{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "3b7037e6-59ff-47ef-bbb4-2e093e60ddc7", "metadata": {}, "outputs": [], "source": [ "WSS4LUAD\n", "├── 1.training\n", "├── 2.validation\n", "│   ├── background-mask\n", "│   ├── img\n", "│   └── mask\n", "├── 3.testing\n", "│   ├── background-mask\n", "│   └── img\n", "├── __MACOSX\n", "│   ├── 1.training\n", "│   ├── 2.validation\n", "│   │   └── background-mask\n", "│   └── 3.testing\n", "└── mask [error opening dir]" ] }, { "cell_type": "code", "execution_count": 2, "id": "45d32b4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 3], dtype=uint8)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = \"/home/ubuntu/thesis/data/WSSS4LUAD/2.validation/mask/00.png\"\n", "from PIL import Image\n", "img = Image.open(data)\n", "import numpy as np\n", "np.unique(np.array(img))" ] }, { "cell_type": "code", "execution_count": 3, "id": "75c6d45c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img" ] }, { "cell_type": "code", "execution_count": 4, "id": "e43613fb", "metadata": {}, "outputs": [], "source": [ "arr = np.array(img)" ] }, { "cell_type": "code", "execution_count": 5, "id": "59d8f5ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 1, 1, ..., 1, 1, 1],\n", " [1, 1, 1, ..., 1, 1, 1],\n", " [1, 1, 1, ..., 1, 1, 1],\n", " ...,\n", " [0, 0, 0, ..., 1, 1, 1],\n", " [0, 0, 0, ..., 1, 1, 1],\n", " [0, 0, 0, ..., 1, 1, 1]], dtype=uint8)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 7, "id": "8f8c471a", "metadata": {}, "outputs": [], "source": [ "arr[arr == 0] = 255" ] }, { "cell_type": "code", "execution_count": 8, "id": "3b5a5be8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 1, 1, ..., 1, 1, 1],\n", " [ 1, 1, 1, ..., 1, 1, 1],\n", " [ 1, 1, 1, ..., 1, 1, 1],\n", " ...,\n", " [255, 255, 255, ..., 1, 1, 1],\n", " [255, 255, 255, ..., 1, 1, 1],\n", " [255, 255, 255, ..., 1, 1, 1]], dtype=uint8)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 9, "id": "62724397", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2, 2, 2, ..., 2, 2, 2],\n", " [2, 2, 2, ..., 2, 2, 2],\n", " [2, 2, 2, ..., 2, 2, 2],\n", " ...,\n", " [0, 0, 0, ..., 2, 2, 2],\n", " [0, 0, 0, ..., 2, 2, 2],\n", " [0, 0, 0, ..., 2, 2, 2]], dtype=uint8)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr + 1" ] }, { "cell_type": "code", "execution_count": 13, "id": "7ef575c8", "metadata": {}, "outputs": [], "source": [ "data_directory = \"/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data\"\n", "import os\n", "lis = os.walk(data)" ] }, { "cell_type": "code", "execution_count": 15, "id": "76979c0a", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'generator' object is not subscriptable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlis\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[0;31mTypeError\u001b[0m: 'generator' object is not subscriptable" ] } ], "source": [ "lis" ] }, { "cell_type": "code", "execution_count": 25, "id": "322fb16b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/DN\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/NEP25\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training 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Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_131_19456_4096_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_229_5120_8192_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_91_7168_3072_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_538_6144_19456_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_398_6144_14336_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_606_18432_21504_img.jpg\n", "/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/56Nx/12_171/img/56Nx_12_171_300_20480_10240_img.jpg\n" ] } ], "source": [ "for root, dirs, files in os.walk(data):\n", " for name in files:\n", " print(os.path.join(root, name))\n", " for name in dirs:\n", " print(os.path.join(root, name))" ] }, { "cell_type": "code", "execution_count": 4, "id": "f44cf4b6", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "\n", "data_directories = [\"/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data\", \"/home/ubuntu/thesis/data/KPI/KPIs24 Validation Data/Task1_patch_level/data\"]\n", "arr_length = 2\n", "#pointer = []\n", "for (i0, data_directory) in enumerate(data_directories):\n", " dir_list = sorted(os.listdir(data_directory))\n", " arr_length += len(dir_list)\n", " #pointer.append(len(dir_list))\n", " for (i1, d1) in enumerate(dir_list):\n", " subdir_list = sorted(os.listdir(os.path.join(data_directory, d1)))\n", " arr_length += len(subdir_list)\n", "pointer = np.zeros(arr_length, dtype=int)\n", "length = np.zeros(arr_length, dtype=int)\n", "pointer[0] = len(data_directories)\n", "len_last_dir_list = 0\n", "len_last_subdir_list = 0\n", "for (i0, data_directory) in enumerate(data_directories):\n", " dir_list = sorted(os.listdir(data_directory))\n", " len_last_dir_list = len(dir_list)\n", " pointer[pointer[i0]] = pointer[i0] + len_last_dir_list\n", " for (i1, d1) in enumerate(dir_list):\n", " subdir_list = sorted(os.listdir(os.path.join(data_directory, d1)))\n", " len_last_subdir_list = len(subdir_list)\n", " for (i2, d2) in enumerate(subdir_list):\n", " file_list = sorted(os.listdir(os.path.join(data_directory, d1, d2,'img')))\n", " length[pointer[pointer[i0] + i1] + i2] = len(file_list)\n", " length[pointer[i0] + i1] += len(file_list)\n", " length[i0] += len(file_list)\n", " if i1 < len(dir_list) - 1:\n", " pointer[pointer[i0] + i1 + 1] = pointer[pointer[i0] + i1] + len(subdir_list)\n", " \n", " if i0 < len(data_directories) - 1:\n", " pointer[i0 + 1] = pointer[pointer[i0] + len_last_dir_list - 1] + len_last_subdir_list\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 2, "id": "3573fdfc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2, 36, 6, 11, 16, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 40, 42, 44, 46, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pointer" ] }, { "cell_type": "code", "execution_count": 3, "id": "bae3bd2a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5214, 1643, 558, 607, 2263, 1786, 92, 96, 141, 86, 143,\n", " 135, 134, 71, 129, 138, 143, 187, 166, 146, 133, 111,\n", " 156, 177, 152, 147, 150, 93, 156, 218, 128, 358, 390,\n", " 313, 370, 355, 274, 299, 209, 861, 144, 130, 182, 117,\n", " 106, 103, 415, 446])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "length" ] }, { "cell_type": "code", "execution_count": 5, "id": "8d0ed058", "metadata": {}, "outputs": [], "source": [ "def get_kpi( idx):\n", " data_directories = [\"/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data\", \"/home/ubuntu/thesis/data/KPI/KPIs24 Validation Data/Task1_patch_level/data\"]\n", " position = 0\n", " if idx < length[0]:\n", " data_directory = data_directories[0]\n", " position = pointer[0]\n", " else:\n", " data_directory = data_directories[1]\n", " idx -= length[0]\n", " position = pointer[1]\n", " dir_list = sorted(os.listdir(data_directory))\n", " for (i1, d1) in enumerate(dir_list):\n", " if idx < length[position + i1]:\n", " position = pointer[position + i1]\n", " subdir_list = sorted(os.listdir(os.path.join(data_directory, d1)))\n", " for (i2, d2) in enumerate(subdir_list):\n", " if idx < length[position + i2]:\n", " file_list = sorted(os.listdir(os.path.join(data_directory, d1, d2,'img')))\n", " image_name = os.path.join(data_directory, d1, d2, 'img', file_list[idx])\n", " mask_name = os.path.join(data_directory, d1, d2, 'mask', file_list[idx])\n", " return image_name, mask_name\n", " else:\n", " idx -= length[position + i2] \n", " else:\n", " idx -= length[position + i1]" ] }, { "cell_type": "code", "execution_count": 7, "id": "c9dfb603", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/NEP25/08_368_03/img/08_368_03_54_12288_2048_img.jpg',\n", " '/home/ubuntu/thesis/data/KPI/KPIs24 Training Data/Task1_patch_level/data/NEP25/08_368_03/mask/08_368_03_54_12288_2048_img.jpg')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_kpi(1636)" ] }, { "cell_type": "code", "execution_count": null, "id": "6a9f5286", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }