streamlit_app.py

# import all required libaries
import streamlit as st
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 cv2
import os
from PIL import Image
st.set_option('deprecation.showfileUploaderEncoding', False)

# st.cache allow our app to stay performant (run fast) since
# we will be loading images
@st.cache(hash_funcs={cv2.dnn_Net: hash})
# this is a helper function to load our face detector model
# from desk
def load_face_detector_model():
    prototxt_path = os.path.sep.join(
        ["face_detector", "deploy.prototxt"])
    weight_path = os.path.sep.join(
        ['face_detector', 'res10_300x300_ssd_iter_140000.caffemodel'])

    net = cv2.dnn.readNet(prototxt_path, weight_path)

    return net


# This will make the app stay performant
@st.cache(allow_output_mutation=True)
# helper function to load our face mask detector model
def load_mask_model():

    mask_model = load_model("mask_detector.model")

    return mask_model


net = load_face_detector_model() # load face detector model
model = load_mask_model() # load mask detector model
confidence_selected = st.sidebar.slider(
    'Select a confidence range', 0.0, 0.1, 0.5, 0.1) # display button to adjust 'confidence' between 0 - 0.5

# Helper function to load the image and loop over the detection
def detect_mask(image):

    image = cv2.imdecode(np.fromstring(image.read(), np.uint8), 1)  #read the image from tempoary memory
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # convert image from BGR to RGB
    orig = image.copy() # get a copy of the image
    (h, w) = image.shape[:2] # get image height and weight
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), # construct a blob from the image
                                 (104.0, 177.0, 123.0))
    net.setInput(blob)  # pass the blob through the detection, get region that differ in propertes, and the face region
    detection = net.forward() 

    for i in range(0, detection.shape[2]): # loop through the detection

        confidence = detection[0, 0, i, 2] # extract confidence vaalue

        if confidence > confidence_selected: # if the confidence is greater than the selected confidence from the side bar

            box = detection[0, 0, i, 3:7] * np.array([w, h, w, h]) # get x and y coordinate for the bounding box
            (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)) # ensure bounding box does not exceed image frame

            face = image[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) # extract face ROI, convert from BGR to RGB
            face = cv2.resize(face, (224, 224))         # resize to 224, 224
            face = img_to_array(face)                      # convert resized face to an array
            face = preprocess_input(face)               # preprocess the array
            face = np.expand_dims(face, axis=0)            # expand array to 2D

            (mask, withoutMask) = model.predict(face)[0] # pass the face through the mask model, detect if there is mask or not

            label = "Mask" if mask > withoutMask else "No Mask" # define label
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255) # bbox is Green if 'mask' else Blue

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

            cv2.putText(image, label, (startX, startY - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2) #display label and bbox rectangle in output frame

        return image, label # return image and label

# Helper function for the about page
def about():

    st.write(
            '''
		This project workflow follow 3 phases

		1. Loading the Face Mask Classifier model
		2. Detect Faces in the image
		3. Extract each Face Region of Interest(ROI)
		4. Apply face mask classifier to each face ROI to determine 'mask' or 'No mask'


	''')

# Helper function for the main page

def main():

    st.title("Face Mask Detector App :mask:") # create App title
    st.write("**Using the Python, Tensorflow and OpenCV**") # streamlit function to display text
    


    activities = ['Home', 'About']
    choice = st.sidebar.selectbox("Hey, What do want to do", activities) # streammlit function to display box selector
    # image, label = detect_mask(image=image)

    if choice == "Home": # If user chooses Home page
        st.write("Go to the about Page to learn more about this Project") # display this
        image_file = st.file_uploader("Upload image", type=['jpeg', 'jpg', 'png']) # streamlit function to upload file
        

        if image_file is not None:  #confirm that the image is not a 0 byte file


            st.sidebar.image(image_file, width=240) # then display a sidebar of the uploaded image
            
            if st.button("Process"): # process button
            
                image, label = detect_mask(image_file) # call mask detection model
                st.image(image, width=420) # display the uploaded image
                st.success('### ' +  label) # dispaly label

    elif choice == 'About': # if user chose About page, then open the about page
        about()


if __name__ == "__main__": 
    main()