fixed submodule error

yolov4-for-darknet_ros @ 81d45c79

-Subproject commit 81d45c7931a37aee040fdfe8978235509e97c39b

docker/yolo/yolov4-for-darknet_ros/LICENSE

+MIT License
+
+Copyright (c) 2020 Tossy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

docker/yolo/yolov4-for-darknet_ros/README.md

+<!--
+[![people](readmemedia/people.png)](https://www.pexels.com/photo/woman-falling-in-line-holding-each-other-1206059/ "people")\
+[![birds](readmemedia/birds.png)](https://www.pexels.com/photo/flock-of-white-and-black-birds-on-ground-4023267/ "birds")\
+-->
+
+<div align = "center">
+<img src="readmemedia/birds.png" width=70%>
+<img src="readmemedia/people.png" width=70%>
+</div>
+
+# YOLO V4 for darknet_ros
+Thanks for taking a look at this repository!! <br>
+This repository is the environment of YOLO V4 ported to darknet_ros. <br>
+If you need the Japanese README file, please go [here](./documents/README_ja.md).
+
+
+
+# Installation
+This repository assumes that the [Robot Operating System (ROS)](https://www.ros.org/) has already been installed on your environment.
+See [ros.org](https://www.ros.org/install/) to know how to install ROS. My environment is [here](#Enviroment).
+
+<!-- このリポジトリを導入する環境では, すでに[ROS(Robot Operating System)](https://www.ros.org/)が導入されていることとします. ROSの導入方法は[ros.org](https://www.ros.org/install/)を参考にしてください. 私の[構築環境](#Environment)はこちらです. -->
+
+
+## Create workspace
+First, create the ROS workspace. The `<workspace>` can be named arbitrarily.
+<!-- まずROSのワークスペースを生成します. `<workspace>`は任意の名前をつけてください. -->
+```bash
+## Create workspace for ROS, Change directory
+$ mkdir -p workspace/src && cd workspace/src
+
+## init workspace
+$ catkin_init_workspace
+
+## Make
+$ cd ../
+$ catkin_make
+```
+
+## Installation your environment
+### Easy Installation
+To clone `darknet` and `darknet_ros` as a submodule, you must clone them with `--recursive`. This allows you to clone the two submodules together.
+<!-- `darknet`と`darknet_ros`をsubmoduleとして扱うためには, `--recursive`をつけて　cloneしてください.
+これによりsubmoduleである2つのリポジトリをまとめてcloneすることができます. -->
+```bash
+$ cd src
+$ git clone --recursive https://github.com/Tossy0423/yolov4-for-darknet_ros.git
+```
+
+
+<!-- ### If you don't use `darknet` and `darknet_ros` as a submodule
+### `darknet`と`darknet_ros`をsubmoduleとして扱わずclone
+To avoid using `darknet` and `darknet_ros` as submodules, you need to clone the two repositories separately.
+`darknet`と`darknet_ros`をsubmoduleとして扱わないためには, ２つのリポジトリを別々にcloneする必要があります.
+```bash
+$ git clone https://github.com/Tossy0423/darknet.git
+$ git clone https://github.com/Tossy0423/darknet_ros.git
+```
+In this case, the tree structure of the folder looks like this.
+このやり方の場合, フォルダのツリー構造は以下のようになります.
+```
+src
+├── darknet
+│
+├── darknet_ros
+``` -->
+
+## Download weights file
+The weights file is very large and needs to be downloaded separately.
+<!-- weightsはファイルサイズが大きので別でダウンロードする必要があります. -->
+Download the weights file to `darknet_ros/darknet_ros/yolo_network_config/weights` to install it.
+<!-- weightsファイルを導入する場所は, `darknet_ros/darknet_ros/yolo_network_config/weights`へダウンロードしてください. -->
+```bash
+$ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
+```
+
+
+## make pkg
+```bash
+# Change workspace directory
+$ cd workspace
+
+## Make
+$ catkin_make
+
+# Write source command in ~/.bashrc
+$ echo "source ~/workspace/devel/setup.bash" >> ~/.bashrc
+```
+
+
+# Environment
+
+|         Topics         	|                     Spec                     	|
+|:----------------------:	|:--------------------------------------------:	|
+|          Model         	|              Dell ALIENWARE m15              	|
+|           CPU          	|   Intel® Core™ i7-8750H CPU @ 2.20GHz × 12   	|
+|           GPU          	| GeForce RTX 2070 with Max-Q Design/PCIe/SSE2 	|
+|           RAM          	|                    15.4 GB                   	|
+|           OS           	|           Ubuntu 18.04.3 LTS bionic          	|
+|       Midlleware       	|                  ROS Melodic                 	|
+|       NVIDIA-SMI       	|                    440.100                   	|
+|     Driver Version     	|                    440.100                   	|
+|      CUDA Version      	|                     10.2                     	|
+| Cuda compilation tools 	|            release 10.1, V10.1.243           	|
+
+
+
+# Demo
+I will describe how to demonstrate it in an implemented environment. Before you can do the demo, you need to prepare for it.
+<!-- ここまで実装した環境でのデモの方法を述べます. デモをする前に準備すべきことがあります. -->
+
+## Preparation
+- Web Camera<br>
+  You don't need to have it if it's already installed.
+  <!-- ノートPCなどのようにすでに搭載されている場合は用意する必要はありません. -->
+  At the Terminal
+  <!-- Terminalで -->
+  > $ ls /dev/video*
+
+  If you enter and display, you are ready to go.　If you do not see anything, prepare your environment.
+  <!-- 入力し表示があれば準備できています.　何も表示されなければ環境を整えてください. -->
+
+- ROS package of `uvc_camera`<br>
+    If it has already been installed, there is no need to do it again. To install, execute the following command
+  <!-- すでにインストールされている場合は再度行う必要はありません.
+  インストールには, 以下のコマンドを実行してください -->
+  ```bash
+  $ sudo apt install ros-melodic-uvc-camera
+  ```
+- Fixing the launch file <br>
+  If you use the `uvc_camera` package, you need to change the Topic of the camera image to input to darknet_ros. Open the file `darknet_ros/darknet_ros/config/ros.yaml` and change the file as follows
+  <!-- `uvc_camera`パッケージを使う場合, カメラ画像のTopicをdarknet_rosへ入力するために変更する必要があります.  任意のエディタで, `darknet_ros/darknet_ros/config/ros.yaml`を開いて以下のように変更します. -->
+  ```
+  ## Before
+  topic: /camera/rgb/image_raw
+
+  ## After
+  topic: /image_raw
+  ```
+## Running
+Execute the three commands in another Terminal.
+<!-- 以下の3つのコマンドは別のTerminalで実行してください. -->
+```bash
+$ roscore
+$ rosrun uvc_camera uvc_camera_node
+$ roslaunch darknet_ros yolo_v4.launch
+```
+<div align=center>
+<img src="readmemedia/camera.png" width=90%>
+</div>
+
+
+# How to build this environment??
+Here is the documentation to set up this environment.
+<!-- この環境を構築するための手順を示したドキュメントをここに示します. -->
+
+
+# Acknowledgment
+This repository consists of two large repositories, [AlexeyAB](https://github.com/AlexeyAB)'s [darknet](https://github.com/AlexeyAB/darknet) and [legged robotics](https://github.com/leggedrobotics)'s [darknet_ros](https://github.com/leggedrobotics/darknet_ros).
+For building this environment, I have a reference to the information published by many developers.
+I would like to thank the developers who made these repositories available to me, and many of them have contributed their information to make this environment possible.
+<!-- このリポジトリは, [AlexeyAB](https://github.com/AlexeyAB)の[darknet](https://github.com/AlexeyAB/darknet), [leggedrobotics](https://github.com/leggedrobotics)の[darknet_ros](https://github.com/leggedrobotics/darknet_ros)の大きなリポジトリで構成されています.
+またこの環境の構築には, 多くの開発者が公開してくれた情報を参考にしました.
+これらの素晴らしいリポジトリを公開してくださった開発者, 情報提供してくださった多くの開発者に感謝します. -->
+
+
+# LISENCE
+<!-- このリポジトリはMIT Licenseを元にリリースしています. 詳しくは`LICENSE`ファイルをご覧ください. -->
+This software is released under the MIT License, see LICENSE.
+
+# Future Plans
+  I will improve it at my whim.
+  <!-- 私の気まぐれで改善していきます. -->
+  - [x] Rewrite the readme file in English. :earth_americas:
+  - [ ] Getting your environment ready to run in the Docker Container. :whale:
+
+# History
+  - I've roughly summarized everything from environment building to manual creation(11. Aug. 2020)
+  - Create an English version of the README(20. Aug. 2020)
+  - Moved `/root/darknet` to `/root/darknet/darknet/darknet_ros` due to redundant file structure.(31. Aug. 2020)
+
+# Autor
+Tossy
+Japan, Osaka
+Please Follow [me](https://twitter.com/gtr35nismo0423)!!

