8.Trainer

By -

 On this second phase, we must take all user data from our dataset and “trainer” the OpenCV Recognizer. This is done directly by a specific OpenCV function. The result will be a .yml file that will be saved on a “trainer/” directory.

Comments

Post a Comment

Popular posts from this blog

3.Installing OpenCV Package

By -
Image
Once you have OpenCV installed in your RPi let’s test to confirm that your camera is working properly. I am assuming that you have a PiCam already installed and enabled on your Raspberry Pi. Enter the below Python code on your IDE: import numpy as np import cv2 cap = cv2.VideoCapture(0) cap.set(3,640) # set Width cap.set(4,480) # set Height while(True): ret, frame = cap.read() frame = cv2.flip(frame, -1) # Flip camera vertically gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('frame', frame) cv2.imshow('gray', gray) k = cv2.waitKey(30) & 0xff if k == 27: # press 'ESC' to quit break cap.release() cv2.destroyAllWindows() The above code will capture the video stream that will be generated by your PiCam, displaying both, in BGR color and Gray mode. Note that I rotated my camera vertically due the way it is assembled. If it is not your case, comment or delete the “flip” command line. You can alternatively down...
Read more

1.Face Detection

By -
Image
  The most basic task on Face Recognition is of course, “Face Detecting”. Before anything, you must “capture” a face (Phase 1) in order to recognize it, when compared with a new face captured on future (Phase 3). The most common way to detect a face (or any objects), is using the “ Haar Cascade classifier ” Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. The good news is that OpenCV comes with a trainer as well...
Read more