Face Mask Detection MobileNetV2

+# Python virtual environments
+venv

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+
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+MIT License
+
+Copyright (c) 2020 Chandrika Deb
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

+<h1 align="center">Face Mask Detection</h1>
+
+<div align= "center">
+  <h4>Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams.</h4>
+</div>
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+![Python](https://img.shields.io/badge/python-v3.6+-blue.svg)
+[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/chandrikadeb7/Face-Mask-Detection/issues)
+[![Forks](https://img.shields.io/github/forks/chandrikadeb7/Face-Mask-Detection.svg?logo=github)](https://github.com/chandrikadeb7/Face-Mask-Detection/network/members)
+[![Stargazers](https://img.shields.io/github/stars/chandrikadeb7/Face-Mask-Detection.svg?logo=github)](https://github.com/chandrikadeb7/Face-Mask-Detection/stargazers)
+[![Issues](https://img.shields.io/github/issues/chandrikadeb7/Face-Mask-Detection.svg?logo=github)](https://github.com/chandrikadeb7/Face-Mask-Detection/issues)
+[![MIT License](https://img.shields.io/github/license/chandrikadeb7/Face-Mask-Detection.svg?style=flat-square)](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/LICENSE)
+[![LinkedIn](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://www.linkedin.com/in/chandrika-deb/)
+
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+![Live Demo](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/Readme_images/Demo.gif)
+
+
+
+## :innocent: Motivation
+In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. Also, the absence of large datasets of __‘with_mask’__ images has made this task more cumbersome and challenging. 
+
+ 
+## :hourglass: Project Demo
+:movie_camera: [YouTube Demo Link](https://www.youtube.com/watch?v=AAkNyZlUae0)
+
+:computer: [Dev Link](https://dev.to/chandrikadeb7/face-mask-detection-my-major-project-3fj3)
+
+[![Already deployed version](https://raw.githubusercontent.com/vasantvohra/TrashNet/master/hr.svg)](https://face-mask--detection-app.herokuapp.com/)
+
+
+
+<p align="center"><img src="https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/Readme_images/Screen%20Shot%202020-05-14%20at%208.49.06%20PM.png" width="700" height="400"></p>
+
+
+## :warning: TechStack/framework used
+
+- [OpenCV](https://opencv.org/)
+- [Caffe-based face detector](https://caffe.berkeleyvision.org/)
+- [Keras](https://keras.io/)
+- [TensorFlow](https://www.tensorflow.org/)
+- [MobileNetV2](https://arxiv.org/abs/1801.04381)
+
+## :star: Features
+Our face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the MobileNetV2 architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).
+
+This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.
+
+## :file_folder: Dataset
+The dataset used can be downloaded here - [Click to Download](https://drive.google.com/drive/folders/1XDte2DL2Mf_hw4NsmGst7QtYoU7sMBVG?usp=sharing)
+
+This dataset consists of __3835 images__ belonging to two classes:
+*	__with_mask: 1916 images__
+*	__without_mask: 1919 images__
+
+The images used were real images of faces wearing masks. The images were collected from the following sources:
+
+* __Bing Search API__ ([See Python script](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/search.py))
+* __Kaggle datasets__ 
+* __RMFD dataset__ ([See here](https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset))
+
+## :key: Prerequisites
+
+All the dependencies and required libraries are included in the file <code>requirements.txt</code> [See here](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/requirements.txt)
+
+## 🚀&nbsp; Installation
+1. Clone the repo
+```
+$ git clone https://github.com/chandrikadeb7/Face-Mask-Detection.git
+```
+
+2. Change your directory to the cloned repo and create a Python virtual environment named 'test'
+```
+$ mkvirtualenv test
+```
+
+3. Now, run the following command in your Terminal/Command Prompt to install the libraries required
+```
+$ pip3 install -r requirements.txt
+```
+
+## :bulb: Working
+
+1. Open terminal. Go into the cloned project directory and type the following command:
+```
+$ python3 train_mask_detector.py --dataset dataset
+```
+
+2. To detect face masks in an image type the following command: 
+```
+$ python3 detect_mask_image.py --image images/pic1.jpeg
+```
+
+3. To detect face masks in real-time video streams type the following command:
+```
+$ python3 detect_mask_video.py 
+```
+## :key: Results
+
+#### Our model gave 93% accuracy for Face Mask Detection after training via <code>tensorflow-gpu==2.0.0</code>
+
+![](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/Readme_images/Screenshot%202020-06-01%20at%209.48.27%20PM.png)
+
+#### We got the following accuracy/loss training curve plot
+![](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/plot.png)
+
+## Streamlit app
+
+Face Mask Detector webapp using Tensorflow & Streamlit
+
+command
+```
+$ streamlit run app.py 
+```
+## Images
+
+<p align="center">
+  <img src="Readme_images/1.PNG">
+</p>
+<p align="center">Upload Images</p>
+
+<p align="center">
+  <img src="Readme_images/2.PNG">
+</p>
+<p align="center">Results</p>
+
+## :clap: And it's done!
+Feel free to mail me for any doubts/query 
+:email: chandrikadeb7@gmail.com
+
+## :handshake: Contribution
+Feel free to **file a new issue** with a respective title and description on the the [Face-Mask-Detection](https://github.com/chandrikadeb7/Face-Mask-Detection/issues) repository. If you already found a solution to your problem, **I would love to review your pull request**! 
+
+## :heart: Owner
+Made with :heart:&nbsp;  by [Chandrika Deb](https://github.com/chandrikadeb7)
+
+## :+1: Credits
+* [https://www.pyimagesearch.com/](https://www.pyimagesearch.com/)
+* [https://www.tensorflow.org/tutorials/images/transfer_learning](https://www.tensorflow.org/tutorials/images/transfer_learning)
+
+## :eyes: License
+MIT © [Chandrika Deb](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/LICENSE)

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