import cv2
from deepface import DeepFace
import math
import time
import argparse


def getFaceBox(net, frame,conf_threshold = 0.75):
    frameOpencvDnn = frame.copy()
    frameHeight = frameOpencvDnn.shape[0]
    frameWidth = frameOpencvDnn.shape[1]
    blob = cv2.dnn.blobFromImage(frameOpencvDnn,1.0,(300,300),[104, 117, 123], True, False)

    net.setInput(blob)
    detections = net.forward()
    bboxes = []

    for i in range(detections.shape[2]):
        confidence = detections[0,0,i,2]
        if confidence > conf_threshold:
            x1 = int(detections[0,0,i,3]* frameWidth)
            y1 = int(detections[0,0,i,4]* frameHeight)
            x2 = int(detections[0,0,i,5]* frameWidth)
            y2 = int(detections[0,0,i,6]* frameHeight)
            bboxes.append([x1,y1,x2,y2])
            cv2.rectangle(frameOpencvDnn,(x1,y1),(x2,y2),(0,255,0),int(round(frameHeight/150)),8)

    return frameOpencvDnn , bboxes




faceProto = "opencv_face_detector.pbtxt"
faceModel = "opencv_face_detector_uint8.pb"

ageProto = "age_deploy.prototxt"
ageModel = "age_net.caffemodel"

genderProto = "gender_deploy.prototxt"
genderModel = "gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
genderList = ['Male', 'Female']


#load the network
ageNet = cv2.dnn.readNet(ageModel,ageProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)
faceNet = cv2.dnn.readNet(faceModel, faceProto)

cap = cv2.VideoCapture(0)
padding = 20

while cv2.waitKey(1) < 0:
    #read frame
    t = time.time()
    hasFrame , frame = cap.read()
    
    result = DeepFace.analyze(frame, actions = ['emotion'], enforce_detection = False)

    if not hasFrame:
        cv2.waitKey()
        break
    #creating a smaller frame for better optimization
    #small_frame = cv2.resize(frame,(0,0),fx = 0.8,fy = 0.8)
    
    

    frameFace ,bboxes = getFaceBox(faceNet,frame)
    if not bboxes:
        print("No face Detected, Checking next frame")
        continue
    for bbox in bboxes:
        face = frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),
                max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]
        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
        genderNet.setInput(blob)
        genderPreds = genderNet.forward()
        gender = genderList[genderPreds[0].argmax()]
        print("Gender : {}, conf = {:.3f}".format(gender, genderPreds[0].max()))

        emotion = result['dominant_emotion']
        
        ageNet.setInput(blob)
        agePreds = ageNet.forward()
        age = ageList[agePreds[0].argmax()]
        print("Age Output : {}".format(agePreds))
        print("Age : {}, conf = {:.3f}".format(age, agePreds[0].max()))

        label = "{},{},{}".format(gender, age, emotion)
        cv2.putText(frameFace, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
        cv2.imshow("Age Gender Emotion Detector", frameFace)
       
    print("time : {:.3f}".format(time.time() - t))


    




        
        