app.py

from flask import Flask
from flask import request, render_template
from flask_cors import CORS, cross_origin
import base64
import cv2
import numpy as np
from statistics import mode
from keras.models import load_model
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
import librosa
import librosa.display
import numpy as np
import tensorflow as tf
from matplotlib.pyplot import specgram
import keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from sklearn.metrics import confusion_matrix
import pyaudio
import wave
from keras.models import model_from_json

tf.keras.backend.clear_session()
print("IMPORTED")
emotion_model_path = './models/fer2013_mini_XCEPTION.102-0.66.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 = []
print("Loaded model from disk")
global graph
graph = tf.get_default_graph()

def data_uri_to_cv2_img(uri):
    encoded_data = uri.split(',')[1]
    nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return img

def emotion_face(bgr_image_base64):
    bgr_image = data_uri_to_cv2_img(bgr_image_base64)
    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)
        with graph.as_default():
        	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]
        print("emotion_text", emotion_text)
        return emotion_text

app = Flask(__name__)
cors = CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'

@app.route('/')
def hello():
    return "HELLO"

@app.route('/face', methods=['POST'])
def face():
	res = emotion_face(request.form["data"])
	print(res)
	return res

if __name__ == '__main__':
    app.run(debug=True)