docker/yolo/yolov4-for-darknet_ros/darknet_ros/.gitmodules

+[submodule "darknet"]
+	path = darknet
+	url = https://github.com/Tossy0423/darknet.git

docker/yolo/yolov4-for-darknet_ros/darknet_ros/LICENSE

+Copyright (c) 2017, Marko Bjelonic, Robotic Systems Lab, ETH Zurich
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the copyright holder nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docker/yolo/yolov4-for-darknet_ros/darknet_ros/README.md

+# YOLO ROS: Real-Time Object Detection for ROS
+
+## Overview
+
+This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. For more information about YOLO, Darknet, available training data and training YOLO see the following link: [YOLO: Real-Time Object Detection](http://pjreddie.com/darknet/yolo/).
+
+The YOLO packages have been tested under ROS Melodic and Ubuntu 18.04. This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.
+
+**Author: [Marko Bjelonic](https://www.markobjelonic.com), marko.bjelonic@mavt.ethz.ch**
+
+**Affiliation: [Robotic Systems Lab](http://www.rsl.ethz.ch/), ETH Zurich**
+
+![Darknet Ros example: Detection image](darknet_ros/doc/test_detection.png)
+![Darknet Ros example: Detection image](darknet_ros/doc/test_detection_anymal.png)
+
+Based on the [Pascal VOC](https://pjreddie.com/projects/pascal-voc-dataset-mirror/) 2012 dataset, YOLO can detect the 20 Pascal object classes:
+
+- person
+- bird, cat, cow, dog, horse, sheep
+- aeroplane, bicycle, boat, bus, car, motorbike, train
+- bottle, chair, dining table, potted plant, sofa, tv/monitor
+
+Based on the [COCO](http://cocodataset.org/#home) dataset, YOLO can detect the 80 COCO object classes:
+
+- person
+- bicycle, car, motorbike, aeroplane, bus, train, truck, boat
+- traffic light, fire hydrant, stop sign, parking meter, bench
+- cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe
+- backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket
+- bottle, wine glass, cup, fork, knife, spoon, bowl
+- banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake
+- chair, sofa, pottedplant, bed, diningtable, toilet, tvmonitor, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush
+
+## Citing
+
+The YOLO methods used in this software are described in the paper: [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640).
+
+If you are using YOLO V3 for ROS, please add the following citation to your publication:
+
+M. Bjelonic
+**"YOLO ROS: Real-Time Object Detection for ROS"**,
+URL: https://github.com/leggedrobotics/darknet_ros, 2018.
+
+    @misc{bjelonicYolo2018,
+      author = {Marko Bjelonic},
+      title = {{YOLO ROS}: Real-Time Object Detection for {ROS}},
+      howpublished = {\url{https://github.com/leggedrobotics/darknet_ros}},
+      year = {2016--2018},
+    }
+
+## Installation
+
+### Dependencies
+
+This software is built on the Robotic Operating System ([ROS]), which needs to be [installed](http://wiki.ros.org) first. Additionally, YOLO for ROS depends on following software:
+
+- [OpenCV](http://opencv.org/) (computer vision library),
+- [boost](http://www.boost.org/) (c++ library),
+
+### Building
+
+[![Build Status](https://ci.leggedrobotics.com/buildStatus/icon?job=github_leggedrobotics/darknet_ros/master)](https://ci.leggedrobotics.com/job/github_leggedrobotics/job/darknet_ros/job/master/)
+
+In order to install darknet_ros, clone the latest version using SSH (see [how to set up an SSH key](https://confluence.atlassian.com/bitbucket/set-up-an-ssh-key-728138079.html)) from this repository into your catkin workspace and compile the package using ROS.
+
+    cd catkin_workspace/src
+    git clone --recursive git@github.com:leggedrobotics/darknet_ros.git
+    cd ../
+
+To maximize performance, make sure to build in *Release* mode. You can specify the build type by setting
+
+    catkin_make -DCMAKE_BUILD_TYPE=Release
+
+or using the [Catkin Command Line Tools](http://catkin-tools.readthedocs.io/en/latest/index.html#)
+
+    catkin build darknet_ros -DCMAKE_BUILD_TYPE=Release
+
+Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. If you do not have CUDA on your System the build process will switch to the CPU version of YOLO. If you are compiling with CUDA, you might receive the following build error:
+
+    nvcc fatal : Unsupported gpu architecture 'compute_61'.
+
+This means that you need to check the compute capability (version) of your GPU. You can find a list of supported GPUs in CUDA here: [CUDA - WIKIPEDIA](https://en.wikipedia.org/wiki/CUDA#Supported_GPUs). Simply find the compute capability of your GPU and add it into darknet_ros/CMakeLists.txt. Simply add a similar line like
