# A Gender and Age Detection program by Mahesh Sawant
import os
import pandas as pd
import cv2
import math
import argparse

dic = {"images": [], "gender": [], "age": []}


def highlightFace(net, frame, conf_threshold=0.7):
    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()
    faceBoxes = []
    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)
            faceBoxes.append([x1, y1, x2, y2])
            cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
    return frameOpencvDnn, faceBoxes


def process_image(image):
    # parser=argparse.ArgumentParser()
    # parser.add_argument('--image')
    #
    # args=parser.parse_args()

    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']

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

    video = cv2.VideoCapture(image)
    padding = 20
    while cv2.waitKey(1) < 0:
        try:
            hasFrame, frame = video.read()
            if not hasFrame:
                cv2.waitKey()
                break

            resultImg, faceBoxes = highlightFace(faceNet, frame)
            if not faceBoxes:
                print("No face detected")

            for faceBox in faceBoxes:
                face = frame[max(0, faceBox[1] - padding):
                             min(faceBox[3] + padding, frame.shape[0] - 1), max(0, faceBox[0] - padding)
                                                                            :min(faceBox[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(f'Gender: {gender}')

                ageNet.setInput(blob)
                agePreds = ageNet.forward()
                age = ageList[agePreds[0].argmax()]

                print(f'Age: {age[1:-1]} years')
                dic['images'].append(image)
                dic['gender'].append(gender)
                dic['age'].append(age[1:-1])

                # cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 2, cv2.LINE_AA)
                # cv2.imshow("Detecting age and gender", resultImg)
        except Exception as e:
            continue



import boto3
s3 = boto3.resource(
    service_name = 's3',
    region_name = 'ap-south-1',
    aws_access_key_id = 'AKIAYNE4X3VIWUPXM75R',
    aws_secret_access_key ='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'
) 
def download_s3_folder(bucket, folder, local_dir='./images'):
    bucket = s3.Bucket(bucket)
    for obj in bucket.objects.filter(Prefix=folder):
        target = obj.key if local_dir is None \
            else os.path.join(local_dir, os.path.relpath(obj.key, folder))
        if not os.path.exists(os.path.dirname(target)):
            os.makedirs(os.path.dirname(target))
        if obj.key[-1] == '/':
            continue
        bucket.download_file(obj.key, target)


def predict_age_gender():
    image = os.listdir('images')

    for img in image:
        img = './images/' + img
        process_image(img)
    print(dic)
    df = pd.DataFrame.from_dict(dic, orient='index').transpose()
    df.head()
    df.to_excel("./output/result_s3.xls")

download_s3_folder('genderagedata','input_images')
predict_age_gender()

s3.Bucket('genderagedata').upload_file(Filename='./output/result_s3.xls', Key='output_images/result.xls')