From 4bb202236961c0da33e53e725420dfb0c0d76a78 Mon Sep 17 00:00:00 2001 From: Xucong Zhang Date: Mon, 28 Jan 2019 13:40:35 +0100 Subject: [PATCH] Destroyed API calls (markdown) --- API-calls.md | 46 ---------------------------------------------- 1 file changed, 46 deletions(-) delete mode 100644 API-calls.md diff --git a/API-calls.md b/API-calls.md deleted file mode 100644 index 32d3659..0000000 --- a/API-calls.md +++ /dev/null @@ -1,46 +0,0 @@ -# Introduction -OpenGaze has the following modules: -* **opengaze**: the high-level class to call other functions, including three common use examples (see below). -* **input_handler**: a basic utility module handling the input types and data. -* **gaze_estimator**: a module to perform the gaze estimation pipeline, including face detection, head pose estimation, data normalization and predict gaze from the input face/eye image patch. -* **face_detector**: a face detection module with the third-party library to perform both face and facial landmark detection. -* **normalizer**: the data normalization module is used to extract the face/eye image patch from the input image according to 3D head pose and target center. It can compensate part of variation caused by head poses. -* **gaze_predictor**: the core module for gaze estimation. It takes an input eye/face patch and outputs the 3D gaze direction in the (normalized) camera coordinate system. -* **data**: a module that defines the stored data structure across different modules. -* **personal_calibrator**: a module to allow the user to calibrate the gaze estimation results on the 2D screen plane. - -# Get started -You can familiarize yourself with the API by exploring the main executable class OpenGaze with files `opengaze.hpp` and `opengaze.cpp`.
-You will find three examples we wrote to demonstrate how to use OpenGaze for gaze visualization, gaze estimation, and personal calibration in the "exe" directory. - -## Gaze Visualization -The main function to call for this example is `OpenGaze::runGazeEstimation()`. After initializing the input by the `InputHandler` class, a gaze estimation task can be simply run as:
-```vector output; - Mat input_image = input_handler_.getNextSample(); - Mat undist_img; - undistort(input_image, undist_img, input_handler_.camera_matrix_, input_handler_.camera_distortion_); - gaze_estimator_.estimateGaze(undist_img, output); -``` -The 3D gaze direction vector will be stored in `output[i].gaze_data.gaze3d` where `i` indicates the `ith` user inthe scene. - -## Gaze Estimation -The main function to call for this example is `OpenGaze::runGazeOnScreen()`. -You still need to estimate a 3D gaze vector first, and then simply run:
-`input_handler_.projectToDisplay(output, true);`
-to calculate the intersection of the gaze vector and screen plane. This will give you the estimated 2D location on the screen stored in `output[i].gaze_data.gaze2d`.
-Note that before this calculation, you have to provide correct calibration information. this calibration information is: -* `calibration.yml` - located in OpenGaze/content/calib/ directory, and stores the camera intrinsic parameters. These parameters can be set with the standard camera calibration procedure provided by [OpenCV](https://docs.opencv.org/3.1.0/dc/dbb/tutorial_py_calibration.html).
-* `monitor.yml` - located in OpenGaze/content/calib/ directory, and stores the rotation and translation between camera and screen. This information can be obtained with the [mirror-based camera-screen calibration method](https://dl.acm.org/citation.cfm?id=1888118). - -## Personal Calibration -The main function to call for this example is `OpenGaze::runPersonalCalibration(int num_calibration_point)`.
-The main class `PersonalCalibrator` can be initialized by:
-`PersonalCalibrator m_calibrator(input_handler_.getScreenWidth(), input_handler_.getScreenHeight());`
-It needs to know the screen size in pixels in order to show the corresponding stimuli on the screen.
-The locations of stimuli are generated randomly by specifying the number of stimulis:
-`m_calibrator.generatePoints(num_calibration_point);`
-Then each stimulus can be shown by calling:
-`m_calibrator.showNextPoint()`
-For each stimulus point, the gaze estimation module estimates the coresponding gaze point on the screen. The stimuli locations and estimated gaze points can be used to generate a personal model by:
-`m_calibrator.generateModel(prediction, ground-truth, 1)`,
-where the last parameter indicates the order of the mapping function.