DLib | C++ library with several utilities | Learning library

by dorian3d C++ Version: v1.1-free License: Non-SPDX

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kandi X-RAY | DLib Summary

DLib is a C++ library typically used in Tutorial, Learning, Example Codes applications. DLib has no bugs, it has no vulnerabilities and it has low support. However DLib has a Non-SPDX License. You can download it from GitHub.

DLib is a collection of C classes to solve common tasks in C programs, as well as to offer additional functionality to use OpenCV data and to solve computer vision problems. I have found myself using these functions very often, so I hope they may be useful to other researchers and developers too.

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DLib has a low active ecosystem.

It has 128 star(s) with 96 fork(s). There are 13 watchers for this library.

It had no major release in the last 12 months.

There are 4 open issues and 7 have been closed. On average issues are closed in 301 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DLib is v1.1-free

Quality

DLib has 0 bugs and 0 code smells.

Security

DLib has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DLib code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DLib has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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DLib releases are available to install and integrate.

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DLib Key Features

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Community Discussions

Trending Discussions on DLib

Cannot install face_recognition using pip install face_recognition on Win10

how to extract self-defined ROI with dlib facelandmarks?

Why such different answers for the xgboost scikit-learn interface?

Recreating Global 3D Points, from Local 3D Points and Global 2D Points; SolvePnP

"ERROR: CMake must be installed to build dlib" when installing face_recognition

dlib import failure on M1 pro

Bounding boxes returned without detected face image in dlib python

Overloading member operator,?

Sorting a tensor list in ascending order

How to shrink/expand facial features using Opencv?

QUESTION

Cannot install face_recognition using pip install face_recognition on Win10

Asked 2022-Apr-04 at 09:48

I get the error

Running setup.py install for dlib ... error error: subprocess-exited-with-error

× Running setup.py install for dlib did not run successfully. │ exit code: 1 ╰─> [58 lines of output] running install running build running build_py package init file 'tools\python\dlib_init_.py' not found (or not a regular file) running build_ext Building extension for Python 3.10.4 (tags/v3.10.4:9d38120, Mar 23 2022, 23:13:41) [MSC v.1929 64 bit (AMD64)] Invoking CMake setup: 'cmake C:\Users\amade\AppData\Local\Temp\pip-install-_k5e982w\dlib_237006073dfd4b13993bf60b7ecb3629\tools\python -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=C:\Users\amade\AppData\Local\Temp\pip-install-_k5e982w\dlib_237006073dfd4b13993bf60b7ecb3629\build\lib.win-amd64-3.10 -DPYTHON_EXECUTABLE=C:\Users\amade\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\python.exe -DCMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE=C:\Users\amade\AppData\Local\Temp\pip-install-_k5e982w\dlib_237006073dfd4b13993bf60b7ecb3629\build\lib.win-amd64-3.10 -A x64' -- Building for: Visual Studio 17 2022 -- Selecting Windows SDK version to target Windows 10.0.19044. -- The C compiler identification is unknown -- The CXX compiler identification is unknown CMake Error at CMakeLists.txt:14 (project): No CMAKE_C_COMPILER could be found.

...

ANSWER

Answered 2022-Apr-03 at 09:50

Follow the steps below to install face_recognition python package on Windows 10.

The instruction has been tested on Windows 10 64bit, python 3.9.

Step 1

Download CMake installation package for your OS from official site

Step 2

Install downloaded CMake installation package. Please make sure that Add CMake to system PATH option has been selected during the installation.

Step 3

Reboot your OS (restart computer)

Step 4

Run pip install dlib It tooks several minutes so be prepared to wait

Step 5

If it completes without any errors, you're all set. Run pip install face_recognition to install face_recognition.

Result

Source https://stackoverflow.com/questions/71717231

QUESTION

how to extract self-defined ROI with dlib facelandmarks?

Asked 2022-Mar-29 at 10:31

I don't know how to extract the irregular area surrounded by green lines. i.e., the left cheek and the right cheek of a face.

...

