<reponame>chinmay-chikhalkar/Face-Mask-Detector
import streamlit as st
from PIL import Image, ImageEnhance
import numpy as np
import cv2
import os
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 detect_mask_image

# Setting custom Page Title and Icon with changed layout and sidebar state
st.set_page_config(page_title='Face Mask Detector', page_icon='😷', layout='centered', initial_sidebar_state='expanded')


def local_css(file_name):
    """ Method for reading styles.css and applying necessary changes to HTML"""
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

def videopreds():
    # define a helper function to detected face and bounding box for each image 
    # in a live video frame
    def detect_and_predict_blood(frame, faceNet, bloodNet):
        
        # grab the dimensions of the frame and then construct a blob
        # from it
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0))
        
        # pass the blob through the network and obtain the face detections
        faceNet.setInput(blob)
        detections = faceNet.forward()
        
        # initialize our list of faces, their corresponding locations,
        # and the list of predictions from our face mask network
        faces = []
        locs = []
        preds = []

        # loop over the detections
        for i in range(0, detections.shape[2]):
            
            # extract the confidence (i.e., probability) associated with
            # the detection
            confidence = detections[0, 0, i, 2]
            
            # filter out weak detections by ensuring the confidence is
            # greater than the minimum confidence
            if confidence > 0.5:
                
                # compute the (x, y)-coordinates of the bounding box for
                # the object
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")
                
                # ensure the bounding boxes fall within the dimensions of
                # the frame
                (startX, startY) = (max(0, startX), max(0, startY))
                (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

                # extract the face ROI, convert it from BGR to RGB channel
                # ordering, resize it to 224x224, and preprocess it
                face = frame[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)
                
                # add the face and bounding boxes to their respective
                # lists
                faces.append(face)
                locs.append((startX, startY, endX, endY))

                # only make a predictions if at least one face was detected
        if len(faces) > 0:
            
            # for faster inference we'll make batch predictions on *all*
            # faces at the same time rather than one-by-one predictions
            # in the above `for` loop
            faces = np.array(faces, dtype="float32")
            preds = bloodNet.predict(faces, batch_size=32)
            
        # return a 2-tuple of the face locations and their corresponding
        # locations
        return (locs, preds)

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

    # load the face mask detector model from disk
    bloodNet = load_model('mask_detector.model')

    # initialize the video stream and allow the camera sensor to warm up
    # vs = VideoStream(src=0).start()
    
    # time.sleep(2.0)
    @st.cache(allow_output_mutation=True)
    def get_cap():
        return cv2.VideoCapture(0)

    cap = get_cap()
   

    frameST = st.empty()
    #param=st.sidebar.slider('chose your value')

    # loop over the frames from the video stream
    while True:
        
        # grab the frame from the threaded video stream and resize it
        # to have a maximum width of 400 pixels
        # frame = vs.read()
        # frame = imutils.resize(frame, width=400)
        ret, frame = cap.read()
        # detect faces in the frame and determine if they are wearing a
        # face mask or not
        (locs, preds) = detect_and_predict_blood(frame, faceNet, bloodNet)

        # loop over the detected face locations and their corresponding
        # locations
        for (box, pred) in zip(locs, preds):
            
            # unpack the bounding box and predictions
            (startX, startY, endX, endY) = box
            (mask, withoutMask) = pred
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            if label == "No Mask":
                cv2.putText(frame, "PLEASE WEAR A MASK!", (5, 70),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)

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

		# display the label and bounding box rectangle on the output
		# frame
            cv2.putText(frame, label, (startX, startY - 10),
			    cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
            cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)


        # show the output frame
        # cv2.imshow("Frame", frame)
        # key = cv2.waitKey(1) & 0xFF
        frameST.image(frame, channels="BGR")
    if st.button('stop the stream',key="stop"):
        cap.release()
        
