record_faces.py

import numpy as np
import cv2

# instantiate a camera object to capture images
cam = cv2.VideoCapture(1)

# create a haar-cascade object for face detection
face_cas = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')

# create a placeholder for storing the data
data = []
ix = 0  # current frame number

while True:
    # retrieve the ret (boolean) and frame from camera
    ret, frame = cam.read()

    # if the camera is working fine, we proceed to extract the face
    if ret == True:
        # convert the current frame to grayscale
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # apply the haar cascade to detect faces in the current frame
        # the other parameters 1.3 and 5 are fine tuning parameters
        # for the haar cascade object
        faces = face_cas.detectMultiScale(gray, 1.3, 5)

        # for each face object we get, we have
        # the corner coords (x, y)
        # and the width and height of the face
        for (x, y, w, h) in faces:

            # get the face component from the image frame
			face_component = frame[y:y+h, x:x+w, :]

            # resize the face image to 50X50X3
            fc = cv2.resize(face_component, (50, 50))

            # store the face data after every 10 frames
            # only if the number of entries is less than 20
            if ix%10 == 0 and len(data) < 20:
                data.append(fc)

            # for visualization, draw a rectangle around the face
            # in the image
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        ix += 1 # increment the current frame number
        cv2.imshow('frame', frame)  # display the frame

        # if the user presses the escape key (ID: 27)
        # or the number of images hits 20, we stop
        # recording.
        if cv2.waitKey(1) == 27 or len(data) >= 20:
            break
    else:
        # if the camera is not working, print "error"
        print "error"

# now we destroy the windows we have created
cv2.destroyAllWindows()

# convert the data to a numpy format
data = np.asarray(data)

# print the shape as a sanity-check
print data.shape

# save the data as a numpy matrix in an encoded format
np.save('face_01', data)

# We'll run the script for different people and store
# the data into multiple files