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Simplify the notebook (WIP again)
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‎playing_with_pytorch.ipynb‎

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@@ -19,7 +19,7 @@
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"id": "doj9EzIVIMFv"
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},
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"source": [
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"# PyTorch"
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"# Playing with SimpleITK and nnU-Net"
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]
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},
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{
@@ -31,28 +31,38 @@
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"## Installation"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "oWi4McPs_NuY"
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},
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"source": [
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"!pip install -qq SimpleITK nnunetv2"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%cd /content\n",
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"!git clone https://github.com/woctezuma/playing-with-simpleitk.git"
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"!git clone https://github.com/woctezuma/playing-with-simpleitk.git\n",
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"\n",
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"%pip install -qq SimpleITK nnunetv2"
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],
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"metadata": {
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"id": "vL5WJI3NVxIe"
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},
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"execution_count": null,
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"outputs": []
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"id": "vL5WJI3NVxIe",
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"outputId": "cedbef25-33a3-45f1-e036-913a318ba177",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content\n",
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"Cloning into 'playing-with-simpleitk'...\n",
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"remote: Enumerating objects: 295, done.\u001b[K\n",
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"remote: Counting objects: 100% (42/42), done.\u001b[K\n",
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"remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
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"remote: Total 295 (delta 22), reused 18 (delta 7), pack-reused 253\u001b[K\n",
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"Receiving objects: 100% (295/295), 30.21 MiB | 23.78 MiB/s, done.\n",
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"Resolving deltas: 100% (42/42), done.\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
@@ -63,94 +73,27 @@
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"## Data nomenclature"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "5oFoBRkhIVVX"
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},
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"source": [
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"Patients"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "8AJwRH-8FmaD"
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},
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"source": [
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"def get_patient_indices():\n",
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" patient_indices = [1,2,5,6,8,10,14,16,18,19,21,22,23,24,25,26,27,28,29,30]\n",
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"\n",
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" return patient_indices"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "P65kljpNIRK4"
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},
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"source": [
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"Folders"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "uiLgnz7zFY13"
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},
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"source": [
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"def get_data_folder():\n",
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" data_folder = 'data/'\n",
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"\n",
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" return data_folder\n",
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"\n",
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"def get_image_folder():\n",
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" image_folder = get_data_folder() + 'imagesTr/'\n",
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"\n",
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" return image_folder\n",
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"\n",
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"def get_ground_truth_folder():\n",
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" ground_truth_folder = get_data_folder() + 'labelsTr/'\n",
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"\n",
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" return ground_truth_folder"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NfQXankUIS88"
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},
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"source": [
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"Files"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "jbGlq7B8biXg"
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},
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"source": [
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"def get_file_extension():\n",
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" file_extension = '.nii.gz'\n",
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"ROOT_FOLDER = '/content/'\n",
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"DATA_FOLDER = 'data/'\n",
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"IMAGE_FOLDER = f'{DATA_FOLDER}imagesTr/'\n",
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"GROUND_TRUTH_FOLDER = f'{DATA_FOLDER}labelsTr/'\n",
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"\n",
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" return file_extension\n",
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"PATIENT_INDICES = [1,2,5,6,8,10,14,16,18,19,21,22,23,24,25,26,27,28,29,30]\n",
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"IMAGE_TYPE = '.nii.gz'\n",
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"\n",
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"def get_image_file_name(patient_no, modality_no=0):\n",
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" image_modality_convention = '_{:04.0f}'.format(modality_no)\n",
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" image_file_name = get_image_folder() + 'patientID' + str(patient_no) + image_modality_convention + get_file_extension()\n",
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"\n",
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" return image_file_name\n",
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" return f'{IMAGE_FOLDER}patientID{patient_no}_{modality_no:04}{IMAGE_TYPE}'\n",
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"\n",
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"def get_ground_truth_file_name(patient_no):\n",
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" ground_truth_file_name = get_ground_truth_folder() +'patientID' + str(patient_no) + get_file_extension()\n",
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"\n",
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" return ground_truth_file_name"
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" return f'{GROUND_TRUTH_FOLDER}patientID{patient_no}{IMAGE_TYPE}'"
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],
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"execution_count": null,
