run.py

import argparse
import os
import sys
import time

import cv2
import numpy as np
from tensorflow.keras.layers import (Conv2D, Dense, Dropout, Flatten,
                                     MaxPooling2D)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
import os
import sys
import subprocess

if sys.platform == 'linux':
    from gpiozero import CPUTemperature

# input arg parsing
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--fullscreen',
                    help='Display window in full screen', action='store_true')
parser.add_argument(
    '-d', '--debug', help='Display debug info', action='store_true')
parser.add_argument(
    '-fl', '--flip', help='Flip incoming video signal', action='store_true')
args = parser.parse_args()

# create model
model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu', input_shape=(48, 48, 1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))

model.load_weights('model.h5')

# prevents openCL usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)

# dictionary which assigns each label an emotion (alphabetical order)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful",
                3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}

def get_gpu_temp():
    temp = subprocess.check_output(['vcgencmd measure_temp | egrep -o \'[0-9]*\.[0-9]*\''],
                                    shell=True, universal_newlines=True)
    return str(float(temp))

# start the webcam feed
cap = cv2.VideoCapture(0)
while True:
    # time for fps
    start_time = time.time()

    # Find haar cascade to draw bounding box around face
    ret, frame = cap.read()
    if args.flip:
        frame = cv2.flip(frame, -1)
    if not ret:
        break
    facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = facecasc.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        cropped_img = np.expand_dims(np.expand_dims(
            cv2.resize(roi_gray, (48, 48)), -1), 0)
        prediction = model.predict(cropped_img)
        maxindex = int(np.argmax(prediction))
        emotion_label = emotion_dict[maxindex]
        cv2.putText(frame, emotion_label, (x+20, y-60),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

    # full screen
    if args.fullscreen:
        cv2.namedWindow("video", cv2.WND_PROP_FULLSCREEN)
        cv2.setWindowProperty("video", cv2.WND_PROP_FULLSCREEN, 1)

    # debug info
    if args.debug:
        fps = str(int(1.0 / (time.time() - start_time)))
        cv2.putText(frame, fps + " fps", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
        if sys.platform == 'linux':
            cpu_temp = str(int(CPUTemperature().temperature)) + " C (CPU)"
            cv2.putText(frame, cpu_temp, (20, 95), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.putText(frame, get_gpu_temp() + " C (GPU)", (20, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.imshow('video', cv2.resize(
        frame, (800, 480), interpolation=cv2.INTER_CUBIC))
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()