'''
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.
'''

# USAGE
# python mask_detect_image.py --image demo_image/1.jpeg
from privacy.config.logger import CustomLogger
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
import numpy as np
import argparse
import cv2
import os
log = CustomLogger()

def mask_image():
    # construct the argument parser and parse the arguments
    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--image", required=True, 
                        help="Path to input image")
    parser.add_argument("-f", "--face", type=str, default="face_detector", 
                        help="Path to face detector model directory")
    parser.add_argument("-m", "--model", type=str, default="mask_detector.model", 
                        help="Path to trained face mask detector model")
    parser.add_argument('-s', '--size', type=int, default=64, 
                        help="Size of face image")
    parser.add_argument("-c", "--confidence", type=float, default=0.5, 
                        help="Minimum probability to filter weak detections")
    args = parser.parse_args()

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

    # load the face mask detector model from disk
    model = load_model(args.model)

    image = cv2.imread(args.image)
    if image is None:
        print('Can not read file: %s' % args.image)
        return

    (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 < args.confidence:
            # 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))

        try:
            face = image[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            face = cv2.resize(face, (args.size, args.size))
            face = img_to_array(face)
            face = preprocess_input(face)
            face = np.expand_dims(face, axis=0)

            mask = model.predict(face)[0]

            label = "Mask" if mask < 0.5 else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            # display the label and bounding box rectangle on the output frame
            cv2.putText(image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 1)
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
        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)

if __name__ == "__main__":
    mask_image()