Added a new aggresive cut function

random/understand_p1.ipynb

@@ -139,6 +139,27 @@
     "    return ans"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<image.Image at 0x79231c61bc20>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = Image(0, 0, 3, 3, [[1, 0, 1], [0, 1, 0], [1, 0, 1]])\n",
+    "img.list_components()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,

src/arclang/image.py

@@ -8,6 +8,7 @@
 
 
 Point = namedtuple("Point", ["x", "y"])
+from scipy.ndimage import label, generate_binary_structure
 
 
 class Image:
@@ -138,7 +139,31 @@ def list_components(self) -> List["Image"]:
 
     def count_components(self) -> int:
         return len(self.list_components())
+
+    def count_components_col(self) -> int:
+        return len(self.list_same_color_components())
 
+
+    def aggressive_connected_components(self, connectivity=2) -> List["Image"]:
+        """
+        Find connected components with a more aggressive connectivity.
+        
+        :param connectivity: 1 for 4-connectivity, 2 for 8-connectivity, 
+                             can be increased for more aggressive connectivity
+        :return: List of Image objects, each representing a component
+        """
+        # Create a structure for the given connectivity
+        struct = generate_binary_structure(2, connectivity)
+
+        # Label the connected components
+        labeled_array, num_features = label(self.mask > 0, structure=struct)
+
+        components = []
+        for i in range(1, num_features + 1):
+            component = np.where(labeled_array == i, self.mask, 0)
+            components.append(Image(self.x, self.y, self.w, self.h, component))
+
+        return components
 
 
     def majority_col(self, include0: int = 0) -> int:

tests/test_image.py

@@ -2,9 +2,9 @@
 
 import numpy as np
 
-from arclang.image import Image  # Assuming your classes are in 'image_module.py'
 from arclang.image import Piece
 from arclang.image import Point
+from arclang.image import Image  # Assuming your classes are in 'image_module.py'
 
 
 class TestImageAdditional(unittest.TestCase):
@@ -39,7 +39,7 @@ def test_is_rectangle(self):
 
     def test_count_components(self):
         img = Image(0, 0, 3, 3, [[1, 0, 1], [0, 1, 0], [1, 0, 1]])
-        self.assertEqual(img.count_components(), 5)
+        self.assertEqual(img.count_components_col(), 5)
 
     def test_majority_col(self):
         img = Image(0, 0, 3, 3, [[1, 2, 1], [2, 1, 2], [1, 2, 1]])