+
+    -O3 -gencode arch=compute_62,code=sm_62
+
+### Download weights
+
+The yolo-voc.weights and tiny-yolo-voc.weights are downloaded automatically in the CMakeLists.txt file. If you need to download them again, go into the weights folder and download the two pre-trained weights from the COCO data set:
+
+    cd catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/weights/
+    wget http://pjreddie.com/media/files/yolov2.weights
+    wget http://pjreddie.com/media/files/yolov2-tiny.weights
+
+And weights from the VOC data set can be found here:
+
+    wget http://pjreddie.com/media/files/yolov2-voc.weights
+    wget http://pjreddie.com/media/files/yolov2-tiny-voc.weights
+
+And the pre-trained weight from YOLO v3 can be found here:
+
+    wget http://pjreddie.com/media/files/yolov3-tiny.weights
+    wget http://pjreddie.com/media/files/yolov3.weights
+
+There are more pre-trained weights from different data sets reported [here](https://pjreddie.com/darknet/yolo/).
+
+### Use your own detection objects
+
+In order to use your own detection objects you need to provide your weights and your cfg file inside the directories:
+
+    catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/weights/
+    catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/cfg/
+
+In addition, you need to create your config file for ROS where you define the names of the detection objects. You need to include it inside:
+
+    catkin_workspace/src/darknet_ros/darknet_ros/config/
+
+Then in the launch file you have to point to your new config file in the line:
+
+    <rosparam command="load" ns="darknet_ros" file="$(find darknet_ros)/config/your_config_file.yaml"/>
+
+### Unit Tests
+
+Run the unit tests using the [Catkin Command Line Tools](http://catkin-tools.readthedocs.io/en/latest/index.html#)
+
+    catkin build darknet_ros --no-deps --verbose --catkin-make-args run_tests
+
+You will see the image above popping up.
+
+## Basic Usage
+
+In order to get YOLO ROS: Real-Time Object Detection for ROS to run with your robot, you will need to adapt a few parameters. It is the easiest if duplicate and adapt all the parameter files that you need to change from the `darknet_ros` package. These are specifically the parameter files in `config` and the launch file from the `launch` folder.
+
+## Nodes
+
+### Node: darknet_ros
+
+This is the main YOLO ROS: Real-Time Object Detection for ROS node. It uses the camera measurements to detect pre-learned objects in the frames.
+
+### ROS related parameters
+
+You can change the names and other parameters of the publishers, subscribers and actions inside `darknet_ros/config/ros.yaml`.
+
+#### Subscribed Topics
+
+* **`/camera_reading`** ([sensor_msgs/Image])
+
+    The camera measurements.
+
+#### Published Topics
+
+* **`object_detector`** ([std_msgs::Int8])
+
+    Publishes the number of detected objects.
+
+* **`bounding_boxes`** ([darknet_ros_msgs::BoundingBoxes])
+
+    Publishes an array of bounding boxes that gives information of the position and size of the bounding box in pixel coordinates.
+
+* **`detection_image`** ([sensor_msgs::Image])
+
+    Publishes an image of the detection image including the bounding boxes.
+
+#### Actions
+
+* **`camera_reading`** ([sensor_msgs::Image])
+
+    Sends an action with an image and the result is an array of bounding boxes.
+
+### Detection related parameters
+
+You can change the parameters that are related to the detection by adding a new config file that looks similar to `darknet_ros/config/yolo.yaml`.
+
+* **`image_view/enable_opencv`** (bool)
+
+    Enable or disable the open cv view of the detection image including the bounding boxes.
+
+* **`image_view/wait_key_delay`** (int)
+
+    Wait key delay in ms of the open cv window.
+
+* **`yolo_model/config_file/name`** (string)
+
+    Name of the cfg file of the network that is used for detection. The code searches for this name inside `darknet_ros/yolo_network_config/cfg/`.
+
+* **`yolo_model/weight_file/name`** (string)
+
+    Name of the weights file of the network that is used for detection. The code searches for this name inside `darknet_ros/yolo_network_config/weights/`.
+
+* **`yolo_model/threshold/value`** (float)
+
+    Threshold of the detection algorithm. It is defined between 0 and 1.
+
+* **`yolo_model/detection_classes/names`** (array of strings)
+
+    Detection names of the network used by the cfg and weights file inside `darknet_ros/yolo_network_config/`.

