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A C++ module to detect and track humans which outputs location information directly in a robot's reference frame.
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## Authors
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Sprint 1 -
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- Arunava Basu (Navigator)
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- Aditi Ramadwar (Driver)
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Sprint 2 -
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- Arunava Basu (Driver)
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- Aditi Ramadwar (Navigator)
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## Introduction
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Human detection or person detection is the computer vision task of the localization and classification of human being(s) in an image. This is a key part in robotic applications for various reasons such as safety, obstacle avoidance, collaborative robotics, etc.
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@@ -43,8 +47,7 @@ Continuous integration is tracked by using Travis CI and code coverage is tracke
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Run this command in the root directory to install the dependencies :
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```
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chmod a+x scripts/install_dependencies.sh
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./scripts/install_dependencies.sh
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sh install_dependencies.sh
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```
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## System Architecture
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*Fig 1 : Activity Diagram*
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The corresponding class diagram can be found [here](https://github.com/llDev-Rootll/Human_Detection_Tracking-CPP/blob/development/UML/revised/Revised_Class_Diagram.pdf).
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## Testing
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## Output Data Explained
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At the output of the Human Detection module from the `detectHumans` method we get a vector of Rects ('Rect' is a data type in OpenCV).
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The structure of Rect is: (x, y, z, redundant); It has four parameters,
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the first three parameters indicate the coordinates of a point in terms of robot's reference frame (x, y, z)
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These coordinates can be extracted from the Rect and used as per application.
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The redundant variable is part of the transformation vector which is not useful for us for showing the location of the human detected.
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For each person detected in frame, two Rects are created. We are taking two location points of each human detected. To get an estimated location of the human in the entire frame, we need to extract the x, y, z coordinates from both the Rects created where,
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x is the distance of the human from the robot's reference frame,
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y is the horizontal component and
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z is the vertical component.
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Example:
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If there are two humans detected in a frame, then we will get a vector of four Rects. Consider the output vector variable we got is 'positions'. Size of positions is 4 since in our example 2 humans are detected, then, first human's location will be extracted from Rects positions[0] and positions[1]
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second human's location will be extracted from Rects positions[2] and positions[3]
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Example for location extraction of one of the coordinates of the first human:
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> x = position[0] (0)
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> y = position[0] (1)
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> z = position[0] (2)
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## Steps to Run the application
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Run the following commands in the root folder to build and run the Human Detector and Tracker
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sh getModels.sh
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sh build_with_coverage.sh
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sh run_app.sh
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## Running Unit tests
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Unit Testing will be used to test each submodule and ensure complete code coverage. For this Google Gtest will be leveraged and identical test classes and methods will be created with minimal modification in order to facilitate testing.
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Execute these commands in the root folder to run the test cases:
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```
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sh run_tests.sh
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```
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## Running cpplint & cppcheck tests
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Run the following command in the root directory to generate cpplint results in **results** folder
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Additionally, a small test subset of the COCO test data will be used to validate model accuracy.
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sh run_cpplint.sh
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Run the following command in the root directory to generate cppcheck results in **results** folder
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sh run_cppcheck.sh
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## Building without code coverage
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Execute these commands in the root folder :
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Run the following command in the root folder :
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```
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chmod a+x scripts/build_without_coverage.sh
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./scripts/build_without_coverage.sh
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sh build_without_coverage.sh
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```
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## Building with code coverage
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Execute these commands in the root folder :
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Run these commands in the root folder :
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```
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sudo apt-get install lcov
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chmod a+x scripts/build_with_coverage.sh
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./scripts/build_with_coverage.sh
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sh build_with_coverage.sh
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```
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## Generate Doxygen documentation
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Run the following command in the root folder:
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sh create_doxy_docs.sh
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## Phase 1
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- Defined Robot, HumanDetector and test classes according to the UML diagrams.
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- Implemented all Robot and HumanDetector methods except for transformToRobotFrame method.
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- Definition and implementation of test cases are planned for Phase 2.
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Please refer to the backlog table, [here](https://docs.google.com/spreadsheets/d/1tjJKUd9B4bBSYAHnrwuMjWNl_lUBmqeB6lw7iTNKZSg/edit?usp=sharing), for an exhaustive list tasks completed in Phase 1.
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Please refer to the backlog table, [here](https://docs.google.com/spreadsheets/d/1tjJKUd9B4bBSYAHnrwuMjWNl_lUBmqeB6lw7iTNKZSg/edit?usp=sharing), for an exhaustive list tasks completed in Phase 1.
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