7 @@ BreakBeforeTernaryOperators: true
 BreakConstructorInitializersBeforeComma: true
 BinPackParameters: true
 BinPackArguments: true
-ColumnLimit:     120
+ColumnLimit: 120
 ConstructorInitializerAllOnOneLineOrOnePerLine: false
 ConstructorInitializerIndentWidth: 4
 DerivePointerAlignment: false
@@ -43,22 +43,21 @@ PenaltyReturnTypeOnItsOwnLine: 60
 PointerAlignment: Left
 SpacesBeforeTrailingComments: 2
 Cpp11BracedListStyle: true
-Standard:        Cpp11
-IndentWidth:     4
-TabWidth:        8
-UseTab:          Never
+Standard: Cpp11
+IndentWidth: 4
+TabWidth: 8
+UseTab: Never
 BreakBeforeBraces: Linux # the position of the braces
 SpacesInParentheses: false
 SpacesInSquareBrackets: false
-SpacesInAngles:  false
+SpacesInAngles: false
 SpaceInEmptyParentheses: false
 SpacesInCStyleCastParentheses: false
 SpaceAfterCStyleCast: false
 SpacesInContainerLiterals: true
 SpaceBeforeAssignmentOperators: true
 ContinuationIndentWidth: 4
-CommentPragmas:  '.*'
-ForEachMacros:   [ foreach, Q_FOREACH, BOOST_FOREACH ]
+CommentPragmas: ".*"
+ForEachMacros: [foreach, Q_FOREACH, BOOST_FOREACH]
 SpaceBeforeParens: ControlStatements
-DisableFormat:   false
-...
+DisableFormat: false

.gitignore

@@ -11,6 +11,7 @@
 *~
 *#
 build*
+.cache*
 
 # Packages #
 ###################

.pre-commit-config.yaml

@@ -0,0 +1,27 @@
+repos:
+  - repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: v14.0.6
+    hooks:
+      - id: clang-format
+
+  - repo: https://github.com/cpplint/cpplint
+    rev: 1.6.0
+    hooks:
+      - id: cpplint
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v2.7.1
+    hooks:
+      - id: prettier
+        types_or: [json, markdown, yaml]
+
+  - repo: https://github.com/lovesegfault/beautysh
+    rev: v6.2.1
+    hooks:
+      - id: beautysh

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.10)
 
-project(ort_utility)
+project(ort_utility VERSION 0.0.1)
 set (CMAKE_CXX_STANDARD 17)
 
 list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake)

CPPLINT.cfg

@@ -0,0 +1,10 @@
+set noparent
+
+filter=-legal/copyright
+filter=-whitespace/braces
+filter=-build/header_guard
+filter=+build/pragma_once
+filter=-build/include_order
+filter=-build/include_subdir
+filter=-build/c++11
+linelength=120

Makefile

@@ -4,7 +4,11 @@ CMAKE_ARGS:=$(CMAKE_ARGS)
 
 default:
 	@mkdir -p build
-	@cd build && cmake .. -DBUILD_EXAMPLES=$(BUILD_EXAMPLES) -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) $(CMAKE_ARGS) && make
+	@cd build && cmake .. -DBUILD_EXAMPLES=$(BUILD_EXAMPLES) \
+                              -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) \
+                              -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
+                              $(CMAKE_ARGS)
+	@cd build && make
 
 debug:
 	@make default BUILD_TYPE=Debug

README.md

@@ -1,4 +1,4 @@
-# small c++ library to quickly use [onnxruntime](https://github.com/microsoft/onnxruntime) to deploy deep learning models #
+# small c++ library to quickly use [onnxruntime](https://github.com/microsoft/onnxruntime) to deploy deep learning models
 
 Thanks to [cardboardcode](https://github.com/cardboardcode), we have [the documentation](https://onnx-runtime-cpp.readthedocs.io/en/latest/index.html) for this small library.
 Hope that they both are helpful for your work.
@@ -9,14 +9,17 @@ Hope that they both are helpful for your work.
 - [x] Examples for famous models, like yolov3, mask-rcnn, [ultra-light-weight face detector](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB), [yolox](https://github.com/Megvii-BaseDetection/YOLOX), [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.3). Might consider supporting more if requested
 - [ ] Batch-inference
 
