main.py

# Importing all the libraries
from keras.applications.mobilenet import preprocess_input
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import cv2
# Camera.py file from where camera will be initialized
from Camera import VideoCamera

# Including the prototxt and caffemodel from face_detector to detect face from the frame
open_cv_deploy = "C:\\Users\Ankit Saini\PycharmProjects\Mask and Non-Mask Detector-20200601T123150Z-001\Mask and Non-Mask Detector\Face_detector\Open-CV_deploy.prototxt"
W = "C:\\Users\Ankit Saini\PycharmProjects\Mask and Non-Mask Detector-20200601T123150Z-001\Mask and Non-Mask Detector\Face_detector\Model.caffemodel"
net = cv2.dnn.readNet(open_cv_deploy, W)
model = load_model("C:\\Users\Ankit Saini\PycharmProjects\Mask and Non-Mask Detector-20200601T123150Z-001\Mask and Non-Mask Detector\mask.model")


# Function which takes camera frame and predicts
def gen(camera):
    while True:
        img = camera.get_frame()
        img = cv2.flip(img, 1)
        (h, w) = img.shape[:2]
        blob = cv2.dnn.blobFromImage(img, 1.0, (300, 300),
                                     (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]
            # Setting the confidence to greater than 0.4
            if confidence > 0.4:
                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))
                # Preprocessing the live feed frame
                face = img[startY:endY, startX:endX]
                face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
                face = cv2.resize(face, (224, 224))
                face = img_to_array(face)
                face = preprocess_input(face)
                face = np.expand_dims(face, axis=0)
                # Predicting the frame from the loaded model
                (mask, withoutMask) = model.predict(face)[0]

                label = "Mask" if mask > withoutMask else "No Mask"
                color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

                label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

                cv2.putText(img, label, (startX, startY - 10),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
                cv2.rectangle(img, (startX, startY), (endX, endY), color, 10)
        cv2.imshow("Frame", img)
        interrupt = cv2.waitKey(10)
        if interrupt & 0xFF == 27:
            break


# Calling the above function
gen(VideoCamera())