        
        # if the `q` key was pressed, break from the loop
        # if key == ord("q"):
        #     break
            
    # do a bit of cleanup
    cv2.destroyAllWindows()
    #vs.stop()

def mask_image():
    global RGB_img
    # load our serialized face detector model from disk
    print("[INFO] loading face detector model...")
    prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
    weightsPath = os.path.sep.join(["face_detector",
                                    "res10_300x300_ssd_iter_140000.caffemodel"])
    net = cv2.dnn.readNet(prototxtPath, weightsPath)

    # load the face mask detector model from disk
    print("[INFO] loading face mask detector model...")
    model = load_model("mask_detector.model")

    # load the input image from disk and grab the image spatial
    # dimensions
    image = cv2.imread("./images/out.jpg")
    (h, w) = image.shape[:2]

    # construct a blob from the image
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
                                 (104.0, 177.0, 123.0))

    # pass the blob through the network and obtain the face detections
    print("[INFO] computing face detections...")
    net.setInput(blob)
    detections = net.forward()

    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with
        # the detection
        confidence = detections[0, 0, i, 2]

        # filter out weak detections by ensuring the confidence is
        # greater than the minimum confidence
        if confidence > 0.5:
            # compute the (x, y)-coordinates of the bounding box for
            # the object
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # ensure the bounding boxes fall within the dimensions of
            # the frame
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            # extract the face ROI, convert it from BGR to RGB channel
            # ordering, resize it to 224x224, and preprocess it
            face = image[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)

            # pass the face through the model to determine if the face
            # has a mask or not
            (mask, withoutMask) = model.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

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

            # 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, 2)
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
            RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask_image()

def mask_detection():
    
    local_css("css/styles.css")
    st.markdown('<h1 align="center">😷 Face Mask Detection</h1>', unsafe_allow_html=True)
    activities = ["Image", "Webcam"]
    st.set_option('deprecation.showfileUploaderEncoding', False)
    st.sidebar.markdown("# Mask Detection on?")
    choice = st.sidebar.selectbox("Choose among the given options:", activities)
    
    if choice == 'Image':
        st.markdown('<h2 align="center">Detection on Image</h2>', unsafe_allow_html=True)
        st.markdown("### Upload your image here ⬇")
        image_file = st.file_uploader("", type=['jpg','png','jpeg'])  # upload image
        if image_file is not None:
            our_image = Image.open(image_file)  # making compatible to PIL
            im = our_image.save('./images/out.jpg')
            saved_image = st.image(image_file, caption='', use_column_width=True)
            st.markdown('<h3 align="center">Image uploaded successfully!</h3>', unsafe_allow_html=True)
            if st.button('Process'):
                st.image(RGB_img, use_column_width=True)
  
    elif choice == 'Webcam':
        st.empty()
        st.empty()
        st.empty()
        st.empty()
        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
        videopreds()
                    
        #st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True)
mask_detection()

import streamlit as st
from PIL import Image, ImageEnhance
import numpy as np
import cv2
import os
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 detect_mask_image

# Setting custom Page Title and Icon with changed layout and sidebar state
st.set_page_config(page_title='Face Mask Detector', page_icon='😷', layout='centered', initial_sidebar_state='expanded')


def local_css(file_name):
    """ Method for reading styles.css and applying necessary changes to HTML"""
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

def videopreds():
    # define a helper function to detected face and bounding box for each image 
    # in a live video frame
    def detect_and_predict_blood(frame, faceNet, bloodNet):
        
        # grab the dimensions of the frame and then construct a blob
        # from it
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0))
        
        # pass the blob through the network and obtain the face detections
        faceNet.setInput(blob)
        detections = faceNet.forward()
        
        # initialize our list of faces, their corresponding locations,
        # and the list of predictions from our face mask network
        faces = []
        locs = []
        preds = []

        # loop over the detections
        for i in range(0, detections.shape[2]):
            
            # extract the confidence (i.e., probability) associated with
            # the detection
            confidence = detections[0, 0, i, 2]
            
            # filter out weak detections by ensuring the confidence is
            # greater than the minimum confidence
            if confidence > 0.5:
                