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"execution_count": 3,
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"outputs": []
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},
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{
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"!unzip -qq CHAOS_Train_Sets.zip"
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],
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"metadata": {
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"id": "bSD1Q_6Quslt"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%mv /content/Train_Sets /content/playing-with-simpleitk/data/"
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],
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"metadata": {
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"id": "TQ8-7kLU7bs5"
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},
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"execution_count": null,
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"outputs": []
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"id": "bSD1Q_6Quslt",
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"outputId": "3341ee8a-eaf7-4dda-b5b8-d12c670f2a04",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content\n",
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"100 150 100 150 0 0 182 0 --:--:-- --:--:-- --:--:-- 182\n",
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"[CHAOS_Train_Sets.zip]\n",
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" End-of-central-directory signature not found. Either this file is not\n",
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" a zipfile, or it constitutes one disk of a multi-part archive. In the\n",
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" latter case the central directory and zipfile comment will be found on\n",
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" the last disk(s) of this archive.\n",
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"unzip: cannot find zipfile directory in one of CHAOS_Train_Sets.zip or\n",
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" CHAOS_Train_Sets.zip.zip, and cannot find CHAOS_Train_Sets.zip.ZIP, period.\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"DATASET_FNAME = \"Dataset000_MY_DATASET\"\n",
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"NNUNET_FOLDER_NAME = f\"{ROOT_FOLDER}nnUNet_base/{DATASET_FNAME}/\"\n",
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"\n",
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"%cd /content/playing-with-simpleitk\n",
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"!python convert_to_nii.py"
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"%mv /content/Train_Sets data/\n",
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"!python convert_to_nii.py\n",
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"%mkdir -p {NNUNET_FOLDER_NAME}\n",
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"%mv data/output/* {NNUNET_FOLDER_NAME}"
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],
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"metadata": {
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"id": "qugHxbsFvCtM"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "HbPo9F8kB31W"
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},
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"source": [
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"%mkdir -p /content/nnUNet_base/Dataset000_MY_DATASET\n",
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"%mv /content/playing-with-simpleitk/data/output/* /content/nnUNet_base/Dataset000_MY_DATASET/"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"## Tests"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "7sGD_c2Iq5Da"
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},
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"source": [
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"root_folder = '/content/'"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "QgbS9WFvrJQb"
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},
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"source": [
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"patient_indices = get_patient_indices()\n",
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"patient_no = patient_indices[0]"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"import numpy as np\n",
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"import SimpleITK as sitk\n",
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"\n",
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"image_name = root_folder + get_image_file_name(patient_no)\n",
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"\n",
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"image = sitk.ReadImage(image_name)\n",
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"print(image.GetSize())\n",
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"\n",
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"v = sitk.GetArrayViewFromImage(image)\n",
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"\n",
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"np.unique(v)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "5_X6uel5LMmS"
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},
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"source": [
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"import numpy as np\n",
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"import SimpleITK as sitk\n",
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"\n",
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"image_name = root_folder + get_ground_truth_file_name(patient_no)\n",
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"patient_no = PATIENT_INDICES[0]\n",
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"\n",
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"image = sitk.ReadImage(image_name)\n",
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"print(image.GetSize())\n",
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"for image_name in [\n",
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" get_image_file_name(patient_no),\n",
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" get_ground_truth_file_name(patient_no),\n",
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" ]:\n",
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"\n",
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"v = sitk.GetArrayViewFromImage(image)\n",
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" image = sitk.ReadImage(ROOT_FOLDER + image_name)\n",
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" print(image.GetSize())\n",
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"\n",
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"np.unique(v)"
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" v = sitk.GetArrayViewFromImage(image)\n",
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" print(np.unique(v))"
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],
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"execution_count": null,
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"outputs": []
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"source": [
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"## Down-sample images\n",
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"\n",
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"Reference: https://github.com/jonasteuwen/SimpleITK-examples/blob/master/examples/resample_isotropically.py"
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"References:\n",
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"- https://github.com/jonasteuwen/SimpleITK-examples/blob/master/examples/resample_isotropically.py\n",
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"- https://simpleitk.org/SPIE2019_COURSE/02_images_and_resampling.html"
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]
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},
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{

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