emotions.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
from keras.models import load_model
from statistics import mode
from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

USE_WEBCAM = True
emotion_model_path = './models/emotion_model.hdf5'
emotion_labels = get_labels('fer2013')
frame_window = 10
emotion_offsets = (20, 40)
face_cascade = cv2.CascadeClassifier('./models/haarcascade_frontalface_default.xml')
emotion_classifier = load_model(emotion_model_path)
emotion_target_size = emotion_classifier.input_shape[1:3]
emotion_window = []
graph = []
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
cap = None
if (USE_WEBCAM == True):
    cap = cv2.VideoCapture(0)
else:
    cap = cv2.VideoCapture('./demo/dinner.mp4')

while cap.isOpened():
    ret, bgr_image = cap.read()

    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)

    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5,
			minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)

    for face_coordinates in faces:

        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]
        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_prediction = emotion_classifier.predict(gray_face)
        emotion_probability = np.max(emotion_prediction)
        emotion_label_arg = np.argmax(emotion_prediction)
        graph.append(emotion_label_arg)
        emotion_text = emotion_labels[emotion_label_arg]
        emotion_window.append(emotion_text)

        if len(emotion_window) > frame_window:
            emotion_window.pop(0)
        try:
            emotion_mode = mode(emotion_window)
        except:
            continue

        if emotion_text == 'angry':
            color = emotion_probability * np.asarray((255, 0, 0))
        elif emotion_text == 'sad':
            color = emotion_probability * np.asarray((0, 0, 255))
        elif emotion_text == 'happy':
            color = emotion_probability * np.asarray((255, 255, 0))
        elif emotion_text == 'surprise':
            color = emotion_probability * np.asarray((0, 255, 255))
        else:
            color = emotion_probability * np.asarray((0, 255, 0))

        color = color.astype(int)
        color = color.tolist()

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_mode,
                  color, 0, -45, 1, 1)

    bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imshow('window_frame', bgr_image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

g=[]
for i in [0,3,4,5,6]:
    a = graph.count(i)
    g.append(a)

p = g.index(max(g))
d = {0:"Angry",1:"Happy",2:"Sad",3:"Surprise",4:"Neutral"}
v = d.get(p)
print("\n\n\nThe predominant emotion during the analysed timeframe is..  "+v+"\n\n\n")

plt.subplot(2,1,1)

height = [3, 12, 5, 18, 45]
bars = ('Angry', 'Happy', 'Sad', 'Surprise', 'Neutral')
y_pos = np.arange(len(bars))
plt.bar(y_pos, g, color = (0.5,0.1,0.5,0.6))

#plt.title('Summary')
#plt.xlabel('Emotions')
plt.ylabel('Instances')
plt.xticks(y_pos, bars)


plt.subplot(2,1,2)

j=0
for i in graph:
    if i==0:
        plt.plot(j,1,'r.')
    else:
        plt.plot(j,i-1,'r.')
    j+=1

#plt.title('Summary')
#plt.ylabel('Emotions')
plt.xlabel('frames')
bars = ('','Angry', 'Happy', 'Sad', 'Surprise', 'Neutral','')
x_pos = np.arange(len(bars))
plt.yticks(x_pos,bars)

plt.tight_layout()

plt.show()