ANSWER

Answered 2022-Mar-29 at 10:31

You can accomplish this by two simple steps:

Create a mask using the point coordinates you have
Execute bitwise_and operation (crop)

Code:

Source https://stackoverflow.com/questions/71659570

QUESTION

Why such different answers for the xgboost scikit-learn interface?

Asked 2022-Mar-21 at 22:59

I am using xgboost for the first time and trying the two different interfaces. First I get the data:

...

ANSWER

Answered 2022-Mar-21 at 22:59

First of all, you didn't specify the metric in cross_val_score, therefore you are not calculating RMSE, but rather the estimator's default metric, which is usually just its loss function. You need to specify it for comparable results:

Source https://stackoverflow.com/questions/71562186

QUESTION

Recreating Global 3D Points, from Local 3D Points and Global 2D Points; SolvePnP

Asked 2022-Mar-11 at 08:38

Aloha, i have a list of 2D Keypoints which are located in the global scope/frame (image points), and a list of corresponding 3D Keypoints in the local scope (often called texture or object points). The image points are ranging from x[0-1920]y[0,1080] and the object points are withing the range of x[-1,1]y[-1,1]. I have followed the approach described in this paper on page 6 with the tutorial from here, but the output of my 3D points is not correct at all, the movement of the points is all over the place. Below is my approach using SolvePnP. Am I on the wrong track here, since SolvePnP is normally used for detecting the camera movement (open for other suggestions!) or is my method wrong?

...

ANSWER

Answered 2022-Mar-09 at 13:13

yes, solvePnP is okay to use
yes, your math is wrong

I'll assume that you get your points from a face landmark detector, so they have a fixed order. I'll also assume that your 3D model points are given in the same order and their values are consistent and somewhat similar to the face you look at. You should exclude points that denote flesh and mandible (as opposed to skull bone). You actually want to track the skull, not the position of lips and jaws that move all over the place.

rvec is an axis-angle encoding. Its length is the amount of rotation (expected between 0 and 3.14=pi) and its direction is the axis of rotation.

Use cv.Rodrigues to turn the rvec into a 3x3 rotation matrix.

In fact, just build yourself some functions that take rvec and tvec and build a 4x4 matrix. Extending all points to be (x,y,z,1) is a hassle but only once.

And make sure you use @ for matrix multiplication (or np.dot, np.matmul, ...) because * is element-wise multiplication.

Source https://stackoverflow.com/questions/71408443

QUESTION

"ERROR: CMake must be installed to build dlib" when installing face_recognition

Asked 2022-Mar-01 at 22:50

I'm facing this error while installing face_recognition in a virtualenv with Python 3.8.10 on Ubuntu 20.04.

...

ANSWER

Answered 2022-Mar-01 at 22:49

ERROR: CMake must be installed to build dlib

Source https://stackoverflow.com/questions/71315443

QUESTION

dlib import failure on M1 pro

Asked 2022-Jan-26 at 15:04

after installing dlib successfully on my m1 pro mac (monterey 12.1)

whenever I try to import dlib, I am receiving the following error:

...

ANSWER

Answered 2022-Jan-26 at 15:04

installing dlib==19.23.0 solves this issue

Source https://stackoverflow.com/questions/70771196

QUESTION

Bounding boxes returned without detected face image in dlib python

Asked 2022-Jan-18 at 13:43

I'm trying to detect multiple faces in a picture using the deepface library with dlib as the backend detector. I'm using the DlibWrapper.py from the deeepface library and i have the following issue: In some cases, the detector returns the bounding box coordinates but doesn't return the detected face image detected face-box coordinates.

I was wondering if this bug occurs because of the negative values of some coordinates of the bounding boxes but i figured out that was not the case, as the negative values are features, not bugs. Here is the DlibWrapper from the deepface library.

...

ANSWER

Answered 2022-Jan-18 at 13:43

Solved!There are edge cases where original rectangle is partially outside the image window. That happens with dlib. So, instead of

detected_face = img[top:bottom, left:right],

the detected face should be

detected_face = img[max(0, top): min(bottom, img_height), max(0, left): min(right, img_width)]

Source https://stackoverflow.com/questions/70754730

QUESTION

Overloading member operator,?

