facial_emotion_image.py

from keras.preprocessing.image import img_to_array
from keras.models import load_model
import imutils
import cv2
import numpy as np
import sys

# parameters for loading data and images
detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
emotion_model_path = 'models/_mini_XCEPTION.106-0.65.hdf5'
img_path = sys.argv[1]

# hyper-parameters for bounding boxes shape
# loading models
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ["angry","disgust","scared", "happy", "sad", "surprised","neutral"]


#reading the frame
orig_frame = cv2.imread(img_path) 
frame = cv2.imread(img_path,0)
faces = face_detection.detectMultiScale(frame,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)

if len(faces) > 0:
    faces = sorted(faces, reverse=True,key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
    (fX, fY, fW, fH) = faces
    roi = frame[fY:fY + fH, fX:fX + fW]
    roi = cv2.resize(roi, (48, 48))
    roi = roi.astype("float") / 255.0
    roi = img_to_array(roi)
    roi = np.expand_dims(roi, axis=0)
    preds = emotion_classifier.predict(roi)[0]
    emotion_probability = np.max(preds)
    label = EMOTIONS[preds.argmax()]
    cv2.putText(orig_frame, label, (fX, fY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
    cv2.rectangle(orig_frame, (fX, fY), (fX + fW, fY + fH),(0, 0, 255), 2)
    
cv2.imshow('test_face', orig_frame)
cv2.imwrite('test_output/'+img_path.split('/')[-1],orig_frame)
if (cv2.waitKey(2000) & 0xFF == ord('q')):
    sys.exit("Thanks")
cv2.destroyAllWindows()