from keras.preprocessing.image import img_to_array

from keras.models import load_model

import imutils

import cv2.cv2 as cv2

import numpy as np

import sys

import time

# 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,

flags=cv2.CASCADE_SCALE_IMAGE)

if len(faces) > 0:

token = 0

for f in faces:

face = sorted(faces, reverse=True,key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[token]

(fX, fY, fW, fH) = face

token += 1

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('test2_output/'+img_path.split('/')[-1],orig_frame)

if (cv2.waitKey(2000) & 0xFF == ord('q')):

sys.exit("Thanks")

time.sleep(10)

cv2.destroyAllWindows()