Asked 2021-Dec-26 at 19:01

#include 
#include 

struct Matrix;

struct literal_assignment_helper
{
    mutable int r;
    mutable int c;
    Matrix& matrix;

    explicit literal_assignment_helper(Matrix& matrix)
            : matrix(matrix), r(0), c(1) {}

    const literal_assignment_helper& operator,(int number) const;
};

struct Matrix
{
    int rows;
    int columns;
    std::vector numbers;

    Matrix(int rows, int columns)
        : rows(rows), columns(columns), numbers(rows * columns) {}

    literal_assignment_helper operator=(int number)
    {
        numbers[0] = number;
        return literal_assignment_helper(*this);
    }

    int* operator[](int row) { return &numbers[row * columns]; }
};

const literal_assignment_helper& literal_assignment_helper::operator,(int number) const
{
    matrix[r][c] = number;

    c++;
    if (c == matrix.columns)
        r++, c = 0;

    return *this;
};


int main()
{
    int rows = 3, columns = 3;

    Matrix m(rows, columns);
    m = 1, 2, 3,
        4, 5, 6,
        7, 8, 9;

    for (int i = 0; i < rows; i++)
    {
        for (int j = 0; j < columns; j++)
            std::cout << m[i][j] << ' ';
        std::cout << std::endl;
    }
}

...

ANSWER

Answered 2021-Dec-26 at 19:01

const literal_assignment_helper& operator,(int number) const;

Source https://stackoverflow.com/questions/70489004

QUESTION

Sorting a tensor list in ascending order

Asked 2021-Dec-05 at 21:29

I am working on a facial comparison app that will give me the closest n number of faces to my target face.

I have done this with dlib/face_recognition as it uses numpy arrays, however i am now trying to do the same thing with facenet/pytorch and running into an issue because it uses tensors.

I have created a database of embeddings and I am giving the function one picture to compare to them. What i would like is for it to sort the list from lowest distances to highest, and give me the lowest 5 results or so.

here is the code I am working on that is doing the comparison. at this point i am feeding it a photo and asking it to compare against the embedding database.

...

ANSWER

Answered 2021-Dec-05 at 16:43

Unfortunately I cannot test your code, but to me it seems like you are operation on a python list of tuples. You can sort that by using a key:

Source https://stackoverflow.com/questions/70232894

QUESTION

How to shrink/expand facial features using Opencv?

Asked 2021-Nov-10 at 06:09

I'm trying write an application make parts of face image bigger or smaller with opencv and dlib. I detect facial landmarks using shape_predictor_68_face_landmarks.dat. In the following function, the tmp variable is supposed to be transformed in such a way that scale nose or left eye on image.

...

ANSWER

Answered 2021-Nov-10 at 06:09

Update #1

Applying pinch and bulge distortion along facial landmarks in small amounts around eyes and nose could probably provide decent results without moving into another method. Though there is a chance it will also noticeably distort eyeglasses if it affects a larger area. These should help,

I am not sure how to do this in opencv alone without face looking unnatural. Below is a general explanation based on my own exploration. Feel free to correct me if anything if I made any mistake.

3D Mesh

One way I think current face beautification methods such as those on Android cameras work is to align a 3d face mesh or a whole head model on top of original face.

It extracts face texture using using facial landmarks and aligns them with corresponding 3d mesh with texture applied to it. This way the 3d mesh can be adjusted and texture will follow face geometry. There are probably additional steps such as passing the result to another network, post-processing involved to make it look more natural.

Mediapipe Face Mesh will probably be helpful also as it provides dense 3d face landmarks with 3D face models, UV visualization, coordinates. This is a discussion for UV unwrapping of face in mediapipe.

Example from, https://github.com/YadiraF/DECA.

Example from, https://github.com/sicxu/Deep3DFaceRecon_pytorch.

GAN

Another way is to use GANs to edit facial features, apply lighting, makeup etc.

Example from, https://github.com/run-youngjoo/SC-FEGAN.

Another example, https://github.com/genforce/idinvert_pytorch.

Source https://stackoverflow.com/questions/69887034

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