feat: Dockerize and complete project
This commit is contained in:
mohamad
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from django.apps import AppConfig
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class CoreConfig(AppConfig):
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default_auto_field = "django.db.models.BigAutoField"
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name = "apps.core"
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verbose_name = "Core"
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@@ -0,0 +1,10 @@
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from apps.products.models import MainProduct
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def navigation(request):
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main_products = (
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MainProduct.objects.filter(is_active=True)
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.prefetch_related("sub_products")
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.order_by("order", "name")
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)
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return {"nav_main_products": main_products}
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@@ -0,0 +1,400 @@
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from django.core.management.base import BaseCommand
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from django.db import transaction
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from apps.pages.models import DownloadItem, FAQEntry
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from apps.products.models import Article, ArticleSection, MainProduct, SubProduct
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MAIN_PRODUCTS = [
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{
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"name": "ViSERA",
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"slug": "visera",
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"short_description": "Visualized & Standardized Environment for Radiomics Analysis",
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"description": (
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"ViSERA is a free, open-source software specialized for visualization, "
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"processing, segmentation, registration, fusion and analysis of medical and "
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"biomedical images, including radiomics and machine learning analysis. "
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"ViSERA is a major, entirely-revamped upgrade to the original SERA "
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"(Matlab-based), now built on Python for broader accessibility and community "
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"contribution. It enables standardized and reproducible radiomic feature "
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"extraction in compliance with the Image Biomarker Standardization Initiative "
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"(IBSI 1.0), and implements image filters standardized against IBSI 2.0."
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),
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"order": 1,
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"sub_products": [
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{
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"name": "Image Processing",
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"slug": "image-processing",
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"short_description": "Standardized filtering, registration, and fusion techniques",
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"description": (
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"Advanced image processing capabilities including standardized filtering "
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"techniques compliant with IBSI 2.0, image registration, fusion, and "
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"Standardized Uptake Value (SUV) conversion. ViSERA employs popular "
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"image processing algorithms to create end-to-end standardized workflows "
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"for consistent, reproducible research outcomes."
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),
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"order": 1,
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"articles": [
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{
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"title": "Image Filtering Techniques",
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"description": (
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"ViSERA implements a comprehensive set of image filtering techniques "
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"fully standardized against the Image Biomarker Standardization "
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"Initiative (IBSI) phase 2. These filters enable reproducible "
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"preprocessing across institutions and studies."
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),
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"order": 1,
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"sections": [
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{"title": "Standardization", "value": "IBSI 2.0 compliant", "order": 1},
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{
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"title": "Available Filters",
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"value": "Mean, Gaussian, Laplacian of Gaussian (LoG), Laws kernels, Gabor, Wavelets (PyWavelets), Log-Sigma",
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"order": 2,
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},
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{"title": "Author", "value": "Tecvico Corp R&D Team", "order": 3},
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],
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},
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{
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"title": "Image Registration & Fusion",
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"description": (
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"ViSERA provides robust image registration and fusion methods, "
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"enabling multi-modal image alignment for PET/CT, PET/MRI, and "
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"other combined modality studies. Standardized Uptake Value (SUV) "
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"conversion is also supported."
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),
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"order": 2,
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"sections": [
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{"title": "Registration Methods", "value": "Rigid, Affine, Deformable (B-spline)", "order": 1},
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{"title": "Fusion Techniques", "value": "Overlay, weighted average, multi-modal blending", "order": 2},
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{"title": "Special Feature", "value": "Standardized Uptake Value (SUV) conversion", "order": 3},
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],
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},
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],
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},
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{
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"name": "Radiomics Features",
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"slug": "radiomics-features",
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"short_description": "IBSI 1.0 compliant handcrafted radiomic feature extraction",
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"description": (
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"ViSERA provides comprehensive handcrafted radiomic feature extraction "
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"fully standardized by the Image Biomarker Standardization Initiative "
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"(IBSI 1.0). Features are computed from segmented regions of interest "
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"across multiple image modalities, enabling reproducible quantitative "
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"imaging biomarker research."
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),
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"order": 2,
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"articles": [
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{
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"title": "IBSI Compliant Feature Extraction",
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"description": (
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"ViSERA computes a comprehensive set of radiomic features "
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"covering all IBSI 1.0 feature classes. Features are extracted "
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"from segmented Regions of Interest (ROIs) and are fully "
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"reproducible across different platforms and institutions."
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),
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"order": 1,
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"sections": [
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{"title": "Standardization", "value": "IBSI 1.0 compliant", "order": 1},
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{
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"title": "Feature Classes",
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"value": "Shape (3D & 2D), First-order Statistics, GLCM, GLRLM, GLSZM, GLDM, NGTDM",
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"order": 2,
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},
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{"title": "Output Formats", "value": "CSV, JSON, Excel", "order": 3},
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{"title": "Reference", "value": "Zwanenburg et al. (2020), Radiology", "order": 4},
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],
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},
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],
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},
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{
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"name": "Medical Image Visualization",
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"slug": "medical-image-visualization",
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"short_description": "Professional multi-modality medical image viewer",
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"description": (
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"ViSERA includes a professional medical image viewer that supports "
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"multiple imaging modalities and file formats. The viewer provides "
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"comfortable, intuitive controls for slice navigation, windowing, "
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"zoom, and annotation, suitable for radiation oncologists, radiologists, "
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"physicists, and data scientists."
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),
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"order": 3,
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"articles": [
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{
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"title": "Multi-Modal Image Viewer",
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"description": (
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"The integrated viewer supports simultaneous display of multiple "
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"image modalities with linked cursors, adjustable window/level, "
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"and overlay capabilities. RT struct contours are rendered "
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"directly over the underlying images."