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.circleci/config.yml

+version: 2.0
+jobs:
+  build:
+    docker:
+       - image: alantrrs/cuda-opencv:latest
+#      - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+    working_directory: ~/work
+    steps:
+      - checkout
+      - run: nvcc --version
+      - run: gcc --version
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=0 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8
\ No newline at end of file

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.github/FUNDING.yml

+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: ['https://paypal.me/alexeyab84', 'https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp', 'https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#', 'https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.github/ISSUE_TEMPLATE/any-other-question-or-issue.md

+---
+name: Any other question or issue
+about: Any other question or issue
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+If something doesn’t work for you, then show 2 screenshots:
+1. screenshots of your issue
+2. screenshots with such information
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF
+```
+
+If you do not get an answer for a long time, try to find the answer among Issues with a Solved label: https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3ASolved

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.github/ISSUE_TEMPLATE/bug-report.md

+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+If you want to report a bug - provide:
+    * description of a bug
+    * what command do you use?
+    * do you use Win/Linux/Mac?
+    * attach screenshot of a bug with previous messages in terminal
+    * in what cases a bug occurs, and in which not?
+    * if possible, specify date/commit of Darknet that works without this bug
+    * show such screenshot with info
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+```

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.github/ISSUE_TEMPLATE/feature_request.md

+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: Feature-request
+assignees: ''
+
+---
+
+For Feature-request:
+    * describe your feature as detailed as possible
+    * provide link to the paper and/or source code if it exist
+    * attach chart/table with comparison that shows improvement

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md

+---
+name: Training issue - no-detections / Nan avg-loss / low accuracy
+about: Training issue - no-detections / Nan avg-loss / low accuracy
+title: ''
+labels: Training issue
+assignees: ''
+
+---
+
+If you have an issue with training - no-detections / Nan avg-loss / low accuracy:
+    * read FAQ: https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions
+    * what command do you use?
+    * what dataset do you use?   
+    * what Loss and mAP did you get?
+    * show chart.png with Loss and mAP    
+    * check your dataset - run training with flag `-show_imgs` i.e. `./darknet detector train ... -show_imgs` and look at the `aug_...jpg` images, do you see correct truth bounded boxes?
+    * rename your cfg-file to txt-file and drag-n-drop (attach) to your message here
+    * show content of generated files `bad.list` and `bad_label.list` if they exist
+    * Read `How to train (to detect your custom objects)` and `How to improve object detection` in the Readme: https://github.com/AlexeyAB/darknet/blob/master/README.md
+    * show such screenshot with info
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+```

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/.gitignore

+*.o
+*.a
+*.dSYM
+*.csv
+*.out
+*.png
+*.so
+*.exe
+*.dll
+*.lib
+*.dylib
+mnist/
+data/
+caffe/
+grasp/
+images/
+opencv/
+convnet/
+decaf/
+submission/
+cfg/
+build/darknet/*
+build_*/
+!build/darknet/YoloWrapper.cs
+.fuse*
+*.weights
+build/*.cmake
+build/*.ninja
+build/*.txt
+build/*.json
+build/CMakeFiles/
+build/detect_cuda_compute_capabilities.cu
+build/.ninja_deps
+build/.ninja_log
+build/Makefile
+
+# OS Generated #
+.DS_Store*
+ehthumbs.db
+Icon?
+Thumbs.db
+*.swp
+
+# IDE generated #
+.vs/
+.vscode/
+
+# Managed by CMake
+src/version.h
+
+# Build artifacts
+lib/
+share/
+include/darknet/
+uselib
+uselib_track
+darknet

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/3rdparty/pthreads/include/sched.h

