facial-detection-128.py

import numpy as np
import cv2
import time
import datetime

# import the cascade for face detection
face_cascade = cv2.CascadeClassifier("cascades/haarcascade_frontalface_default.xml")

def TakeSnapshotAndSave():
    # access the webcam (every webcam has a number, the default is 0)
    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

    count = 0
    anterior = 0

    # while count < 100:
    while(cap.isOpened()):
        if count % 30 == 0:
            print(count, datetime.datetime.now())

        # Capture frame-by-frame
        ret, frame = cap.read()

        # to detect faces in video
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)

        for (x, y, w, h) in faces:
            print(x, y, w, h)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

        if len(faces) > 0:
            cv2.imwrite("opencv-" + str(time.time()) + ".jpg", frame)

            # Create training data
            for (x, y, w, h) in faces:
                print("training-face-128", x, y, w, h)
                cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
                cropped = frame[y:y+h, x:x+w]
                resized = cv2.resize(cropped, (128, 128), interpolation = cv2.INTER_AREA)

                cv2.imwrite("training-face-128/opencv-" + str(time.time()) + ".jpg", resized)

        if count % 100 == 0:
            cv2.imwrite("debug-" + str(count) + ".jpg", frame)

        if anterior != len(faces):
            anterior = len(faces)
            print("faces: " + str(len(faces)) + " at " + str(datetime.datetime.now()))

        count = count + 1
        time.sleep(2)

    cap.release()

if __name__ == "__main__":
    TakeSnapshotAndSave()