'''
MIT license https://opensource.org/licenses/MIT Copyright 2024 Infosys Ltd

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
'''

from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from tqdm import tqdm
from glob import glob
from privacy.config.logger import CustomLogger
import tensorflow as tf
import numpy as np
import argparse
import time
import cv2
import os
log = CustomLogger()
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input-dir', 
                    help="Directory of input image")
parser.add_argument('-o', '--output-dir', default='data/64x64_dataset', 
                    help="Where to write the extracted data")
parser.add_argument('-s', '--size', default=64, type=int, 
                    help="Size of output face image")
parser.add_argument('-c', '--confidence', default=0.5, type=float, 
                    help='Confidence threadhold to detect face')

def extract_face(filename, output_dir, net, size, confidence_threshold):
    image = cv2.imread(filename)
    filename_out = filename.split('/')[-1].split('.')[0]
    (h, w) = image.shape[:2]

    blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, mean=(104.0, 177.0, 123.0))
    net.setInput(blob)
    detections = net.forward()

    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence < confidence_threshold:
            # Drop low confidence detections
            continue

        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        (startX, startY) = (max(0, startX), max(0, startY))
        (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

        color = (0, 255, 0)
        cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

        try:
            frame = image[startY:endY, startX:endX]
            frame = cv2.resize(frame, (size, size), interpolation=cv2.INTER_AREA)
            if i > 0:
                image_out = os.path.join(output_dir, '%s_%s.jpg' % (filename_out, i))
            else:
                image_out = os.path.join(output_dir, '%s.jpg' % filename_out)
            cv2.imwrite(image_out, frame)
        except Exception as e:
                log.error(str(e))
                log.error("Line No:"+str(e.__traceback__.tb_lineno))
    
    # show the output image
    cv2.imshow("Output", image)
    cv2.waitKey(0)

def app():
    args = parser.parse_args()

    assert os.path.isdir(args.input_dir), "Couldn't find the dataset at {}".format(args.input_dir)
    os.makedirs(args.output_dir, exist_ok=True)

    prototxt_path = os.path.join('face_detector', 'deploy.prototxt')
    weights_ath = os.path.join('face_detector', 'res10_300x300_ssd_iter_140000.caffemodel')
    net = cv2.dnn.readNet(prototxt_path, weights_ath)

    input_imgs = []
    for t in ('jpeg', 'png', 'jpg'):
        input_imgs.extend(glob(args.input_dir + '/*.' + t))

    for filename in tqdm(sorted(input_imgs)):
        extract_face(filename, args.output_dir, net, args.size, args.confidence)
        # time.sleep(1)
    print("Done!")

if __name__ == '__main__':
    app()