playground.py

# To use Inference Engine backend, specify location of plugins:
# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH
import argparse
import random
import threading
import time

import cv2 as cv
import playsound
import pyttsx3


def thread_second():
    while 1:
        playsound.playsound('audio3.mp3', True)
        time.sleep(5)


def counter(jumpingJacks):
    engine = pyttsx3.init()
    engine.say(str(jumpingJacks))

    def thread_test():
        engine.runAndWait()

    processThread = threading.Thread(target=thread_test)  # <- note extra ','
    processThread.start()


processThread = threading.Thread(target=thread_second)  # <- note extra ','
# processThread.start()

parser = argparse.ArgumentParser()
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--thr', default=0.2, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
parser.add_argument('--jumpingQuantity', default=0, type=int, help='Enter number of jumping jacks')

args = parser.parse_args()

BODY_PARTS = {"Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
              "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
              "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
              "LEye": 15, "REar": 16, "LEar": 17, "Background": 18}

POSE_PAIRS = [["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
              ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
              ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
              ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
              ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"]]

jumpingJacks = 0
armsDown = True

prevThreeArmsDown = [False, False, False]

inWidth = args.width
inHeight = args.height
jumpingQuan = args.jumpingQuantity

net = cv.dnn.readNetFromTensorflow("graph_opt.pb")

cap = cv.VideoCapture(args.input if args.input else 0)

while cv.waitKey(1) < 0:
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break

    frameWidth = frame.shape[1]
    frameHeight = frame.shape[0]

    net.setInput(cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False))
    out = net.forward()
    out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elements

    assert (len(BODY_PARTS) == out.shape[1])

    points = []
    for i in range(len(BODY_PARTS)):
        # Slice heatmap of corresponging body's part.
        heatMap = out[0, i, :, :]

        # Originally, we try to find all the local maximums. To simplify a sample
        # we just find a global one. However only a single pose at the same time
        # could be detected this way.
        _, conf, _, point = cv.minMaxLoc(heatMap)
        x = (frameWidth * point[0]) / out.shape[3]
        y = (frameHeight * point[1]) / out.shape[2]
        # Add a point if it's confidence is higher than threshold.
        points.append((int(x), int(y)) if conf > args.thr else None)

    pointsSeen = 0
    for point in points:
        if point != None:
            pointsSeen += 1

    prevThreeArmsDown[2] = prevThreeArmsDown[1]
    prevThreeArmsDown[1] = prevThreeArmsDown[0]
    prevThreeArmsDown[0] = armsDown

    armsDown = (pointsSeen > 8)

    if armsDown == False and prevThreeArmsDown[0] == False and prevThreeArmsDown[1] == True and prevThreeArmsDown[
        2] == True:
        jumpingJacks += 1
        if jumpingQuan > 0:
            counter(jumpingQuan - jumpingJacks)
            counter("more to go")
            r1 = random.randint(0, 11)
            if r1 < 4:
                if r1 == 3:
                    counter("Come on")
                elif r1 == 2:
                    counter("You got this")
                elif r1 == 1:
                    counter("Almost done!")
                else:
                    counter("Move your butt loser.")

            if jumpingQuan <= jumpingJacks:
                counter("You are done and now awake! Have a good day.")
                exit()

    print("Jumping Jacks: ", jumpingJacks)

    for pair in POSE_PAIRS:

        partFrom = pair[0]
        partTo = pair[1]
        assert (partFrom in BODY_PARTS)
        assert (partTo in BODY_PARTS)

        idFrom = BODY_PARTS[partFrom]
        idTo = BODY_PARTS[partTo]

        if points[idFrom] and points[idTo]:
            cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 3)
            cv.ellipse(frame, points[idFrom], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)
            cv.ellipse(frame, points[idTo], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)

    t, _ = net.getPerfProfile()
    freq = cv.getTickFrequency() / 1000
    cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))

    cv.imshow('Alarm time - Do jumping jacks', frame)