8.Trainer
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.
3.Installing OpenCV Package
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
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