prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  CCA failure when enabled
  **Describe the bug**
  The CCA (Largest Connected Component Analysis) function was implemented as a standalone function, which causes it to fail when called in the segmentation pipeline with post-processing enabled. The expected behavior is a likely failure due to this issue.
  
  **To Reproduce**
  Run a segmentation pipeline with CCA enabled for the post-processing.
  
  **Expected behavior**
  The CCA function should be corrected and integrated with the segmentation pipeline to work correctly and tested
  
  **GaNDLF Version**
  <!-- Put the output of the following command:
  python -c 'import GANDLF as g;print(g.__version__)'
  -->
  Version information of the GaNDLF package in the virtual environment. 0.0.16-dev
  
  **Desktop (please complete the following information):**
   - OS: Linux, Ubuntu
   - Version (including Build information, if any): 22.04
  
  **Additional context**
  None
  
  
  </issue>
  <code>
  [start of GANDLF/data/post_process/morphology.py]
  1 import torch
  2 import torch.nn.functional as F
  3 from skimage.measure import label
  4 import numpy as np
  5 from scipy.ndimage import binary_fill_holes, binary_closing
  6 from GANDLF.utils.generic import get_array_from_image_or_tensor
  7 
  8 
  9 def torch_morphological(input_image, kernel_size=1, mode="dilation"):
  10     """
  11     This function enables morphological operations using torch. Adapted from https://github.com/DIVA-DIA/Generating-Synthetic-Handwritten-Historical-Documents/blob/e6a798dc2b374f338804222747c56cb44869af5b/HTR_ctc/utils/auxilary_functions.py#L10.
  12 
  13     Args:
  14         input_image (torch.Tensor): The input image.
  15         kernel_size (list): The size of the window to take a max over.
  16         mode (str): The type of morphological operation to perform.
  17 
  18     Returns:
  19         torch.Tensor: The output image after morphological operations.
  20     """
  21 
  22     if len(input_image.shape) == 4:
  23         max_pool = F.max_pool2d
  24     elif len(input_image.shape) == 5:
  25         max_pool = F.max_pool3d
  26     else:
  27         raise ValueError("Input image has invalid shape for morphological operations.")
  28 
  29     if mode == "dilation":
  30         output_image = max_pool(
  31             input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  32         )
  33     elif mode == "erosion":
  34         output_image = -max_pool(
  35             -input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  36         )
  37     elif mode == "closing":
  38         output_image = max_pool(
  39             input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  40         )
  41         output_image = -max_pool(
  42             -output_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  43         )
  44     elif mode == "opening":
  45         output_image = -max_pool(
  46             -input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  47         )
  48         output_image = max_pool(
  49             output_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2
  50         )
  51 
  52     return output_image
  53 
  54 
  55 def fill_holes(input_image, params=None):
  56     """
  57     This function fills holes in masks.
  58 
  59     Args:
  60         input_image (torch.Tensor): The input image.
  61         params (dict): The parameters dict; unused.
  62 
  63     Returns:
  64         torch.Tensor: The output image after morphological operations.
  65     """
  66     input_image_array = get_array_from_image_or_tensor(input_image).astype(int)
  67     input_image_array_closed = binary_closing(input_image_array)
  68     # Fill the holes in binary objects
  69     output_array = binary_fill_holes(input_image_array_closed).astype(int)
  70 
  71     return torch.from_numpy(output_array)
  72 
  73 
  74 def cca(input_image):
  75     """
  76     This function performs connected component analysis on the input image.
  77 
  78     Args:
  79         input_image (torch.Tensor): The input image.
  80         params (dict): The parameters dict;
  81 
  82     Returns:
  83         torch.Tensor: The output image after morphological operations.
  84     """
  85     seg = get_array_from_image_or_tensor(input_image)
  86     mask = seg != 0
  87 
  88     connectivity = input_image.dim() - 1
  89     labels_connected = label(mask, connectivity=connectivity)
  90     labels_connected_sizes = [
  91         np.sum(labels_connected == i) for i in np.unique(labels_connected)
  92     ]
  93     largest_region = np.argmax(labels_connected_sizes[1:]) + 1
  94     seg[labels_connected != largest_region] = 0
  95     return seg
  96
  [end of GANDLF/data/post_process/morphology.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/GANDLF/data/post_process/morphology.py b/GANDLF/data/post_process/morphology.py
  --- a/GANDLF/data/post_process/morphology.py
  +++ b/GANDLF/data/post_process/morphology.py
  @@ -71,7 +71,7 @@
       return torch.from_numpy(output_array)
   
   
  -def cca(input_image):
  +def cca(input_image, params=None):
       """
       This function performs connected component analysis on the input image.
   
  @@ -85,11 +85,15 @@
       seg = get_array_from_image_or_tensor(input_image)
       mask = seg != 0
   
  -    connectivity = input_image.dim() - 1
  +    connectivity = input_image.ndim - 1
       labels_connected = label(mask, connectivity=connectivity)
       labels_connected_sizes = [
           np.sum(labels_connected == i) for i in np.unique(labels_connected)
       ]
  -    largest_region = np.argmax(labels_connected_sizes[1:]) + 1
  +    largest_region = 0
  +    if len(labels_connected_sizes) > 1:
  +        largest_region = np.argmax(labels_connected_sizes[1:]) + 1
       seg[labels_connected != largest_region] = 0
  +
       return seg
  +
  
verification_info: {"golden_diff": "diff --git a/GANDLF/data/post_process/morphology.py b/GANDLF/data/post_process/morphology.py\n--- a/GANDLF/data/post_process/morphology.py\n+++ b/GANDLF/data/post_process/morphology.py\n@@ -71,7 +71,7 @@\n     return torch.from_numpy(output_array)\n \n \n-def cca(input_image):\n+def cca(input_image, params=None):\n     \"\"\"\n     This function performs connected component analysis on the input image.\n \n@@ -85,11 +85,15 @@\n     seg = get_array_from_image_or_tensor(input_image)\n     mask = seg != 0\n \n-    connectivity = input_image.dim() - 1\n+    connectivity = input_image.ndim - 1\n     labels_connected = label(mask, connectivity=connectivity)\n     labels_connected_sizes = [\n         np.sum(labels_connected == i) for i in np.unique(labels_connected)\n     ]\n-    largest_region = np.argmax(labels_connected_sizes[1:]) + 1\n+    largest_region = 0\n+    if len(labels_connected_sizes) > 1:\n+        largest_region = np.argmax(labels_connected_sizes[1:]) + 1\n     seg[labels_connected != largest_region] = 0\n+\n     return seg\n+\n", "issue": "CCA failure when enabled\n**Describe the bug**\r\nThe CCA (Largest Connected Component Analysis) function was implemented as a standalone function, which causes it to fail when called in the segmentation pipeline with post-processing enabled. The expected behavior is a likely failure due to this issue.\r\n\r\n**To Reproduce**\r\nRun a segmentation pipeline with CCA enabled for the post-processing.\r\n\r\n**Expected behavior**\r\nThe CCA function should be corrected and integrated with the segmentation pipeline to work correctly and tested\r\n\r\n**GaNDLF Version**\r\n<!-- Put the output of the following command:\r\npython -c 'import GANDLF as g;print(g.__version__)'\r\n-->\r\nVersion information of the GaNDLF package in the virtual environment. 0.0.16-dev\r\n\r\n**Desktop (please complete the following information):**\r\n - OS: Linux, Ubuntu\r\n - Version (including Build information, if any): 22.04\r\n\r\n**Additional context**\r\nNone\r\n\n", "before_files": [{"content": "import torch\nimport torch.nn.functional as F\nfrom skimage.measure import label\nimport numpy as np\nfrom scipy.ndimage import binary_fill_holes, binary_closing\nfrom GANDLF.utils.generic import get_array_from_image_or_tensor\n\n\ndef torch_morphological(input_image, kernel_size=1, mode=\"dilation\"):\n    \"\"\"\n    This function enables morphological operations using torch. Adapted from https://github.com/DIVA-DIA/Generating-Synthetic-Handwritten-Historical-Documents/blob/e6a798dc2b374f338804222747c56cb44869af5b/HTR_ctc/utils/auxilary_functions.py#L10.\n\n    Args:\n        input_image (torch.Tensor): The input image.\n        kernel_size (list): The size of the window to take a max over.\n        mode (str): The type of morphological operation to perform.\n\n    Returns:\n        torch.Tensor: The output image after morphological operations.\n    \"\"\"\n\n    if len(input_image.shape) == 4:\n        max_pool = F.max_pool2d\n    elif len(input_image.shape) == 5:\n        max_pool = F.max_pool3d\n    else:\n        raise ValueError(\"Input image has invalid shape for morphological operations.\")\n\n    if mode == \"dilation\":\n        output_image = max_pool(\n            input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n    elif mode == \"erosion\":\n        output_image = -max_pool(\n            -input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n    elif mode == \"closing\":\n        output_image = max_pool(\n            input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n        output_image = -max_pool(\n            -output_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n    elif mode == \"opening\":\n        output_image = -max_pool(\n            -input_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n        output_image = max_pool(\n            output_image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2\n        )\n\n    return output_image\n\n\ndef fill_holes(input_image, params=None):\n    \"\"\"\n    This function fills holes in masks.\n\n    Args:\n        input_image (torch.Tensor): The input image.\n        params (dict): The parameters dict; unused.\n\n    Returns:\n        torch.Tensor: The output image after morphological operations.\n    \"\"\"\n    input_image_array = get_array_from_image_or_tensor(input_image).astype(int)\n    input_image_array_closed = binary_closing(input_image_array)\n    # Fill the holes in binary objects\n    output_array = binary_fill_holes(input_image_array_closed).astype(int)\n\n    return torch.from_numpy(output_array)\n\n\ndef cca(input_image):\n    \"\"\"\n    This function performs connected component analysis on the input image.\n\n    Args:\n        input_image (torch.Tensor): The input image.\n        params (dict): The parameters dict;\n\n    Returns:\n        torch.Tensor: The output image after morphological operations.\n    \"\"\"\n    seg = get_array_from_image_or_tensor(input_image)\n    mask = seg != 0\n\n    connectivity = input_image.dim() - 1\n    labels_connected = label(mask, connectivity=connectivity)\n    labels_connected_sizes = [\n        np.sum(labels_connected == i) for i in np.unique(labels_connected)\n    ]\n    largest_region = np.argmax(labels_connected_sizes[1:]) + 1\n    seg[labels_connected != largest_region] = 0\n    return seg\n", "path": "GANDLF/data/post_process/morphology.py"}]}