main.py

# -*- coding: utf-8 -*-
"""
Created on Tue May 15 11:50:00 2018

@author: mbilkhu
"""

import cv2
from keras.models import load_model
import numpy as np
from statistics import mode

# Some Utility Functions
def get_labels():
    return {0:'angry',1:'disgust',2:'fear',3:'happy',
        4:'sad',5:'surprise',6:'neutral'}

def load_detection_model(model_path):
    detection_model = cv2.CascadeClassifier(model_path)
    return detection_model

def detect_faces(detection_model, gray_image_array):
    return detection_model.detectMultiScale(gray_image_array, 1.3, 5)

def draw_bounding_box(face_coordinates, image_array, color):
    x, y, w, h = face_coordinates
    cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)

def apply_offsets(face_coordinates, offsets):
    x, y, width, height = face_coordinates
    x_off, y_off = offsets
    return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)

def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,
                                                font_scale=2, thickness=2):
    x, y = coordinates[:2]
    cv2.putText(image_array, text, (x + x_offset, y + y_offset),
                cv2.FONT_HERSHEY_SIMPLEX,
                font_scale, color, thickness, cv2.LINE_AA)

def preprocess_input(x, v2=True):
    x = x.astype('float32')
    x = x / 255.0
    if v2:
        x = x - 0.5
        x = x * 2.0
    return x

detection_model_path = 'haarcascade_frontalface_default.xml'
emotion_model_path = 'model.hdf5'
emotion_labels = get_labels()

frame_window = 10
emotion_offsets = (20, 40)

face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
emotion_target_size = emotion_classifier.input_shape[1:3]

emotion_window = []

cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
while True:
    bgr_image = video_capture.read()[1]
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
    faces = detect_faces(face_detection, gray_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)
        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