# author: Arun Ponnusamy
# website: https://www.arunponnusamy.com

# import necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
from keras.utils import get_file
import numpy as np
import argparse
import cv2
import sys
import os
import cvlib as cv

# download pre-trained model file (one-time download)
dwnld_link = "https://s3.ap-south-1.amazonaws.com/arunponnusamy/pre-trained-weights/gender_detection.model"
model_path = get_file("gender_detection.model", dwnld_link,
                     cache_subdir="pre-trained", cache_dir=os.getcwd())
                     
# load model
model = load_model(model_path)

while True:
    print("image:\n")
    imgname = sys.stdin.readline()
    if not imgname:
        break

    # read input image
    image = cv2.imread("t:\\WIKI-IMG-set\\wiki\\errors\\"+imgname.strip())


    if image is None:
        print("Could not read input image")
        exit()


    # load pre-trained model
    model = load_model(model_path)

    # detect faces in the image
    face, confidence = cv.detect_face(image)

    classes = ['man','woman']

    # loop through detected faces
    for idx, f in enumerate(face):

         # get corner points of face rectangle
        (startX, startY) = f[0], f[1]
        (endX, endY) = f[2], f[3]

        # draw rectangle over face
        cv2.rectangle(image, (startX,startY), (endX,endY), (0,255,0), 2)

        # crop the detected face region
        face_crop = np.copy(image[startY:endY,startX:endX])

        # preprocessing for gender detection model
        face_crop = cv2.resize(face_crop, (96,96))
        face_crop = face_crop.astype("float") / 255.0
        face_crop = img_to_array(face_crop)
        face_crop = np.expand_dims(face_crop, axis=0)

        # apply gender detection on face
        conf = model.predict(face_crop)[0]
        print(conf)
        print(classes)

        # get label with max accuracy
        idx = np.argmax(conf)
        label = classes[idx]

        label = "{}: {:.2f}%".format(label, conf[idx] * 100)

        Y = startY - 10 if startY - 10 > 10 else startY + 10

        # write label and confidence above face rectangle
        cv2.putText(image, label, (startX, Y),  cv2.FONT_HERSHEY_SIMPLEX,
                    0.7, (0, 255, 0), 2)

    # display output
    cv2.imshow("gender detection", image)

    # press any key to close window
    cv2.waitKey()

    # save output
    cv2.imwrite("gender_detection.jpg", image)

    # release resources
    cv2.destroyAllWindows()
