Update main.py

main.py

@@ -10,53 +10,57 @@ import tempfile
 # Load the Brain Tumor CNN Model
 braintumor_model = load_model('models/braintumor.h5')
 
-# Configuring Flask
-UPLOAD_FOLDER = 'static/uploads'
-ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
-
+# Configuring Flask application
 app = Flask(__name__)
-app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-app.secret_key = "nielitchandigarhpunjabpolice"
+app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0  # Disable caching for images
+app.secret_key = "nielitchandigarhpunjabpolice"  # Secret key for session management
+
+# Allowed image file extensions
+ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
 
 def allowed_file(filename):
-    """Check if the file is a valid image format"""
+    """Check if the file is a valid image format (png, jpg, jpeg)."""
     return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
 
 def preprocess_imgs(set_name, img_size):
     """
-    Preprocess the image by resizing and applying VGG16 preprocessing
+    Preprocess images by resizing them to the target size (224x224 for VGG16) 
+    and applying appropriate resizing techniques.
     """
     set_new = []
     for img in set_name:
-        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
+        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)  # Resize image
         set_new.append(img)
     return np.array(set_new)
 
 def crop_imgs(set_name, add_pixels_value=0):
     """
-    Crop the region of interest (ROI) in the image (brain tumor detection)
+    Crop the region of interest (ROI) in the image for brain tumor detection.
+    This method finds contours in the image, then crops the detected tumor region.
     """
     set_new = []
     for img in set_name:
+        # Convert to grayscale and apply Gaussian blur
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
         gray = cv2.GaussianBlur(gray, (5, 5), 0)
 
-        # Threshold the image and find contours to crop the image
+        # Threshold the image and find contours
         thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
         thresh = cv2.erode(thresh, None, iterations=2)
         thresh = cv2.dilate(thresh, None, iterations=2)
 
+        # Find the largest contour and crop the image around it
         cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
         cnts = imutils.grab_contours(cnts)
         c = max(cnts, key=cv2.contourArea)
 
-        # Find the extreme points and crop the image
+        # Get the extreme points (left, right, top, bottom) of the contour for cropping
         extLeft = tuple(c[c[:, :, 0].argmin()][0])
         extRight = tuple(c[c[:, :, 0].argmax()][0])
         extTop = tuple(c[c[:, :, 1].argmin()][0])
         extBot = tuple(c[c[:, :, 1].argmax()][0])
 
+        # Add some extra pixels around the contour to ensure the full tumor is captured
         ADD_PIXELS = add_pixels_value
         new_img = img[extTop[1]-ADD_PIXELS:extBot[1]+ADD_PIXELS,
                       extLeft[0]-ADD_PIXELS:extRight[0]+ADD_PIXELS].copy()
@@ -66,45 +70,56 @@ def crop_imgs(set_name, add_pixels_value=0):
 
 @app.route('/')
 def brain_tumor():
+    """
+    The main page that renders the HTML form for the user to upload an image
+    and input their details.
+    """
     return render_template('braintumor.html')
 
 @app.route('/resultbt', methods=['POST'])
 def resultbt():
+    """
+    The result page that processes the uploaded image, makes predictions using 
+    the trained brain tumor detection model, and displays the result.
+    """
     if request.method == 'POST':
-        # Get user input from the form
+        # Get user input from the form (name, email, phone, etc.)
         firstname = request.form['firstname']
         lastname = request.form['lastname']
         email = request.form['email']
         phone = request.form['phone']
         gender = request.form['gender']
         age = request.form['age']
-        file = request.files['file']
+        file = request.files['file']  # The uploaded image file
 
         if file and allowed_file(file.filename):
-            # Use tempfile to create a temporary file path
+            # Use tempfile to create a temporary file path for the uploaded image
             temp_file = tempfile.NamedTemporaryFile(delete=False)
-            filename = secure_filename(file.filename)
+            filename = secure_filename(file.filename)  # Secure the filename
             file.save(temp_file.name)  # Save file to the temp location
 
             flash('Image successfully uploaded and displayed below')
 
-            # Read and process the image
+            # Read the image, crop the region of interest (tumor), and preprocess
             img = cv2.imread(temp_file.name)
-            img = crop_imgs([img])
-            img = img.reshape(img.shape[1:])
-            img = preprocess_imgs([img], (224, 224))
+            img = crop_imgs([img])  # Crop the tumor area from the image
+            img = img.reshape(img.shape[1:])  # Reshape for the model input
+            img = preprocess_imgs([img], (224, 224))  # Resize image to 224x224
 
-            # Model prediction
+            # Model prediction: 0 - no tumor, 1 - tumor detected
             pred = braintumor_model.predict(img)
             if pred < 0.5:
-                pred = 0
+                pred = 0  # No tumor detected
             else:
-                pred = 1
+                pred = 1  # Tumor detected
 
+            # Return the result to the user, including input data and prediction
             return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=pred, gender=gender)
         else:
+            # If the file is not valid, show an error message
             flash('Allowed image types are - png, jpg, jpeg')
             return redirect(request.url)
 
 if __name__ == '__main__':
+    # Run the app in debug mode for development purposes
     app.run(debug=True)