                # compute the (x, y)-coordinates of the bounding box for
                # the object
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")
                
                # ensure the bounding boxes fall within the dimensions of
                # the frame
                (startX, startY) = (max(0, startX), max(0, startY))
                (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

                # extract the face ROI, convert it from BGR to RGB channel
                # ordering, resize it to 224x224, and preprocess it
                face = frame[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)
                
                # add the face and bounding boxes to their respective
                # lists
                faces.append(face)
                locs.append((startX, startY, endX, endY))

                # only make a predictions if at least one face was detected
        if len(faces) > 0:
            
            # for faster inference we'll make batch predictions on *all*
            # faces at the same time rather than one-by-one predictions
            # in the above `for` loop
            faces = np.array(faces, dtype="float32")
            preds = bloodNet.predict(faces, batch_size=32)
            
        # return a 2-tuple of the face locations and their corresponding
        # locations
        return (locs, preds)

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

    # load the face mask detector model from disk
    bloodNet = load_model('mask_detector.model')

    # initialize the video stream and allow the camera sensor to warm up
    # vs = VideoStream(src=0).start()
    
    # time.sleep(2.0)
    @st.cache(allow_output_mutation=True)
    def get_cap():
        return cv2.VideoCapture(0)

    cap = get_cap()
   

    frameST = st.empty()
    #param=st.sidebar.slider('chose your value')

    # loop over the frames from the video stream
    while True:
        
        # grab the frame from the threaded video stream and resize it
        # to have a maximum width of 400 pixels
        # frame = vs.read()
        # frame = imutils.resize(frame, width=400)
        ret, frame = cap.read()
        # detect faces in the frame and determine if they are wearing a
        # face mask or not
        (locs, preds) = detect_and_predict_blood(frame, faceNet, bloodNet)

        # loop over the detected face locations and their corresponding
        # locations
        for (box, pred) in zip(locs, preds):
            
            # unpack the bounding box and predictions
            (startX, startY, endX, endY) = box
            (mask, withoutMask) = pred
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            if label == "No Mask":
                cv2.putText(frame, "PLEASE WEAR A MASK!", (5, 70),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)

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

		# display the label and bounding box rectangle on the output
		# frame
            cv2.putText(frame, label, (startX, startY - 10),
			    cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
            cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)


        # show the output frame
        # cv2.imshow("Frame", frame)
        # key = cv2.waitKey(1) & 0xFF
        frameST.image(frame, channels="BGR")
    if st.button('stop the stream',key="stop"):
        cap.release()
        
        
        # if the `q` key was pressed, break from the loop
        # if key == ord("q"):
        #     break
            
    # do a bit of cleanup
    cv2.destroyAllWindows()
    #vs.stop()

def mask_image():
    global RGB_img
    # load our serialized face detector model from disk
    print("[INFO] loading face detector model...")
    prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
    weightsPath = os.path.sep.join(["face_detector",
                                    "res10_300x300_ssd_iter_140000.caffemodel"])
    net = cv2.dnn.readNet(prototxtPath, weightsPath)

    # load the face mask detector model from disk
    print("[INFO] loading face mask detector model...")
    model = load_model("mask_detector.model")

    # load the input image from disk and grab the image spatial
    # dimensions
    image = cv2.imread("./images/out.jpg")
    (h, w) = image.shape[:2]

    # construct a blob from the image
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
                                 (104.0, 177.0, 123.0))

    # pass the blob through the network and obtain the face detections
    print("[INFO] computing face detections...")
    net.setInput(blob)
    detections = net.forward()

    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with
        # the detection
        confidence = detections[0, 0, i, 2]

        # filter out weak detections by ensuring the confidence is
        # greater than the minimum confidence
        if confidence > 0.5:
            # compute the (x, y)-coordinates of the bounding box for
            # the object
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # ensure the bounding boxes fall within the dimensions of
            # the frame
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            # extract the face ROI, convert it from BGR to RGB channel
            # ordering, resize it to 224x224, and preprocess it
            face = image[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)

