dlib | making real world machine learning and data analysis | Machine Learning library
kandi X-RAY | dlib Summary
kandi X-RAY | dlib Summary
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See for the main project documentation and API reference.
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Lines of Code : 8License : Strong Copyleft (CC BY-SA 4.0)
Copycross_val_score(xgbr, X, y, cv=5, scoring = 'neg_root_mean_squared_error')
from sklearn.model_selection import KFold
cv_results = xgb.cv(dtrain=dmatrix, params=params, metrics={'rmse'}, folds = KFold(n_splits=5))ERROR: CMake must be installed to build dlib
import dlib
from PIL import Image
from skimage import io
h, w, c = sample_img.shape
print('width: ', w)
print('height: ', h)
xleft = data.xmin*w
xleft = int(xleft)
xtop = data.ymin*h
xtop namestodistance = [('Alice', .1), ('Bob', .3), ('Carrie', .2)]
names_top = sorted(namestodistance, key=lambda x: x[1])
print(names_top[:2])
namestodistance = list(map(lambda x: (x[0], x[1].item()), namestodistance)locations = face_recognition.face_locations(frame, model="hog")
face3Dmodel = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corner
(-150.0, -150.0,CMD python3 manage.py migrate && python3 manage.py runserver 0.0.0.0:8000
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
for (top, right, bottom, left), name in zip(faceLocations, faceNames):
cv2.rectangle(img, int(left)-20, int(top)-20, int(right)+20, int(bottom)+20), (255, 0, 0), cv2.FILLED)
akaze = cv2.AKAZE_create()
Community Discussions
Trending Discussions on dlib
QUESTION
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 09:50Follow the steps below to install face_recognition python package on Windows 10.
The instruction has been tested on Windows 10 64bit, python 3.9.
Download CMake installation package for your OS from official site
Step 2Install downloaded CMake installation package. Please make sure that Add CMake to system PATH option has been selected during the installation.
Reboot your OS (restart computer)
Step 4Run pip install dlib
It tooks several minutes so be prepared to wait
If it completes without any errors, you're all set. Run pip install face_recognition to install face_recognition.
Result
QUESTION
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 10:31You can accomplish this by two simple steps:
- Create a mask using the point coordinates you have
- Execute bitwise_and operation (crop)
Code:
QUESTION
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 22:59First 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:
QUESTION
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,
solvePnPis 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.
QUESTION
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 22:49ERROR: CMake must be installed to build dlib
QUESTION
ANSWER
Answered 2022-Jan-26 at 15:04installing dlib==19.23.0 solves this issue
QUESTION
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 13:43Solved!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)]
QUESTION
#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 19:01const literal_assignment_helper& operator,(int number) const;
QUESTION
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 16:43Unfortunately 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:
QUESTION
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 06:09Applying 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,
- Pinch/bulge distortion using Python OpenCV
- Image Warping - Bulge Effect Algorithm
- https://math.stackexchange.com/questions/266250/explanation-of-this-image-warping-bulge-filter-algorithm
- Formulas for Barrel/Pincushion distortion
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 MeshOne 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. GANAnother 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.Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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