Added Self-Trained Classifications

README.md

@@ -108,6 +108,30 @@ The main script will display a list of available classifiers. Enter the number c
 
 To stop the script, press `Q`, `ESC`, or otherwise close the window.
 
+Alternatively, you can also run the individual scripts in each subdirectory below to run the classifiers directly.
+
+---
+
+## Flask Web App
+
+If you would like to use the interactive website version of this app, you can run the Flask web app. The web app allows you to use the classifiers through a web interface. You can choose a classifier and the app will run the selected classifier.
+
+To run the Flask web app, use the following command:
+
+```bash
+python app.py
+```
+
+The web app will start running on `http://127.0.0.1:5000/`. Open this URL in your web browser to access the web app. You can then choose a classifier from the list of available classifiers and run it.
+
+Here is what it looks like:
+
+<p align="center">
+  <img src="flask-web-app.png" alt="Flask Web App" width="100%">
+</p>
+
+Note that the app has also been Deployed to Heroku [here](https://ai-multipurpose-classifier-b1655f2a20d4.herokuapp.com/). However, due to changes in Heroku's free tier regarding available Dynos (and I'm a broke college student), the app may not work as expected. If you encounter any issues, please run the app locally using the instructions above.
+
 ---
 
 ## Vehicle Classification

Vehicle-Classification/vehicle_detection.py

@@ -60,12 +60,12 @@ def process_input(source):
 
     if source == 'webcam':
         cap = cv2.VideoCapture(0)
-        while True:
+        while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
                 break
             process_frame(frame, model, classes, output_layers, colors)
-            if cv2.waitKey(1) == 27:
+            if cv2.waitKey(1) == 27 or cv2.getWindowProperty("Video", cv2.WND_PROP_VISIBLE) < 1:
                 break
         cap.release()
     elif source.endswith(('.png', '.jpg', '.jpeg')):
@@ -77,12 +77,12 @@ def process_input(source):
         cv2.waitKey(0)
     else:
         cap = cv2.VideoCapture(source)
-        while True:
+        while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
                 break
             process_frame(frame, model, classes, output_layers, colors)
-            if cv2.waitKey(1) == 27:
+            if cv2.waitKey(1) == 27 or cv2.getWindowProperty("Video", cv2.WND_PROP_VISIBLE) < 1:
                 break
         cap.release()