-## Installation ##
+## Installation
+
 - build onnxruntime from source with the following script
+
 ```bash
     sudo bash ./scripts/install_onnx_runtime.sh
 ```
 
-## How to build ##
-***
+## How to build
+
+---
 
 ```bash
 make default
@@ -25,17 +28,21 @@ make default
 make apps
 ```
 
-## How to test apps ##
-***
+## How to test apps
 
-### Image Classification With Squeezenet ###
-***
+---
+
+### Image Classification With Squeezenet
+
+---
 
 ```bash
 # after make apps
 ./build/examples/TestImageClassification ./data/squeezenet1.1.onnx ./data/images/dog.jpg
 ```
+
 the following result can be obtained
+
 ```
 264 : Cardigan, Cardigan Welsh corgi : 0.391365
 263 : Pembroke, Pembroke Welsh corgi : 0.376214
@@ -44,30 +51,39 @@ the following result can be obtained
 230 : Shetland sheepdog, Shetland sheep dog, Shetland : 0.020529
 ```
 
-### Object Detection With Tiny-Yolov2 trained on VOC dataset (with 20 classes) ###
-***
+### Object Detection With Tiny-Yolov2 trained on VOC dataset (with 20 classes)
+
+---
+
 - Download model from onnx model zoo: [HERE](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov2)
 
 - The shape of the output would be
+
 ```text
     OUTPUT_FEATUREMAP_SIZE X OUTPUT_FEATUREMAP_SIZE * NUM_ANCHORS * (NUM_CLASSES + 4 + 1)
     where OUTPUT_FEATUREMAP_SIZE = 13; NUM_ANCHORS = 5; NUM_CLASSES = 20 for the tiny-yolov2 model from onnx model zoo
 ```
+
 - Test tiny-yolov2 inference apps
+
 ```bash
 # after make apps
 ./build/examples/tiny_yolo_v2 [path/to/tiny_yolov2/onnx/model] ./data/images/dog.jpg
 ```
+
 - Test result
 
 ![tinyyolov2 test result](./data/images/result.jpg)
 
-### Object Instance Segmentation With MaskRCNN trained on MS CoCo Dataset (80 + 1(background) clasess) ###
-***
+### Object Instance Segmentation With MaskRCNN trained on MS CoCo Dataset (80 + 1(background) clasess)
+
+---
+
 - Download model from onnx model zoo: [HERE](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn)
 
 - As also stated in the url above, there are four outputs: boxes(nboxes x 4), labels(nboxes), scores(nboxes), masks(nboxesx1x28x28)
 - Test mask-rcnn inference apps
+
 ```bash
 # after make apps
 ./build/examples/mask_rcnn [path/to/mask_rcnn/onnx/model] ./data/images/dogs.jpg
@@ -79,12 +95,14 @@ the following result can be obtained
 
 ![indoor maskrcnn result](./data/images/indoor_maskrcnn_result.jpg)
 
-### Yolo V3 trained on Ms CoCo Dataset ###
-***
+### Yolo V3 trained on Ms CoCo Dataset
+
+---
 
 - Download model from onnx model zoo: [HERE](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov3)
 
 - Test yolo-v3 inference apps
+
 ```bash
 # after make apps
 ./build/examples/yolov3 [path/to/yolov3/onnx/model] ./data/images/no_way_home.jpg
@@ -96,61 +114,67 @@ the following result can be obtained
   <img width="1000" height="667" src="./data/images/no_way_home_result.jpg">
 </p>
 
+### [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB)
 
-### [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) ###
-***
+---
 
 - App to use onnx model trained with famous light-weight [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB)
 - Sample weight has been saved [./data/version-RFB-640.onnx](./data/version-RFB-640.onnx)
 - Test inference apps
+
 ```bash
 # after make apps
 ./build/examples/ultra_light_face_detector ./data/version-RFB-640.onnx ./data/images/endgame.jpg
 ```
 
 - Test results:
-![ultra light weight face result](./data/images/endgame_result.jpg)
+  ![ultra light weight face result](./data/images/endgame_result.jpg)
 
