from keras.preprocessing.image import img_to_array
from datetime import datetime
import imutils
import cv2
import mysql.connector
import time
from keras.models import load_model
import numpy as np


# parameters for loading data and images
timeDelay = 0
detection_model_path = 'haarcascade/haarcascade_frontalface_default.xml'
emotion_model_path = 'cnnmodel/_mini_XCEPTION.102-0.66.hdf5'
mydb = mysql.connector.connect(
  host="localhost",
  user="root",
  passwd="",
  database="datas"
)
mycursor = mydb.cursor()


face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
 "neutral"]





camera = cv2.VideoCapture(0)
while True:
    frame = camera.read()[1]
    #reading the frame
    frame = imutils.resize(frame,width=900,height=900)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)
    
    canvas = np.zeros((250, 300, 3), dtype="uint8")
    frameClone = frame.copy()
    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 = gray[fY:fY + fH, fX:fX + fW]
        roi = cv2.resize(roi, (64, 64))
        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()]
    else: continue

 
    for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
                # construct the label text
                text = "{}: {:.2f}%".format(emotion, prob * 100)


                
                w = int(prob * 300)
                cv2.rectangle(canvas, (7, (i * 35) + 5),
                (w, (i * 35) + 35), (0, 255, 0), -1)
                cv2.putText(canvas, text, (10, (i * 35) + 23),
                cv2.FONT_HERSHEY_SIMPLEX, 0.45,
                (255, 0, 255), 2)
                cv2.putText(frameClone, label, (fX, fY - 10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
                cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
                              (255, 0, 0), 2)

    cv2.imshow('Employee Mood Checker', frameClone)
    cv2.imshow("Mood Detector", canvas)
    timeDelay += 1
    if timeDelay == 50:
        now = datetime.now()
        current_time = now.strftime("%H:%M:%S")
        sql = "INSERT INTO emotion (emoji) VALUES ('{}')".format(label)
        print(sql)
        mycursor.execute(sql)
        mydb.commit()
        print(mycursor.rowcount, "record inserted.")
        timeDelay = 0
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

camera.release()
cv2.destroyAllWindows()