{
  "id": 664236,
  "title": "Multi channel masks",
  "url": "/competitions/recodai-luc-scientific-image-forgery-detection/discussion/664236",
  "author_name": "",
  "post_date": "2025-12-23T11:03:04.696799600Z",
  "votes": 2,
  "comment_count": 5,
  "views": 0,
  "content": "<p>HEllo everyone, there are some masks in the original train masks that have channels with each channel annotated differently. Do these different channel masks have any meaning? Or is it just an error?\nREgards </p>",
  "messages": [
    {
      "id": "3380916",
      "postDate": "12/23/2025 11:03:04",
      "content": "<p>HEllo everyone, there are some masks in the original train masks that have channels with each channel annotated differently. Do these different channel masks have any meaning? Or is it just an error?\nREgards </p>",
      "rawMarkdown": "HEllo everyone, there are some masks in the original train masks that have channels with each channel annotated differently. Do these different channel masks have any meaning? Or is it just an error?\nREgards",
      "votes": null
    },
    {
      "id": "3380917",
      "postDate": "12/23/2025 11:05:58",
      "content": "<p>Each channel means different region of copy-move. </p>",
      "rawMarkdown": "Each channel means different region of copy-move.",
      "votes": null
    },
    {
      "id": "3381184",
      "postDate": "12/24/2025 01:19:55",
      "content": "<p>are we expected to create multichannel masks?</p>",
      "rawMarkdown": "are we expected to create multichannel masks?",
      "votes": null
    },
    {
      "id": "3381199",
      "postDate": "12/24/2025 02:23:05",
      "content": "<p>the simplest and most efficient way would be to use np.max or np.sum ```python</p>\n<h1>mask = np.load(mask_paths[2740])</h1>\n<h1>print(mask.shape)</h1>\n<h1># binary_mask = (mask.sum(axis=0) &gt; 0).astype(np.uint8)</h1>\n<h1>binary_mask = mask.max(axis=0)</h1>\n<h1>print(binary_mask.shape)</h1>\n<h1>plt.figure(figsize=(10, 8))</h1>\n<h1>plt.subplot(131)</h1>\n<h1>plt.imshow(mask[0, …]);</h1>\n<h1>plt.subplot(132)</h1>\n<h1>plt.imshow(mask[1, …]);</h1>\n<h1>plt.subplot(133)</h1>\n<h1>plt.imshow(binary_mask);</h1>\n<p>``` to create a single unified binary mask</p>",
      "rawMarkdown": "the simplest and most efficient way would be to use np.max or np.sum ```python\n# mask = np.load(mask_paths[2740])\n# print(mask.shape)\n# # binary_mask = (mask.sum(axis=0) > 0).astype(np.uint8)\n# binary_mask = mask.max(axis=0)\n# print(binary_mask.shape)\n# plt.figure(figsize=(10, 8))\n# plt.subplot(131)\n# plt.imshow(mask[0, ...]);\n# plt.subplot(132)\n# plt.imshow(mask[1, ...]);\n# plt.subplot(133)\n# plt.imshow(binary_mask);\n``` to create a single unified binary mask",
      "votes": null
    },
    {
      "id": "3381590",
      "postDate": "12/25/2025 01:40:07",
      "content": "<blockquote>\n  <p>Each channel means different region of copy-move.\n  Is it guaranteed that way? ie. no RGB, when there are 3 channels and no gray scale (u16 format) in 2 channel masks?</p>\n</blockquote>",
      "rawMarkdown": "> Each channel means different region of copy-move.\nIs it guaranteed that way? ie. no RGB, when there are 3 channels and no gray scale (u16 format) in 2 channel masks?",
      "votes": null
    },
    {
      "id": "3381618",
      "postDate": "12/25/2025 03:39:59",
      "content": "<p>well, the channels are irregular, therefore creating a third channel &gt; 1 will either require discarding channels or padding channels</p>",
      "rawMarkdown": "well, the channels are irregular, therefore creating a third channel > 1 will either require discarding channels or padding channels",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3380917,
      "author_name": "theodorlu",
      "author_url": "",
      "post_date": "12/23/2025 11:05:58",
      "content": "<p>Each channel means different region of copy-move. </p>",
      "votes": null,
      "replies": [
        {
          "id": 3381184,
          "author_name": "cjpal18",
          "author_url": "",
          "post_date": "12/24/2025 01:19:55",
          "content": "<p>are we expected to create multichannel masks?</p>",
          "votes": null,
          "replies": [
            {
              "id": 3381199,
              "author_name": "samu2505",
              "author_url": "",
              "post_date": "12/24/2025 02:23:05",
              "content": "<p>the simplest and most efficient way would be to use np.max or np.sum ```python</p>\n<h1>mask = np.load(mask_paths[2740])</h1>\n<h1>print(mask.shape)</h1>\n<h1># binary_mask = (mask.sum(axis=0) &gt; 0).astype(np.uint8)</h1>\n<h1>binary_mask = mask.max(axis=0)</h1>\n<h1>print(binary_mask.shape)</h1>\n<h1>plt.figure(figsize=(10, 8))</h1>\n<h1>plt.subplot(131)</h1>\n<h1>plt.imshow(mask[0, …]);</h1>\n<h1>plt.subplot(132)</h1>\n<h1>plt.imshow(mask[1, …]);</h1>\n<h1>plt.subplot(133)</h1>\n<h1>plt.imshow(binary_mask);</h1>\n<p>``` to create a single unified binary mask</p>",
              "votes": null,
              "replies": []
            }
          ]
        },
        {
          "id": 3381590,
          "author_name": "venkatagoli",
          "author_url": "",
          "post_date": "12/25/2025 01:40:07",
          "content": "<blockquote>\n  <p>Each channel means different region of copy-move.\n  Is it guaranteed that way? ie. no RGB, when there are 3 channels and no gray scale (u16 format) in 2 channel masks?</p>\n</blockquote>",
          "votes": null,
          "replies": [
            {
              "id": 3381618,
              "author_name": "samu2505",
              "author_url": "",
              "post_date": "12/25/2025 03:39:59",
              "content": "<p>well, the channels are irregular, therefore creating a third channel &gt; 1 will either require discarding channels or padding channels</p>",
              "votes": null,
              "replies": []
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3380916": "HEllo everyone, there are some masks in the original train masks that have channels with each channel annotated differently. Do these different channel masks have any meaning? Or is it just an error?\nREgards",
    "3380917": "Each channel means different region of copy-move.",
    "3381184": "are we expected to create multichannel masks?",
    "3381199": "the simplest and most efficient way would be to use np.max or np.sum ```python\n# mask = np.load(mask_paths[2740])\n# print(mask.shape)\n# # binary_mask = (mask.sum(axis=0) > 0).astype(np.uint8)\n# binary_mask = mask.max(axis=0)\n# print(binary_mask.shape)\n# plt.figure(figsize=(10, 8))\n# plt.subplot(131)\n# plt.imshow(mask[0, ...]);\n# plt.subplot(132)\n# plt.imshow(mask[1, ...]);\n# plt.subplot(133)\n# plt.imshow(binary_mask);\n``` to create a single unified binary mask",
    "3381590": "> Each channel means different region of copy-move.\nIs it guaranteed that way? ie. no RGB, when there are 3 channels and no gray scale (u16 format) in 2 channel masks?",
    "3381618": "well, the channels are irregular, therefore creating a third channel > 1 will either require discarding channels or padding channels"
  },
  "source": "meta"
}