human
Give me the Python code with OpenCV libraries and so on from the extracted Genki4 face images (that is, read the images and extract the faces with haarcascade_frontalface_default.xml) to extract the HOG and LBP features and equalize the x function and the labels from Read another file and put it equal to the y vector. For this purpose, you can use the Scikit-image library, which has functions for extracting these features. After forming the feature matrix for all dataset images, use the SVM algorithm. This algorithm is available in Scikit-learn library. Then save the trained model with the pickle or joblib library to load it in the test program on the video.
I want the accuracy to be printed and only 50 images to be read.
Also, the svm kernel must be rbf.
  I don't have any files in npy format. But I have the labels file, the first 5 lines of which are as follows:
1 -0.021162 0.059530 -0.039662
1 -0.057745 0.083098 -0.094952
1 0.095993 0.028798 0.065996
1 0.000000 0.047124 0.171268
1 0.036073 0.043633 -0.181721

And the names of the files are with the following pattern:
file0001.jpg
file0002.jpg
...
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open