run_classifier.py

import sys

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

from inference import detect_faces
from inference import draw_text
from inference import draw_bounding_box
from inference import apply_offsets
from inference import load_detection_model
from inference import load_image


emotion_labels = {0:'angry',1:'disgust',2:'fear',3:'happy',
                4:'sad',5:'surprise',6:'neutral'}
# parameters for loading data and images
image_path = sys.argv[1]
detection_model_path = 'haarcascade_frontalface_default.xml'
emotion_model_path = 'emotion.h5'

font = cv2.FONT_HERSHEY_SIMPLEX

# hyper-parameters for bounding boxes shape
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# loading images
rgb_image = load_image(image_path, grayscale=False)
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')

faces = detect_faces(face_detection, gray_image)
print("Found " + str(len(faces)) + " faces")

for face_coordinates in faces:
    x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
    rgb_face = rgb_image[y1:y2, x1:x2]    
    gray_face = gray_image[y1:y2, x1:x2]

    try:
        gray_face = cv2.resize(gray_face, (emotion_target_size))
    except:
        print("Error resizing")
        continue

    
    rgb_face = np.expand_dims(rgb_face, 0)

    #gray_face = preprocess_input(gray_face)
    gray_face = np.expand_dims(gray_face, 0)
    gray_face = np.expand_dims(gray_face, -1)
    emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
    emotion_text = emotion_labels[emotion_label_arg]

    color = (255, 0, 0)

    draw_bounding_box(face_coordinates, rgb_image, color)
    draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)

bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imwrite('predicted.png', bgr_image)