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# import the necessary packages
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2
import os


def detect_and_predict_age(frame, faceNet, ageNet, minConf=0.5):
    # define the list of age buckets our age detector will predict
    AGE_BUCKETS = ["(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)",
                   "(38-43)", "(48-53)", "(60-100)"]

    # initialize our results list
    results = []

    # grab the dimensions of the frame and then construct a blob from it
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0))

    # pass the blob through the network and obtain the face detections
    faceNet.setInput(blob)
    detections = faceNet.forward()
    
    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with the prediction
        confidence = detections[0, 0, i, 2]

        # filter out weak detections by ensuring the confidence is greater than the minimum confidence
        if confidence > minConf:
            # compute the (x, y)-coordinates of the bounding box for the object
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # extract the ROI of the face
            face = frame[startY:endY, startX:endX]
            
            # ensure the face ROI is sufficiently large
            if face.shape[0] < 20 or face.shape[1] < 20:
                continue
                
            # construct a blob from *just* the face ROI
            faceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227),
                                             (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)

            # make predictions on the age and find the age bucket with the largest corresponding probability
            ageNet.setInput(faceBlob)
            preds = ageNet.forward()
            i = preds[0].argmax()
            age = AGE_BUCKETS[i]
            ageConfidence = preds[0][i]
            # construct a dictionary consisting of both the face bounding box location along with the age prediction,
            # then update our results list
            d = {"loc": (startX, startY, endX, endY),"age": (age, ageConfidence)}
                 
            results.append(d)
                 
    # return our results to the calling function
    return results


# +
#load the files and directories
face = os.path.join("face_detector")
age = os.path.join("age_detector")

# load our serialized face detector model from disk
prototxtPath = os.path.sep.join([face, "deploy.prototxt"])
weightsPath = os.path.sep.join([face,
	"res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

# load our serialized age detector model from disk
prototxtPath = os.path.sep.join([age, "age_deploy.prototxt"])
weightsPath = os.path.sep.join([age, "age_net.caffemodel"])
ageNet = cv2.dnn.readNet(prototxtPath, weightsPath)
# -

conf = 0.5
# initialize the video stream and allow the camera sensor to warm up
vs = VideoStream(src=0).start()
time.sleep(2.0)

# +
# loop over the frames from the video stream
while True:
    # grab the frame from the threaded video stream and resize it to have a maximum width of 400 pixels
    frame = vs.read()
    frame = imutils.resize(frame,width = 400)
    
    # detect faces in the frame, and for each face in the frame, predict the age
    results = detect_and_predict_age(frame,faceNet,ageNet,minConf=conf)
    
    # loop over the results
    for r in results:
        # draw the bounding box of the face along with the associated predicted age
        text = "{}: {:.2f}%".format(r["age"][0], r["age"][1] * 100)
        (startX, startY, endX, endY) = r["loc"]
        y = startY - 10 if startY - 10 > 10 else startY + 10
        cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 0, 255), 2)
        cv2.putText(frame, text, (startX, y), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
    
    # show the output frame
    cv2.imshow("frame",frame)
    key = cv2.waitKey(1) & 0xFF
    
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

# -



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