+/*
+ * Module: sched.h
+ *
+ * Purpose:
+ *      Provides an implementation of POSIX realtime extensions
+ *      as defined in 
+ *
+ *              POSIX 1003.1b-1993      (POSIX.1b)
+ *
+ * --------------------------------------------------------------------------
+ *
+ *      Pthreads-win32 - POSIX Threads Library for Win32
+ *      Copyright(C) 1998 John E. Bossom
+ *      Copyright(C) 1999,2005 Pthreads-win32 contributors
+ * 
+ *      Contact Email: rpj@callisto.canberra.edu.au
+ * 
+ *      The current list of contributors is contained
+ *      in the file CONTRIBUTORS included with the source
+ *      code distribution. The list can also be seen at the
+ *      following World Wide Web location:
+ *      http://sources.redhat.com/pthreads-win32/contributors.html
+ * 
+ *      This library is free software; you can redistribute it and/or
+ *      modify it under the terms of the GNU Lesser General Public
+ *      License as published by the Free Software Foundation; either
+ *      version 2 of the License, or (at your option) any later version.
+ * 
+ *      This library is distributed in the hope that it will be useful,
+ *      but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *      MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ *      Lesser General Public License for more details.
+ * 
+ *      You should have received a copy of the GNU Lesser General Public
+ *      License along with this library in the file COPYING.LIB;
+ *      if not, write to the Free Software Foundation, Inc.,
+ *      59 Temple Place - Suite 330, Boston, MA 02111-1307, USA
+ */
+#if !defined(_SCHED_H)
+#define _SCHED_H
+
+#undef PTW32_SCHED_LEVEL
+
+#if defined(_POSIX_SOURCE)
+#define PTW32_SCHED_LEVEL 0
+/* Early POSIX */
+#endif
+
+#if defined(_POSIX_C_SOURCE) && _POSIX_C_SOURCE >= 199309
+#undef PTW32_SCHED_LEVEL
+#define PTW32_SCHED_LEVEL 1
+/* Include 1b, 1c and 1d */
+#endif
+
+#if defined(INCLUDE_NP)
+#undef PTW32_SCHED_LEVEL
+#define PTW32_SCHED_LEVEL 2
+/* Include Non-Portable extensions */
+#endif
+
+#define PTW32_SCHED_LEVEL_MAX 3
+
+#if ( defined(_POSIX_C_SOURCE) && _POSIX_C_SOURCE >= 200112 )  || !defined(PTW32_SCHED_LEVEL)
+#define PTW32_SCHED_LEVEL PTW32_SCHED_LEVEL_MAX
+/* Include everything */
+#endif
+
+
+#if defined(__GNUC__) && !defined(__declspec)
+# error Please upgrade your GNU compiler to one that supports __declspec.
+#endif
+
+/*
+ * When building the library, you should define PTW32_BUILD so that
+ * the variables/functions are exported correctly. When using the library,
+ * do NOT define PTW32_BUILD, and then the variables/functions will
+ * be imported correctly.
+ */
+#if !defined(PTW32_STATIC_LIB)
+#  if defined(PTW32_BUILD)
+#    define PTW32_DLLPORT __declspec (dllexport)
+#  else
+#    define PTW32_DLLPORT __declspec (dllimport)
+#  endif
+#else
+#  define PTW32_DLLPORT
+#endif
+
+/*
+ * This is a duplicate of what is in the autoconf config.h,
+ * which is only used when building the pthread-win32 libraries.
+ */
+
+#if !defined(PTW32_CONFIG_H)
+#  if defined(WINCE)
+#    define NEED_ERRNO
+#    define NEED_SEM
+#  endif
+#  if defined(__MINGW64__)
+#    define HAVE_STRUCT_TIMESPEC
+#    define HAVE_MODE_T
+#  elif defined(_UWIN) || defined(__MINGW32__)
+#    define HAVE_MODE_T
+#  endif
+#endif
+
+/*
+ *
+ */
+
+#if PTW32_SCHED_LEVEL >= PTW32_SCHED_LEVEL_MAX
+#if defined(NEED_ERRNO)
+#include "need_errno.h"
+#else
+#include <errno.h>
+#endif
+#endif /* PTW32_SCHED_LEVEL >= PTW32_SCHED_LEVEL_MAX */
+
+#if (defined(__MINGW64__) || defined(__MINGW32__)) || defined(_UWIN)
+# if PTW32_SCHED_LEVEL >= PTW32_SCHED_LEVEL_MAX
+/* For pid_t */
+#  include <sys/types.h>
+/* Required by Unix 98 */
+#  include <time.h>
+# else
+   typedef int pid_t;
+# endif
+#else
+ typedef int pid_t;
+#endif
+
+/* Thread scheduling policies */
+
+enum {
+  SCHED_OTHER = 0,
+  SCHED_FIFO,
+  SCHED_RR,
+  SCHED_MIN   = SCHED_OTHER,
+  SCHED_MAX   = SCHED_RR
+};
+
+struct sched_param {
+  int sched_priority;
+};
+
+#if defined(__cplusplus)
+extern "C"
+{
+#endif                          /* __cplusplus */
+
+PTW32_DLLPORT int __cdecl sched_yield (void);
+
+PTW32_DLLPORT int __cdecl sched_get_priority_min (int policy);
+
+PTW32_DLLPORT int __cdecl sched_get_priority_max (int policy);
+
+PTW32_DLLPORT int __cdecl sched_setscheduler (pid_t pid, int policy);
+
+PTW32_DLLPORT int __cdecl sched_getscheduler (pid_t pid);
+
+/*
+ * Note that this macro returns ENOTSUP rather than
+ * ENOSYS as might be expected. However, returning ENOSYS
+ * should mean that sched_get_priority_{min,max} are
+ * not implemented as well as sched_rr_get_interval.
+ * This is not the case, since we just don't support
+ * round-robin scheduling. Therefore I have chosen to
+ * return the same value as sched_setscheduler when
+ * SCHED_RR is passed to it.
+ */
+#define sched_rr_get_interval(_pid, _interval) \
+  ( errno = ENOTSUP, (int) -1 )
+
+
+#if defined(__cplusplus)
+}                               /* End of extern "C" */
+#endif                          /* __cplusplus */
+
+#undef PTW32_SCHED_LEVEL
+#undef PTW32_SCHED_LEVEL_MAX
+
+#endif                          /* !_SCHED_H */
+

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/3rdparty/pthreads/include/semaphore.h

