Finish coding

CV-App/algorithms/invis_cloak.py

@@ -19,6 +19,9 @@ class InvisCloak (Algorithm):
         # Includes noice reduction and histogram spread
         self.middle_value_picture = None
 
+        # Buffer for background image
+        self.background = None
+
         # Clean up results folder
         folder_path = os.path.join("results")
 
@@ -78,6 +81,7 @@ class InvisCloak (Algorithm):
         elif event == cv2.EVENT_MBUTTONUP:
             # Save current image as background
             cv2.imwrite(f"results/background.png", self.picture_buffer[self.n - 1])
+            self.background = cv2.imread("results/background.png")
 
 
     def _plotNoise(self, img, name:str):
@@ -172,29 +176,23 @@ class InvisCloak (Algorithm):
             Hier steht Ihr Code zu Aufgabe 2.3.1 (StatischesSchwellwertverfahren)
             - Binärmaske erstellen
         """
-        # 0 = blue, 1 = green, 2 = red
-        selected_color_channel = 2
+        # Convert BGR -> HSV
+        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
 
-        # Color threshold values for color deletion
-        lower_bound, upper_bound = 100, 255
+        # Area 1: H = 0-10 (strong Rot)
+        lower_red1 = np.array([0, 100, 50])
+        upper_red1 = np.array([0, 255, 255])
 
-        # Creating binary mask
-        binary_mask = (lower_bound < img[:, :, selected_color_channel]) * (img[:, :, selected_color_channel] < upper_bound)
+        # Area 2: H = 169-179 (red-violet)
+        lower_red2 = np.array([171, 100, 50])
+        upper_red2 = np.array([179, 255, 255])
 
-        # Store binary mask to results folder
-        if save_binary_mask:
-            cv2.imwrite(f"results/{datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}_binary_mask.png",
-                        binary_mask * 256)
-
-        try:
-            # Get background image
-            background = cv2.imread("results/background.png")
-
-            # Apply mask to image
-            img[binary_mask] = background[binary_mask]
-        except:
-            print("No background image")
+        # Create binary mask for both red areas
+        mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
+        mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
 
+        # Combine both masks
+        mask = cv2.bitwise_or(mask1, mask2)
 
         """
             Hier steht Ihr Code zu Aufgabe 2.3.2 (Binärmaske)
@@ -202,12 +200,42 @@ class InvisCloak (Algorithm):
             - Wahl größte zusammenhängende Region
         """
 
+        # Optimizing mask with opening and closing
+        kernel = np.ones((5, 5), np.uint8)
+
+        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+
+        # Select biggest connected area
+        cnts, _ = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        if cnts:
+            c = max(cnts, key=cv2.contourArea)
+            new_mask = np.zeros_like(mask)
+            cv2.drawContours(new_mask, [c], -1, color = 255, thickness = -1)
+            mask = new_mask
+        else:
+            mask = np.zeros_like(mask)
 
         """
-            Hier steht Ihr Code zu Aufgabe 2.3.1 (Bildmodifizerung)
+            Hier steht Ihr Code zu Aufgabe 2.3.3 (Bildmodifizerung)
             - Hintergrund mit Mausklick definieren
             - Ersetzen des Hintergrundes
         """
 
+        # Return image if no background is set
+        if self.background is None:
+            return img
 
-        return img
+        # 3-channel mask for binary operations
+        mask_3ch = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
+
+        # Background are: Get from save background image
+        background_part = cv2.bitwise_and(self.background, mask_3ch)
+
+        # Foreground area: Extract from current image
+        foreground_part = cv2.bitwise_and(img, cv2.bitwise_not(mask_3ch))
+
+        # Merge both areas
+        output = cv2.add(background_part, foreground_part)
+        return output