 ### [YoloX: high-performance anchor-free YOLO by Megvii](https://github.com/Megvii-BaseDetection/YOLOX)
-***
+
+---
 
 - Download onnx model trained on COCO dataset from [HERE](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime)
+
 ```bash
 # this app tests yolox_l model but you can try with other yolox models also.
 wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_l.onnx -O ./data/yolox_l.onnx
 ```
 
 - Test inference apps
+
 ```bash
 # after make apps
 ./build/examples/yolox ./data/yolox_l.onnx ./data/images/matrix.jpg
 ```
+
 - Test results:
-![yolox result](./data/images/matrix_result.jpg)
+  ![yolox result](./data/images/matrix_result.jpg)
 
 ### [Semantic Segmentation Paddle Seg](https://github.com/PaddlePaddle/PaddleSeg)
-***
+
+---
 
 - Download PaddleSeg's bisenetv2 trained on cityscapes dataset that has been converted to onnx [HERE](https://drive.google.com/file/d/1e-anuWG_ppDXmoy0sQ0sgrdutCTGlk95/view?usp=sharing) and copy to [./data directory](./data)
 
 <details>
 <summary>You can also convert your own PaddleSeg with following procedures</summary>
 
-*  [export PaddleSeg model](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.3/docs/model_export.md)
-*  convert exported model to onnx format with [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)
+- [export PaddleSeg model](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.3/docs/model_export.md)
+- convert exported model to onnx format with [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)
 
 </details>
 
 - Test inference apps
 
 ```bash
-./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./sample_city_scapes.png
-./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./odaiba.jpg
+./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./data/images/sample_city_scapes.png
+./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./data/images/odaiba.jpg
 ```
 
 - Test results:
 
-    +  cityscapes dataset's color legend
+  - cityscapes dataset's color legend
 
 ![city scapes color legend](./data/images/cityscapes_legend.jpg)
 

examples/MaskRCNN.cpp

@@ -3,10 +3,6 @@
  *
  * @author  btran
  *
- * @date    2020-05-18
- *
- * Copyright (c) organization
- *
  */
 
 #include <cstring>

examples/MaskRCNN.hpp

@@ -3,10 +3,6 @@
  *
  * @author  btran
  *
- * @date    2020-05-18
- *
- * Copyright (c) organization
- *
  */
 
 #pragma once

examples/MaskRCNNApp.cpp

@@ -3,11 +3,8 @@
  *
  * @author  btran
  *
- * @date    2020-05-18
- *
- * Copyright (c) organization
- *
  */
+
 #include <chrono>
 #include <iostream>
 #include <memory>
@@ -24,7 +21,7 @@ static const std::vector<cv::Scalar> COLORS = toCvScalarColors(Ort::MSCOCO_COLOR
 
 namespace
 {
-cv::Mat processOneFrame(Ort::MaskRCNN& osh, const cv::Mat& inputImg, int newW, int newH, int paddedW, int paddedH,
+cv::Mat processOneFrame(const Ort::MaskRCNN& osh, const cv::Mat& inputImg, int newW, int newH, int paddedW, int paddedH,
                         float ratio, float* dst, const float confThresh = 0.5,
                         const cv::Scalar& meanVal = cv::Scalar(102.9801, 115.9465, 122.7717),
                         bool visualizeMask = true);
@@ -68,7 +65,7 @@ int main(int argc, char* argv[])
 
 namespace
 {
-cv::Mat processOneFrame(Ort::MaskRCNN& osh, const cv::Mat& inputImg, int newW, int newH, int paddedW, int paddedH,
+cv::Mat processOneFrame(const Ort::MaskRCNN& osh, const cv::Mat& inputImg, int newW, int newH, int paddedW, int paddedH,
                         float ratio, float* dst, float confThresh, const cv::Scalar& meanVal, bool visualizeMask)
 {
     cv::Mat tmpImg;

examples/SemanticSegmentationPaddleSegBisenetv2.hpp

@@ -8,6 +8,8 @@
 #pragma once
 
 #include <opencv2/opencv.hpp>
+#include <string>
+#include <vector>
 
 #include <ort_utility/ort_utility.hpp>