+/*
+ * Module: semaphore.h
+ *
+ * Purpose:
+ *	Semaphores aren't actually part of the PThreads standard.
+ *	They are defined by the POSIX Standard:
+ *
+ *		POSIX 1003.1b-1993	(POSIX.1b)
+ *
+ * --------------------------------------------------------------------------
+ *
+ *      Pthreads-win32 - POSIX Threads Library for Win32
+ *      Copyright(C) 1998 John E. Bossom
+ *      Copyright(C) 1999,2005 Pthreads-win32 contributors
+ * 
+ *      Contact Email: rpj@callisto.canberra.edu.au
+ * 
+ *      The current list of contributors is contained
+ *      in the file CONTRIBUTORS included with the source
+ *      code distribution. The list can also be seen at the
+ *      following World Wide Web location:
+ *      http://sources.redhat.com/pthreads-win32/contributors.html
+ * 
+ *      This library is free software; you can redistribute it and/or
+ *      modify it under the terms of the GNU Lesser General Public
+ *      License as published by the Free Software Foundation; either
+ *      version 2 of the License, or (at your option) any later version.
+ * 
+ *      This library is distributed in the hope that it will be useful,
+ *      but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *      MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ *      Lesser General Public License for more details.
+ * 
+ *      You should have received a copy of the GNU Lesser General Public
+ *      License along with this library in the file COPYING.LIB;
+ *      if not, write to the Free Software Foundation, Inc.,
+ *      59 Temple Place - Suite 330, Boston, MA 02111-1307, USA
+ */
+#if !defined( SEMAPHORE_H )
+#define SEMAPHORE_H
+
+#undef PTW32_SEMAPHORE_LEVEL
+
+#if defined(_POSIX_SOURCE)
+#define PTW32_SEMAPHORE_LEVEL 0
+/* Early POSIX */
+#endif
+
+#if defined(_POSIX_C_SOURCE) && _POSIX_C_SOURCE >= 199309
+#undef PTW32_SEMAPHORE_LEVEL
+#define PTW32_SEMAPHORE_LEVEL 1
+/* Include 1b, 1c and 1d */
+#endif
+
+#if defined(INCLUDE_NP)
+#undef PTW32_SEMAPHORE_LEVEL
+#define PTW32_SEMAPHORE_LEVEL 2
+/* Include Non-Portable extensions */
+#endif
+
+#define PTW32_SEMAPHORE_LEVEL_MAX 3
+
+#if !defined(PTW32_SEMAPHORE_LEVEL)
+#define PTW32_SEMAPHORE_LEVEL PTW32_SEMAPHORE_LEVEL_MAX
+/* Include everything */
+#endif
+
+#if defined(__GNUC__) && ! defined (__declspec)
+# error Please upgrade your GNU compiler to one that supports __declspec.
+#endif
+
+/*
+ * When building the library, you should define PTW32_BUILD so that
+ * the variables/functions are exported correctly. When using the library,
+ * do NOT define PTW32_BUILD, and then the variables/functions will
+ * be imported correctly.
+ */
+#if !defined(PTW32_STATIC_LIB)
+#  if defined(PTW32_BUILD)
+#    define PTW32_DLLPORT __declspec (dllexport)
+#  else
+#    define PTW32_DLLPORT __declspec (dllimport)
+#  endif
+#else
+#  define PTW32_DLLPORT
+#endif
+
+/*
+ * This is a duplicate of what is in the autoconf config.h,
+ * which is only used when building the pthread-win32 libraries.
+ */
+
+#if !defined(PTW32_CONFIG_H)
+#  if defined(WINCE)
+#    define NEED_ERRNO
+#    define NEED_SEM
+#  endif
+#  if defined(__MINGW64__)
+#    define HAVE_STRUCT_TIMESPEC
+#    define HAVE_MODE_T
+#  elif defined(_UWIN) || defined(__MINGW32__)
+#    define HAVE_MODE_T
+#  endif
+#endif
+
+/*
+ *
+ */
+
+#if PTW32_SEMAPHORE_LEVEL >= PTW32_SEMAPHORE_LEVEL_MAX
+#if defined(NEED_ERRNO)
+#include "need_errno.h"
+#else
+#include <errno.h>
+#endif
+#endif /* PTW32_SEMAPHORE_LEVEL >= PTW32_SEMAPHORE_LEVEL_MAX */
+
+#define _POSIX_SEMAPHORES
+
+#if defined(__cplusplus)
+extern "C"
+{
+#endif				/* __cplusplus */
+
+#if !defined(HAVE_MODE_T)
+typedef unsigned int mode_t;
+#endif
+
+
+typedef struct sem_t_ * sem_t;
+
+PTW32_DLLPORT int __cdecl sem_init (sem_t * sem,
+			    int pshared,
+			    unsigned int value);
+
+PTW32_DLLPORT int __cdecl sem_destroy (sem_t * sem);
+
+PTW32_DLLPORT int __cdecl sem_trywait (sem_t * sem);
+
+PTW32_DLLPORT int __cdecl sem_wait (sem_t * sem);
+
+PTW32_DLLPORT int __cdecl sem_timedwait (sem_t * sem,
+				 const struct timespec * abstime);
+
+PTW32_DLLPORT int __cdecl sem_post (sem_t * sem);
+
+PTW32_DLLPORT int __cdecl sem_post_multiple (sem_t * sem,
+				     int count);
+
+PTW32_DLLPORT int __cdecl sem_open (const char * name,
+			    int oflag,
+			    mode_t mode,
+			    unsigned int value);
+
+PTW32_DLLPORT int __cdecl sem_close (sem_t * sem);
+
+PTW32_DLLPORT int __cdecl sem_unlink (const char * name);
+
+PTW32_DLLPORT int __cdecl sem_getvalue (sem_t * sem,
+				int * sval);
+
+#if defined(__cplusplus)
+}				/* End of extern "C" */
+#endif				/* __cplusplus */
+
+#undef PTW32_SEMAPHORE_LEVEL
+#undef PTW32_SEMAPHORE_LEVEL_MAX
+
+#endif				/* !SEMAPHORE_H */

