sakshamgupta006 / dest

:panda_face: One Millisecond Deformable Shape Tracking Library (DEST)

About this library

Deformable Shape Tracking (DEST) is a C++ library providing high performance 2D shape tracking leveraging machine learning methods. The video below shows the real-time capabilities of DEST in annotating video sequences / still images with facial landmarks.

This DEST tracker was previously trained on thousands of training samples from available face databases.

DEST features

• A generic framework for learning arbitrary shape transformations.
• A lightning fast landmark alignment module.
• State of the art performance and accuracy.
• Pre-trained trackers for a quick start.
• Cross platform minimal disk footprint serialization.
• Built in support for IMM and ibug annotated face database import.

Using DEST

Using involves the following steps. First include DEST

#include <dest/dest.h>

Next, load a trained tracker from disk

dest::core::Tracker t;
t.load("destcv.bin");

Note that each release contains pre-trained tracker files. Assuming that our goal is to align face landmarks, we also need a face detector to provide a coarse estimate (rectangle) of the face area. DEST includes a convenience wrapper for OpenCV based face detection

#include <dest/face/face_detector.h>

//...

dest::face::FaceDetector fd;
fd.loadClassifiers("classifier_frontalface.xml");

OpenCV uses Viola Jones algorithm for face detection. This algorithm requires a training phase. You can find application ready files in OpenCV or here. Use the face detector to find a face in the given image.

dest::core::Rect r;
fd.detectSingleFace(img, r);

Here img is either dest::core::Image or cv::Mat. Once we have a rough estimate of the face location, we need to find a shape normalizing transform. By default the following is used

dest::core::Rect ur = dest::core::unitRectangle();
dest::core::ShapeTransform shapeToImage;

shapeToImage = dest::core::estimateSimilarityTransform(ur, r);

Finally, invoke the tracker to get the face landmarks

dest::core::Shape s = t.predict(img, shapeToImage);

The shape s contains the landmark locations in columns (x,y) for the given image. The number of landmarks depends on the data used during training.

Note, you need to use same shape normalization procedure during tracking as in training. This also holds true for the way rough estimates (face detector in this example) are generated.

Building from source

DEST requires the following pre-requisites

• CMake - for generating cross platform build files
• Eigen 3.x - for linear algebra calculations

Optionally, you need

• OpenCV 2.x / 3.x - for image processing related functions
• A compiler with OpenMP capabilities.

To build follow these steps

1. Fork or download a release of this repository. We recommend releases as those include pre-trained trackers.
2. Point CMake to the source directory.
3. Click CMake Configure and select your toolchain.
4. Specify DEST_EIGEN_DIR.
5. Select DEST_WITH_OPENCV if required. When selected you will be asked to specify OpenCV_DIR next time you run Configure. Set OpenCV_DIR to the directory containing the file OpenCVConfig.cmake.
6. Select DEST_WITH_OPENMP if required.
7. Select DEST_VERBOSE if verbose logging is required.
8. Click CMake Generate.
9. Open generated solution and build ALL_BUILD.

When is OpenCV is required?

OpenCV is required during training and when running the demo samples. DEST comes with its own Eigen based image type, OpenCV is mainly used for convenience functions such as image loading and rendering.

Any other dependencies?

Yes, those are inline included and are header only. DEST makes use of Google flatbuffers for serialization, tinydir for enumerating files and TCLAP for command line parsing.

Supported platforms

Although Deformable Shape Tracking should build across multiple platforms and architectures, tests are carried out on these systems

• Windows 8/10 MSVC10 / MSVC12 x64
• OS X 10.10 XCode 7.x x64

If the build should fail for a specific platform, don't hesitate to create an issue.

Using the tools

DEST comes with a set of handy tools to train and evaluate and trackers. The tools below require OpenCV support. Make sure to enable it before building the library.

dest_align

dest_align is a command line tool to test a previously trained tracker on sample images. It shows intermediate steps and is thus best used for debugging. Its main application is the face alignment.

To run dest_align on a single image type

> dest_align -t destcv.bin -d classifier_frontalface.xml image.png

Here destcv.bin is a pre-trained tracker file and classifier_frontalface.xml contains trained HAAR classifiers for face detection. When run, you should see an image with annotated landmarks. This is the initial situation before alignment. Use any key to cycle through cascades.

Type dest_align --help for detailed help.

dest_track_video

dest_track_video is a command line tool to track faces over multiple frames.

> dest_track_video -t destcv.bin -d classifier_frontalface.xml video.avi

This tool can also handle camera input. Specify a numeric device id, such as 0, to open a physical device.

DEST requires a rough estimate (global similarity transform) of the target shape. Here we use an OpenCV face detector for exactly this job. It works great but has the drawback of being slow compared to dest::core::Tracker. For this reason dest_track_video supports a --detect-rate parameter. If set to 1, the face detector will be invoked in all frames. Setting it to bigger values will run the face detector only every n-th frame. Between detection frames, the tool tracks the face through to simulation a face detector based on the previous tracking results.

Type dest_track_video --help for detailed help.

dest_train

dest_train allows you to train your own tracker. This step requires a training database. DEST comes with a set of importers for common face databases. You can use your own database as well: all you need to train are images, landmarks and initial estimates (usually rectangles) to provide a rough estimate of the shape.

To train a tracker using a supported database format type

> dest_train --rectangles rectangles.csv --load-mirrowed --load-max-size 640 directory

Here directory is the directory containing the shape database. rectangles.csv provide estimates of rough shape location and size. dest_train makes no assumption on how those are generated, but make sure that you use the same method during training and running the tracker later on. In case you want to go with OpenCV face detector rectangles, you can use dest_generate_rects_viola_jones to generate the rectangles. The IO format for rectangles.csv is documented at dest::io::importRectangles.

Type dest_train --help for detailed help.

dest_evaluate

dest_evaluate can is a tool used to evaluate a previously trained tracker. It loads a test database and and computes tracker statistics. These statistics include the mean Euclidean distance between target and estimated shape landmarks normalized by the inter-ocular distance when the loaded database contains faces. Here is how you invoke it

> dest_evaluate --rectangles rectangles.csv -t destcv.bin database

When using

• a pre-trained tracker from our [release](release
• on the ibug annotated HELEN test database
• using OpenCV Viola Jones estimated face rectangles

you should see roughly the following output

Loading ibug database. Found 330 candidate entries.
Successfully loaded 330 entries from database.
Average normalized error: 0.0451457  

dest_gen_rects

dest_gen_rects is a utility to generate face rectangles for a training database using OpenCVs Viola Jones algorithm. These rectangles can be fed into dest_train for learning. Note, if your application comes with a face detector built in, you may want to use your face detector to generate these rectangles.

Type dest_gen_rects --help for detailed help.

References

1. Kazemi, Vahid, and Josephine Sullivan. "One millisecond face alignment with an ensemble of regression trees." Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014.
2. Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
3. Chrysos, Grigoris, et al. "Offline deformable face tracking in arbitrary videos." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2015.
4. Gower, John C. "Generalized procrustes analysis." Psychometrika 40.1 (1975): 33-51.

License

DEST is licensed under 'three-clause' BSD license.

Copyright (c) 2015/2016, Christoph Heindl
All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

2. 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.

3. 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 THE 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.

DEST uses third party libraries that are distributed under their own terms.