diff --git a/Detection Models/Face_Mask_Detection/Face_Mask_Detection.ipynb b/Detection Models/Face_Mask_Detection/Face_Mask_Detection.ipynb
index b937ecd44..a236a398c 100644
--- a/Detection Models/Face_Mask_Detection/Face_Mask_Detection.ipynb
+++ b/Detection Models/Face_Mask_Detection/Face_Mask_Detection.ipynb
@@ -1062,12 +1062,23 @@
"id": "dbfeb3a0-d0a7-4446-8ae4-559206d46034",
"metadata": {},
"outputs": [],
+ "source": [
+ "#save the model\n",
+ "model.save('mask_detection_model.keras')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a62eb7fc",
+ "metadata": {},
+ "outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -1081,7 +1092,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/Detection Models/Face_Mask_Detection/mask_detection.keras b/Detection Models/Face_Mask_Detection/mask_detection.keras
new file mode 100644
index 000000000..63dc22c9b
Binary files /dev/null and b/Detection Models/Face_Mask_Detection/mask_detection.keras differ
diff --git a/Detection Models/Face_Mask_Detection/predictive_system_gui.py b/Detection Models/Face_Mask_Detection/predictive_system_gui.py
new file mode 100644
index 000000000..0243d7db2
--- /dev/null
+++ b/Detection Models/Face_Mask_Detection/predictive_system_gui.py
@@ -0,0 +1,83 @@
+import cv2
+import numpy as np
+import keras
+import tk as tk
+from PIL import ImageTk, Image
+import tkinter as tk
+from tkinter import filedialog
+import os
+
+# Load the face mask detector model
+model =keras.models.load_model("mask_detection.keras")
+
+# Initialize the GUI
+root = tk.Tk()
+root.title("Mask Detection")
+root.geometry("500x400")
+
+# Function to classify the image
+def classify_image(image):
+ # Preprocess the image
+ image = image.resize((128, 128))
+ image = np.array(image)
+ image = image / 255.0
+ input_image_reshaped = np.reshape(image, [1,128, 128, 3])
+ # Make predictions
+ predictions = model.predict(input_image_reshaped)
+ input_pred_label = np.argmax(predictions)
+
+ if input_pred_label == 1:
+ result = "With Mask"
+ else:
+ result = "Without Mask"
+
+ # Update the result label text
+ result_label.configure(text="Prediction: " + result)
+
+# Function to handle file upload
+def upload_image():
+ file_path = filedialog.askopenfilename(initialdir=os.getcwd(), title="Select Image File",
+ filetypes=(("JPEG files", "*.jpg"), ("PNG files", "*.png")))
+ if file_path:
+ # Display the uploaded image in the GUI
+ image = Image.open(file_path)
+ image.thumbnail((300, 300))
+ photo = ImageTk.PhotoImage(image)
+ photo_label.configure(image=photo)
+ photo_label.image = photo
+
+ classify_image(image)
+
+# Function to handle capturing photo from webcam
+def capture_photo():
+ video_capture = cv2.VideoCapture(0)
+ _, frame = video_capture.read()
+ video_capture.release()
+ frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+ image = Image.fromarray(frame)
+ classify_image(image)
+
+ # Display the captured photo in the GUI
+ image.thumbnail((300, 300))
+ photo = ImageTk.PhotoImage(image)
+ photo_label.configure(image=photo)
+ photo_label.image = photo
+
+result_label = tk.Label(root, text="", font=("Helvetica", 16))
+result_label.pack(pady=10)
+
+# Create the GUI components
+upload_button = tk.Button(root, text="Upload Image", command=upload_image)
+upload_button.pack(pady=10)
+
+capture_button = tk.Button(root, text="Capture Photo", command=capture_photo)
+capture_button.pack(pady=10)
+
+result_label = tk.Label(root, text="")
+result_label.pack(pady=10)
+
+photo_label = tk.Label(root)
+photo_label.pack()
+
+# Run the GUI main loop
+root.mainloop()
diff --git a/Detection Models/Face_Mask_Detection/streamlit_app.py b/Detection Models/Face_Mask_Detection/streamlit_app.py
new file mode 100644
index 000000000..c30cc9a77
--- /dev/null
+++ b/Detection Models/Face_Mask_Detection/streamlit_app.py
@@ -0,0 +1,72 @@
+import cv2
+import numpy as np
+import keras
+import streamlit as st
+import PIL.Image
+
+model=keras.models.load_model('mask_detection.keras')
+
+
+
+
+
+def main():
+ st.title("Mask Detection App")
+ st.write("Please choose an option")
+
+ option = st.selectbox("Select an option", ("Upload Image", "Capture Photo"))
+
+ if option == "Upload Image":
+ uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+
+ if uploaded_file is not None:
+ # Read and preprocess the image
+ image = PIL.Image.open(uploaded_file)
+ image = image.resize((128,128))
+ image = np.array(image) # Convert to NumPy array
+ image = image / 255.0 # Normalize the image
+ input_image_reshaped = np.reshape(image, [1, 128, 128, 3])
+
+ # Make predictions
+ predictions = model.predict(input_image_reshaped)
+ input_pred_label = np.argmax(predictions)
+
+ # Display the result
+ if input_pred_label == 1:
+ st.markdown("Prediction: With Mask
", unsafe_allow_html=True)
+ else:
+ st.markdown("Prediction: Without Mask
", unsafe_allow_html=True)
+
+ # Display the uploaded image
+ st.write("")
+ st.write("**Uploaded Image**")
+ st.image(image, use_column_width=True)
+
+ elif option == "Capture Photo":
+ video_capture = cv2.VideoCapture(0)
+ _, frame = video_capture.read()
+ video_capture.release()
+
+ # Convert captured frame to RGB
+ frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+ # Resize and preprocess the image
+ image = cv2.resize(frame, (64, 64))
+ image = image / 255.0
+ input_image_reshaped = np.reshape(image, [1, 64, 64, 3])
+
+ # Make predictions
+ predictions = model.predict(input_image_reshaped)
+ input_pred_label = np.argmax(predictions)
+
+ # Display the result
+ if input_pred_label == 1:
+ st.markdown("Prediction: With Mask
", unsafe_allow_html=True)
+ else:
+ st.markdown("Prediction: Without Mask
", unsafe_allow_html=True)
+
+ # Display the captured photo
+ st.image(frame, channels="RGB", use_column_width=True)
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file