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/DarknetConfig.cmake.in

+# Config file for the Darknet package
+
+get_filename_component(Darknet_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+list(APPEND CMAKE_MODULE_PATH "${Darknet_CMAKE_DIR}")
+
+include(CMakeFindDependencyMacro)
+
+if(@OpenCV_FOUND@)
+  find_dependency(OpenCV)
+endif()
+
+if(@ENABLE_CUDA@)
+  include(CheckLanguage)
+  check_language(CUDA)
+  if(NOT CMAKE_CUDA_COMPILER)
+    message(STATUS " --> WARNING: Unable to find native CUDA integration!")
+  endif()
+  find_dependency(CUDA)
+  cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES})
+  if(@CUDNN_FOUND@)
+    find_dependency(CUDNN)
+  endif()
+endif()
+
+set(CMAKE_THREAD_PREFER_PTHREAD ON)
+find_dependency(Threads)
+
+if(MSVC)
+  find_dependency(PThreads_windows)
+  set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}")
+endif()
+
+if(@OPENMP_FOUND@)
+  find_dependency(OpenMP)
+endif()
+
+# Our library dependencies (contains definitions for IMPORTED targets)
+include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake")
+include("${Darknet_CMAKE_DIR}/DarknetConfigVersion.cmake")
+
+get_target_property(FULL_DARKNET_INCLUDE_DIRS Darknet::dark INTERFACE_INCLUDE_DIRECTORIES)
+list(GET FULL_DARKNET_INCLUDE_DIRS 0 Darknet_INCLUDE_DIR)
+get_filename_component(Darknet_INCLUDE_DIR "${Darknet_INCLUDE_DIR}" REALPATH)
+
+find_package_handle_standard_args(Darknet REQUIRED_VARS Darknet_INCLUDE_DIR VERSION_VAR PACKAGE_VERSION)

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/LICENSE

+                                  YOLO LICENSE
+                             Version 2, July 29 2016
+
+THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER
+SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN
+TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES
+LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S
+THE REAL LICENSE:
+
+0. Darknet is public domain.
+1. Do whatever you want with it.
+2. Stop emailing me about it!

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/Makefile

+GPU=1
+CUDNN=1
+CUDNN_HALF=0
+OPENCV=1
+AVX=0
+OPENMP=0
+LIBSO=0
+ZED_CAMERA=0
+ZED_CAMERA_v2_8=0
+
+# set GPU=1 and CUDNN=1 to speedup on GPU
+# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher
+# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)
+# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above
+# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X
+
+USE_CPP=0
+DEBUG=0
+
+ARCH= -gencode arch=compute_30,code=sm_30 \
+      -gencode arch=compute_35,code=sm_35 \
+      -gencode arch=compute_50,code=[sm_50,compute_50] \
+      -gencode arch=compute_52,code=[sm_52,compute_52] \
+	    -gencode arch=compute_61,code=[sm_61,compute_61]
+
+OS := $(shell uname)
+
+# Tesla A100 (GA100), DGX-A100, RTX 3080
+# ARCH= -gencode arch=compute_80,code=[sm_80,compute_80]
+
+# Tesla V100
+# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]
+
+# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores
+# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]
+
+# Jetson XAVIER
+# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]
+
+# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
+# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
+
+# GP100/Tesla P100 - DGX-1
+# ARCH= -gencode arch=compute_60,code=sm_60
+
+# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:
+# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]
+
+# For Jetson Tx2 or Drive-PX2 uncomment:
+# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]
+
+
+VPATH=./src/
+EXEC=darknet
+OBJDIR=./obj/
+
+ifeq ($(LIBSO), 1)
+LIBNAMESO=libdarknet.so
+APPNAMESO=uselib
+endif
+
+ifeq ($(USE_CPP), 1)
+CC=g++
+else
+CC=gcc
+endif
+
+CPP=g++ -std=c++11
+NVCC=nvcc
+OPTS=-Ofast
+LDFLAGS= -lm -pthread
+COMMON= -Iinclude/ -I3rdparty/stb/include
+CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC
+
+ifeq ($(DEBUG), 1)
+#OPTS= -O0 -g
+#OPTS= -Og -g
+COMMON+= -DDEBUG
+CFLAGS+= -DDEBUG
+else
+ifeq ($(AVX), 1)
+CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a
+endif
+endif
+
+CFLAGS+=$(OPTS)
+
+ifneq (,$(findstring MSYS_NT,$(OS)))
+LDFLAGS+=-lws2_32
+endif
+
+ifeq ($(OPENCV), 1)
+COMMON+= -DOPENCV
+CFLAGS+= -DOPENCV
+LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`
+COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`
+endif
+
+ifeq ($(OPENMP), 1)
+    ifeq ($(OS),Darwin) #MAC
+	    CFLAGS+= -Xpreprocessor -fopenmp
+	else
+		CFLAGS+= -fopenmp
+	endif
+LDFLAGS+= -lgomp
+endif
+
+ifeq ($(GPU), 1)
+COMMON+= -DGPU -I/usr/local/cuda/include/
+CFLAGS+= -DGPU
+ifeq ($(OS),Darwin) #MAC
+LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand
+else
+LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
+endif
+endif
+
+ifeq ($(CUDNN), 1)
+COMMON+= -DCUDNN
+ifeq ($(OS),Darwin) #MAC
+CFLAGS+= -DCUDNN -I/usr/local/cuda/include
+LDFLAGS+= -L/usr/local/cuda/lib -lcudnn
+else
+CFLAGS+= -DCUDNN -I/usr/local/cudnn/include
+LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn
+endif
+endif
+
+ifeq ($(CUDNN_HALF), 1)
+COMMON+= -DCUDNN_HALF
+CFLAGS+= -DCUDNN_HALF
+ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]
+endif
+
+ifeq ($(ZED_CAMERA), 1)
+CFLAGS+= -DZED_STEREO -I/usr/local/zed/include
+ifeq ($(ZED_CAMERA_v2_8), 1)
+LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed
+#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0
+else
+LDFLAGS+= -L/usr/local/zed/lib -lsl_zed
+#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0
+endif
+endif
+
+OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o
+ifeq ($(GPU), 1)
+LDFLAGS+= -lstdc++
+OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
+endif
+
+OBJS = $(addprefix $(OBJDIR), $(OBJ))
+DEPS = $(wildcard src/*.h) Makefile include/darknet.h
+
+all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)
+
+ifeq ($(LIBSO), 1)
+CFLAGS+= -fPIC
+
+$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp
+	$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)
+
+$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp
+	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)
+endif
+
+$(EXEC): $(OBJS)
+	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)
+
+$(OBJDIR)%.o: %.c $(DEPS)
+	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
+
+$(OBJDIR)%.o: %.cpp $(DEPS)
+	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@
+
+$(OBJDIR)%.o: %.cu $(DEPS)
+	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
+
+$(OBJDIR):
+	mkdir -p $(OBJDIR)
+backup:
+	mkdir -p backup
+results:
+	mkdir -p results
+setchmod:
+	chmod +x *.sh
+
+.PHONY: clean
+
+clean:
+	rm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/build.sh

