Merge branch 'master' of https://github.com/chandrikadeb7/Face-Mask-D…

…etection into master

.github/pull_request_template.md

@@ -0,0 +1,41 @@
+#### Issue Number
+ISSUE #
+<!-- Please Mention the issue number as  ISSUE #(Issue Number)
+Example:
+ISSUE #5
+-->
+
+#### Describe the changes you've made
+<!--
+A clear and concise description of what you have done to successfully close your assigned issue. Any new files? or anything you feel to let us know!
+-->
+
+#### Describe if there is any unusual behaviour of your code(Write `NA` if there isn't)
+<!--
+A clear and concise description of it.
+-->
+
+#### Additional context (OPTIONAL)
+<!--
+Add any other context or screenshots about the feature request here.
+-->
+
+#### Test plan (OPTIONAL)
+<!--
+A good test plan should give instructions that someone else can easily follow.
+How someone can test your code?
+-->
+
+#### Checklist
+<!--
+Example how to mark a checkbox :-
+- [x] My code follows the code style of this project.
+-->
+- [ ] My code follows the code style of this project.
+- [ ] I have performed a self-review of my own code.
+- [ ] My change requires a change to the documentation.
+- [ ] I have updated the documentation accordingly.
+- [ ] I have commented my code, particularly in hard-to-understand areas.
+- [ ] My changes generate no new warnings.
+- [ ] I have added tests that prove my fix is effective or that my feature works.
+- [ ] The title of my pull request is a short description of the requested changes.

ResNet50_v2/Readme.md

@@ -0,0 +1,34 @@
+# Mask Detection using ResNet50 v2 
+
+
+## 🚀  Steps to run the ResNet50 v2
+
+- To clone the Repository: <br>
+```
+git clone https://github.com/chandrikadeb7/Face-Mask-Detection.git
+```
+- Open terminal. Go into the cloned project directory and type the following command to train the model and create ResNet50_mask_detector.model file <br>
+```
+python3 ResNet50_v2/mask_with_resnet.py --dataset dataset --model "ResNet50_v2/ResNet50_mask_detector.model"
+```
+
+- To detect face masks in an image type the following command: <br>
+```
+python3 detect_mask_image.py --image images/pic1.jpeg --model ResNet50_v2/ResNet50_mask_detector.model
+```
+
+
+## :key:&nbsp; Result 
+
+![alt text](Readme_images/Matrix.png)
+
+### Training Loss and Accuracy 
+![alt text](Readme_images/Graph.png)
+
+## :star:&nbsp; Output
+![alt text](Readme_images/output.png)
+
+
+## :star:&nbsp; Output Video
+![alt text](Readme_images/Output.gif)
+

