lab4 no extras

# Importing libraries
import numpy as np
import cv2
import matplotlib.pyplot as plt
import argparse
from datetime import datetime

# A function for plotting the images


def plotImages(img):
    plt.imshow(img, cmap="gray")
    plt.axis('off')
    plt.style.use('seaborn')
    plt.gcf()

    # Shows the image
    plt.show()

faces = []

# Reading an image using OpenCV
# OpenCV reads images by default in BGR format
image_src = "arctic.jpg"
image = cv2.imread(image_src)

# Converting BGR image into a RGB image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# plotting the original image (remove comment to see the original image, then the image with the detected faces)
# plotImages(image)

parser = argparse.ArgumentParser(description='Face detection')
parser.add_argument('--face_cascade', help='Path to face cascade.', default='haarcascade_frontalface_alt.xml')
parser.add_argument('--face_cascade2', help='Path to face cascade 2.', default='haarcascade_frontalface_alt_tree.xml')
parser.add_argument('--face_cascade3', help='Path to face cascade 3.', default='haarcascade_frontalface_alt2.xml')
parser.add_argument('--face_cascade4', help='Path to face cascade 4.', default='haarcascade_frontalface_default.xml')
parser.add_argument('--face_cascade5', help='Path to face cascade 5.', default='haarcascade_frontalface_extended.xml')
parser.add_argument('--face_cascade6', help='Path to face cascade 6.', default='haarcascade_profileface.xml')
# parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
args = parser.parse_args()

face_cascade_name = args.face_cascade
# eyes_cascade_name = args.eyes_cascade

face_cascade = cv2.CascadeClassifier()
# eyes_cascade = cv2.CascadeClassifier()

if not face_cascade.load(cv2.samples.findFile(face_cascade_name)):
 print('--(!)Error loading face cascade')
 exit(0)

faces = face_cascade.detectMultiScale(image, 1.1, 2)

# Draw rectangle around the faces which is our region of interest (ROI)
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
    roi = image[y:y+h, x:x+w]
    # applying a gaussian blur over this new rectangle area
    roi = cv2.blur(roi, (20, 20))
    # impose this blurred image on original image to get final image

    image[y:y+roi.shape[0], x:x+roi.shape[1]] = roi

# print("Faces detected in the image:", len(faces))
# Display the output
plotImages(image)