
import time
import streamlit as st
from PIL import Image, ImageEnhance
import numpy as np
from imutils.video import VideoStream
import cv2
import imutils
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)

#global model import
# 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"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

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


def mask_image():
    #global RGB_img

    # 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...")
    faceNet.setInput(blob)
    detections = faceNet.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) = maskNet.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "with_Mask" if mask > withoutMask else "without_Mask"
            color = (0, 255, 0) if label == "with_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)
            outImg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    return outImg
#mask_image()

def detect_and_predict_mask(frame, faceNet, maskNet):
	# 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]
			if face.any():
				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 = maskNet.predict(faces, batch_size=32)

	# return a 2-tuple of the face locations and their corresponding
	# locations
	return (locs, preds)


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'])  # 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.markdown(outImg, unsafe_allow_html=True)
                st.image(mask_image(), use_column_width=True)
                #st.markdown(mask_image(), unsafe_allow_html=True)

    if choice == 'Webcam':
        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
        st.markdown('<h3 align="center">Starting video stream...</h3>', unsafe_allow_html=True)
        FRAME_WINDOW = st.image([])
        vs = VideoStream(src=0).start()
        time.sleep(2.0)

        while True:
            frame = vs.read()
            frame = imutils.resize(frame, width=600)
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            
            # detect faces in the frame and determine if they are wearing a
	        # face mask or not
            (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
            
            # 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

                # determine the class label and color we'll use to draw
                # the bounding box and text
                label = "with Mask" if mask > withoutMask else "without Mask"
                color = (0, 255, 0) if label == "with Mask" else (255, 0, 0)
                    
                # 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)
                FRAME_WINDOW.image(frame)       
        st.write('Stopped')
mask_detection()


import time
import streamlit as st
from PIL import Image, ImageEnhance
import numpy as np
from imutils.video import VideoStream
import cv2
import imutils
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)

#global model import
# 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"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

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


def mask_image():
    #global RGB_img

    # 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...")
    faceNet.setInput(blob)
    detections = faceNet.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) = maskNet.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "with_Mask" if mask > withoutMask else "without_Mask"
            color = (0, 255, 0) if label == "with_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)
            outImg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    return outImg
#mask_image()

def detect_and_predict_mask(frame, faceNet, maskNet):
	# 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]
			if face.any():
				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 = maskNet.predict(faces, batch_size=32)

	# return a 2-tuple of the face locations and their corresponding
	# locations
	return (locs, preds)


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'])  # 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.markdown(outImg, unsafe_allow_html=True)
                st.image(mask_image(), use_column_width=True)
                #st.markdown(mask_image(), unsafe_allow_html=True)

    if choice == 'Webcam':
        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
        st.markdown('<h3 align="center">Starting video stream...</h3>', unsafe_allow_html=True)
        FRAME_WINDOW = st.image([])
        vs = VideoStream(src=0).start()
        time.sleep(2.0)

        while True:
            frame = vs.read()
            frame = imutils.resize(frame, width=600)
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            
            # detect faces in the frame and determine if they are wearing a
	        # face mask or not
            (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
            
            # 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

                # determine the class label and color we'll use to draw
                # the bounding box and text
                label = "with Mask" if mask > withoutMask else "without Mask"
                color = (0, 255, 0) if label == "with Mask" else (255, 0, 0)
                    
                # 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)
                FRAME_WINDOW.image(frame)       
        st.write('Stopped')
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 #global model import # load our serialized face detector model from disk # load the face mask detector model from disk #global RGB_img # 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 #mask_image() # 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 ## Upload your image here ⬇") # upload image # making compatible to PIL #st.markdown(outImg, unsafe_allow_html=True) #st.markdown(mask_image(), unsafe_allow_html=True) # 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 # 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