+#!/usr/bin/env bash
+
+number_of_build_workers=8
+bypass_vcpkg=true
+force_cpp_build=false
+
+if [[ "$OSTYPE" == "darwin"* ]]; then
+  vcpkg_triplet="x64-osx"
+else
+  vcpkg_triplet="x64-linux"
+fi
+
+if [[ ! -z "${VCPKG_ROOT}" ]] && [ -d ${VCPKG_ROOT} ] && [ ! "$bypass_vcpkg" = true ]
+then
+  vcpkg_path="${VCPKG_ROOT}"
+  vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake"
+  vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet"
+  echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}"
+  additional_defines="-DBUILD_SHARED_LIBS=OFF"
+elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ ! "$bypass_vcpkg" = true ]
+then
+  vcpkg_path="${WORKSPACE}/vcpkg"
+  vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake"
+  vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet"
+  echo "Found vcpkg in WORKSPACE/vcpkg: ${vcpkg_path}"
+  additional_defines="-DBUILD_SHARED_LIBS=OFF"
+elif [ ! "$bypass_vcpkg" = true ]
+then
+  (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!")
+fi
+
+if [ "$force_cpp_build" = true ]
+then
+  additional_build_setup="-DBUILD_AS_CPP:BOOL=TRUE"
+fi
+
+## DEBUG
+#mkdir -p build_debug
+#cd build_debug
+#cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup}
+#cmake --build . --target install -- -j${number_of_build_workers}
+##cmake --build . --target install --parallel ${number_of_build_workers}  #valid only for CMake 3.12+
+#rm -f DarknetConfig.cmake
+#rm -f DarknetConfigVersion.cmake
+#cd ..
+#cp cmake/Modules/*.cmake share/darknet/
+
+# RELEASE
+mkdir -p build_release
+cd build_release
+cmake .. -DCMAKE_BUILD_TYPE=Release ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup}
+cmake --build . --target install -- -j${number_of_build_workers}
+#cmake --build . --target install --parallel ${number_of_build_workers}  #valid only for CMake 3.12+
+rm -f DarknetConfig.cmake
+rm -f DarknetConfigVersion.cmake
+cd ..
+cp cmake/Modules/*.cmake share/darknet/

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/cmake/Modules/FindStb.cmake

+# Distributed under the OSI-approved BSD 3-Clause License.
+# Copyright Stefano Sinigardi
+
+#.rst:
+# FindStb
+# ------------
+#
+# Find the Stb include headers.
+#
+# Result Variables
+# ^^^^^^^^^^^^^^^^
+#
+# This module defines the following variables:
+#
+# ``Stb_FOUND``
+#   True if Stb library found
+#
+# ``Stb_INCLUDE_DIR``
+#   Location of Stb headers
+#
+
+include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake)
+include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake)
+
+if(NOT Stb_INCLUDE_DIR)
+  find_path(Stb_INCLUDE_DIR NAMES stb_image.h PATHS ${Stb_DIR} PATH_SUFFIXES include)
+endif()
+
+find_package_handle_standard_args(Stb DEFAULT_MSG Stb_INCLUDE_DIR)
+mark_as_advanced(Stb_INCLUDE_DIR)

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/cmake/vcpkg_linux.diff

+stb
+pthreads
+ffmpeg
+opencv[ffmpeg]
+--triplet
+x64-linux
\ No newline at end of file

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/cmake/vcpkg_linux_cuda.diff

+cuda
+cudnn
+stb
+pthreads
+ffmpeg
+opencv[ffmpeg]
+--triplet
+x64-linux
\ No newline at end of file

docker/yolo/yolov4-for-darknet_ros/darknet_ros/darknet/cmake/vcpkg_osx.diff

+stb
+pthreads
+ffmpeg
+opencv[ffmpeg]
+--triplet
+x64-osx
\ No newline at end of file