            # pass the face through the model to determine if the face
            # has a mask or not
            (mask, withoutMask) = model.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

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

            # 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, 2)
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
            RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask_image()

def mask_detection():
    
    local_css("css/styles.css")
    st.markdown('<h1 align="center">😷 Face Mask Detection</h1>', unsafe_allow_html=True)
    activities = ["Image", "Webcam"]
    st.set_option('deprecation.showfileUploaderEncoding', False)
    st.sidebar.markdown("# Mask Detection on?")
    choice = st.sidebar.selectbox("Choose among the given options:", activities)
    
    if choice == 'Image':
        st.markdown('<h2 align="center">Detection on Image</h2>', unsafe_allow_html=True)
        st.markdown("### Upload your image here ⬇")
        image_file = st.file_uploader("", type=['jpg','png','jpeg'])  # upload image
        if image_file is not None:
            our_image = Image.open(image_file)  # making compatible to PIL
            im = our_image.save('./images/out.jpg')
            saved_image = st.image(image_file, caption='', use_column_width=True)
            st.markdown('<h3 align="center">Image uploaded successfully!</h3>', unsafe_allow_html=True)
            if st.button('Process'):
                st.image(RGB_img, use_column_width=True)
  
    elif choice == 'Webcam':
        st.empty()
        st.empty()
        st.empty()
        st.empty()
        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
        videopreds()
                    
        #st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True)
mask_detection()
# Setting custom Page Title and Icon with changed layout and sidebar state Method for reading styles.css and applying necessary changes to HTML # define a helper function to detected face and bounding box for each image # in a live video frame # grab the dimensions of the frame and then construct a blob # from it # pass the blob through the network and obtain the face detections # initialize our list of faces, their corresponding locations, # and the list of predictions from our face mask network # loop over the detections # extract the confidence (i.e., probability) associated with # the detection # filter out weak detections by ensuring the confidence is # greater than the minimum confidence # compute the (x, y)-coordinates of the bounding box for # the object # ensure the bounding boxes fall within the dimensions of # the frame # extract the face ROI, convert it from BGR to RGB channel # ordering, resize it to 224x224, and preprocess it # add the face and bounding boxes to their respective # lists # only make a predictions if at least one face was detected # for faster inference we'll make batch predictions on *all* # faces at the same time rather than one-by-one predictions # in the above `for` loop # return a 2-tuple of the face locations and their corresponding # locations # load our serialized face detector model from disk # load the face mask detector model from disk # initialize the video stream and allow the camera sensor to warm up # vs = VideoStream(src=0).start() # time.sleep(2.0) #param=st.sidebar.slider('chose your value') # loop over the frames from the video stream # grab the frame from the threaded video stream and resize it # to have a maximum width of 400 pixels # frame = vs.read() # frame = imutils.resize(frame, width=400) # detect faces in the frame and determine if they are wearing a # face mask or not # loop over the detected face locations and their corresponding # locations # unpack the bounding box and predictions # the bounding box and text # include the probability in the label # display the label and bounding box rectangle on the output # frame # show the output frame # cv2.imshow("Frame", frame) # key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop # if key == ord("q"): #     break # do a bit of cleanup #vs.stop() # load our serialized face detector model from disk # load the face mask detector model from disk # load the input image from disk and grab the image spatial # dimensions # construct a blob from the image # pass the blob through the network and obtain the face detections # loop over the detections # extract the confidence (i.e., probability) associated with # the detection # filter out weak detections by ensuring the confidence is # greater than the minimum confidence # compute the (x, y)-coordinates of the bounding box for # the object # ensure the bounding boxes fall within the dimensions of # the frame # extract the face ROI, convert it from BGR to RGB channel # ordering, resize it to 224x224, and preprocess it # pass the face through the model to determine if the face # has a mask or not # determine the class label and color we'll use to draw # the bounding box and text # include the probability in the label # display the label and bounding box rectangle on the output # frame ## Upload your image here ⬇") # upload image # making compatible to PIL #st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True)