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


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():
    st.title("Face mask detection")
    activities = ["Image", "Webcam"]
    st.set_option('deprecation.showfileUploaderEncoding', False)
    choice = st.sidebar.selectbox("Mask Detection on?", activities)

    if choice == 'Image':
        st.subheader("Detection on image")
        image_file = st.file_uploader("Upload Image", 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='image uploaded successfully', use_column_width=True)
            if st.button('Process'):
                st.image(RGB_img)

    if choice == 'Webcam':
        st.subheader("Detection on webcam")
        st.text("This feature will be avilable soon")
mask_detection()

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