ResNet50_v2/mask_with_resnet.py

@@ -0,0 +1,128 @@
+# -*- coding: utf-8 -*-
+
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.utils import to_categorical
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow_hub as hub
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.preprocessing.image import load_img
+from tensorflow.keras import layers
+from tensorflow.keras.layers import Input
+import argparse
+import os
+from tensorflow.keras.applications import ResNet50V2
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-d", "--dataset", required=True,
+	help="path to input dataset")
+ap.add_argument("-p", "--plot", type=str, default="plot.png",
+	help="path to output loss/accuracy plot")
+ap.add_argument("-m", "--model", type=str,
+	default="mask_detector.model",
+	help="path to output face mask detector model")
+args = vars(ap.parse_args())
+
+print("[INFO] loading images...")
+imagePaths = list(paths.list_images(args["dataset"]))
+data = []
+labels = []
+
+IMG_SIZE = 224
+CHANNELS = 3
+N_LABELS=2
+
+
+# loop over the image paths
+for imagePath in imagePaths:
+	# extract the class label from the filename
+  label = imagePath.split(os.path.sep)[-2]
+	# load the input image (224x224) and preprocess it
+  image = load_img(imagePath, target_size=(IMG_SIZE, IMG_SIZE))
+  image = img_to_array(image)
+  image = image/255
+#image = preprocess_input(image)
+
+	# update the data and labels lists, respectively
+  data.append(image)
+  labels.append(label)
+
+data = np.array(data, dtype="float32")
+labels = np.array(labels)
+
+lb = LabelBinarizer()
+labels = lb.fit_transform(labels)
+labels = to_categorical(labels)
+
+(trainX, testX, trainY, testY) = train_test_split(data, labels,	test_size=0.20, stratify=labels, random_state=42)
+
+aug = ImageDataGenerator(
+	rotation_range=20,
+	zoom_range=0.15,
+	width_shift_range=0.2,
+	height_shift_range=0.2,
+	shear_range=0.15,
+	horizontal_flip=True,
+	fill_mode="nearest")
+
+
+feature_extractor_layer = ResNet50V2(weights="imagenet", include_top=False,
+	input_tensor=Input(shape=(IMG_SIZE,IMG_SIZE,CHANNELS)))
+
+feature_extractor_layer.trainable = False
+
+model = tf.keras.Sequential([
+    feature_extractor_layer,
+    layers.Flatten(name="flatten"),
+    layers.Dense(1024, activation='relu', name='hidden_layer'),
+    layers.Dropout(0.5),
+    layers.Dense(N_LABELS, activation='sigmoid', name='output')
+])
+
+model.summary()
+
+LR = 1e-5 # Keep it small when transfer learning
+EPOCHS = 20
+BS = 256
+
+model.compile(
+  optimizer=tf.keras.optimizers.Adam(learning_rate=LR),
+  loss="binary_crossentropy",
+  metrics=["accuracy"])
+
+import time
+start = time.time()
+history = model.fit(aug.flow(trainX, trainY, batch_size=BS),
+	steps_per_epoch=len(trainX) // BS,
+	validation_data=(testX, testY),
+	epochs=EPOCHS)
+print('\nTraining took {}'.format((time.time()-start)))
+
+print("[INFO] evaluating network...")
+predIdxs = model.predict(testX, batch_size=BS)
+
+predIdxs = np.argmax(predIdxs, axis=1)
+
+print(classification_report(testY.argmax(axis=1), predIdxs,target_names=lb.classes_))
+
+print("[INFO] saving mask detector model...")
+model.save(args["model"], save_format="h5")
+
+N = EPOCHS
+plt.style.use("ggplot")
+plt.figure()
+plt.plot(np.arange(0, N), history.history["loss"], label="train_loss")
+plt.plot(np.arange(0, N), history.history["val_loss"], label="val_loss")
+plt.plot(np.arange(0, N), history.history["accuracy"], label="accuracy")
+plt.plot(np.arange(0, N), history.history["val_accuracy"], label="val_accuracy")
+plt.title("Training Loss and Accuracy")
+plt.xlabel("Epoch #")
+plt.ylabel("Loss/Accuracy")
+plt.legend(loc="lower left")
+plt.savefig(args["plot"])

app.py

@@ -8,6 +8,15 @@
 from tensorflow.keras.models import load_model
 import detect_mask_image
 
+# Setting custom Page Title and Icon with changed layout and sidebar state
+st.beta_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 mask_image():
     global RGB_img
@@ -85,22 +94,26 @@ def mask_image():
 mask_image()
 
 def mask_detection():
-    st.title("Face 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)
-    choice = st.sidebar.selectbox("Mask Detection on?", activities)
+    st.sidebar.markdown("# Mask Detection on?")
+    choice = st.sidebar.selectbox("Choose among the given options:", activities)
 
     if choice == 'Image':
-        st.subheader("Detection on image")
-        image_file = st.file_uploader("Upload Image", type=['jpg'])  # upload 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='image uploaded successfully', use_column_width=True)
+            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)
 
     if choice == 'Webcam':
-        st.subheader("Detection on webcam")
-        st.text("This feature will be avilable soon")
+        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
+        st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True)
 mask_detection()

css/styles.css

@@ -0,0 +1,16 @@
+/* Changing body color to dark grey and text color to gainsboro(version of white) */
+body {
+    color: gainsboro;
+    background-color: #333;
+}
+
+/* Changing sideline color to light gray and text color to black */
+.sidebar .sidebar-content {
+    background-color: #c1c1c1;
+    background-image: none;
+    color: black;
+}
+
+/* Removing Main Menubar and Footer */
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}

detect_mask_video.py

@@ -6,6 +6,7 @@
 from tensorflow.keras.preprocessing.image import img_to_array
 from tensorflow.keras.models import load_model
 from imutils.video import VideoStream
+from playsound import playsound
 import numpy as np
 import argparse
 import imutils
@@ -143,6 +144,11 @@ def detect_and_predict_mask(frame, faceNet, maskNet):
 			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
 		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
 
+		# Alarm when "No Mask" detected
+		if mask < withoutMask:
+			path = os.path.abspath("Alarm.wav")
+			playsound(path)
+
 	# show the output frame
 	cv2.imshow("Frame", frame)
 	key = cv2.waitKey(1) & 0xFF

requirements.txt

@@ -9,3 +9,5 @@ scipy==1.4.1
 scikit-learn==0.23.1
 pillow==7.2.0
 streamlit==0.65.2
+playsound
+pyobjc