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),
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"order": 1,
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"sections": [
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{"title": "Supported Modalities", "value": "CT, MRI, PET, SPECT, CBCT", "order": 1},
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{"title": "File Formats", "value": "DICOM, NIFTI (.nii, .nii.gz), NRRD, MHA, NII", "order": 2},
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{"title": "Special Support", "value": "RT Struct, RT Dose, RT Plan visualization", "order": 3},
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],
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},
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],
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},
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{
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"name": "Format Conversion",
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"slug": "format-conversion",
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"short_description": "Professional converter for medical imaging file formats",
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"description": (
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"ViSERA provides a professional image format converter supporting all "
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"major medical imaging standards. Seamlessly convert between DICOM, "
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"NIFTI, NRRD, MHA, and other formats without loss of spatial metadata "
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"or patient information integrity."
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),
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"order": 4,
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"articles": [
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{
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"title": "Medical Image Format Converter",
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"description": (
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"The built-in converter handles complex DICOM series reconstruction, "
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"preserving spatial orientation, voxel spacing, and relevant metadata "
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"throughout conversion. Batch conversion is supported for large "
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"research datasets."
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),
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"order": 1,
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"sections": [
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{"title": "Input Formats", "value": "DICOM (all SOP classes), NIFTI, NRRD, NII, MHA, MetaImage", "order": 1},
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{"title": "Output Formats", "value": "NIFTI (.nii.gz), NRRD, MHA, NII", "order": 2},
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{"title": "Batch Processing", "value": "Supported — process entire datasets automatically", "order": 3},
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],
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},
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],
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},
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{
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"name": "Workflow Management",
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"slug": "workflow-management",
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"short_description": "Reproducible research workflow creation and sharing",
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"description": (
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"ViSERA's workflow management system allows researchers to design, save, "
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"share, and reuse analysis pipelines. Workflows connect individual "
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"processing steps — from image loading and preprocessing to feature "
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"extraction and machine learning — into reproducible, shareable sequences "
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"that ensure consistency across studies and institutions."
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),
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"order": 5,
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"articles": [
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{
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"title": "Reproducible Research Workflows",
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"description": (
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"Create end-to-end analysis pipelines by visually connecting "
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"processing nodes. Each workflow can be exported, shared with "
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"collaborators, and re-executed to reproduce results on new datasets."
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),
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"order": 1,
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"sections": [
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{"title": "Key Benefit", "value": "Usability, Reusability and Reproducibility (URR)", "order": 1},
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{"title": "Collaboration", "value": "Share workflows, datasets, and results with research teams", "order": 2},
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{"title": "Compatibility", "value": "Works with all supported image modalities and feature extractors", "order": 3},
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],
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},
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],
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},
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],
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},
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]
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FAQ_ENTRIES = [
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{
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"question": "What is the ViSERA license?",
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"answer": (
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"ViSERA is free and open-source for research purposes.\n\n"
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"License: CC BY-NC-SA (Creative Commons Attribution-NonCommercial-ShareAlike). "
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"This means you may use, share, and adapt the software for non-commercial "
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"research purposes, provided you give appropriate credit and distribute "
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"derivatives under the same license."
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),
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"order": 1,
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},
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{
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"question": "How do I cite ViSERA in my research?",
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"answer": (
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"Please cite the following reference if you publish results obtained with "
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"the help of ViSERA:\n\n"
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"M. R. Salmanpour, I. Shiri, M. Hosseinzadeh, H. Zaidi, S. Ashrafinia, "
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"M. Oveisi, A. Rahmim. ViSERA: Visualized & Standardized Environment for "
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"Radiomics Analysis — A Shareable, Executable, and Reproducible Workflow "
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"Generator. Proc. IEEE Medical Imaging Conference, 2023."
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),
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"order": 2,
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},
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{
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"question": "Which operating systems does ViSERA support?",
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"answer": (
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"ViSERA currently fully supports Windows 10 and above (64-bit). "
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"New versions to support macOS and Linux systems are under active development "
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"and coming soon. Follow our Discord or check the Downloads page for updates."
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),
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"order": 3,
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},
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{
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"question": "Can I install a new version over an existing installation?",
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"answer": (
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"Yes, you can install the new version without removing the previous one. "
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"However, if you encounter any problems after upgrading, we recommend "
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"uninstalling the old version first, then performing a clean installation "
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"of the new release."
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),
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"order": 4,
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},
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{
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"question": "Is ViSERA suitable for clinical use?",
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"answer": (
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"ViSERA is designed and intended exclusively for research purposes. "
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"It is not certified for clinical diagnostic use. Always consult with "
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"qualified medical professionals for clinical decisions."
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),
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"order": 5,
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},
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{
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"question": "Where can I get support or report issues?",
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"answer": (
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"Support is available via email at support@tecvico.com and through our "
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"community Discord server. For bug reports and feature requests, please "
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"use the Discord forum or contact us directly by email."
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),
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"order": 6,
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},
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]
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DOWNLOAD_ITEMS = [
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{
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"name": "ViSERA Desktop",
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"platform": "windows",
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"version": "1.0.0",
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"download_url": "https://github.com/tecvico/visera/releases/latest/download/ViSERA-Setup.exe",
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"description": "Windows 10 and above (64-bit). Installer package.",
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"is_active": True,
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"order": 1,
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},
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{
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"name": "ViSERA Desktop",
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"platform": "macos",
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"version": "Coming Soon",
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"download_url": "#",
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"description": "macOS version is under development.",
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"is_active": False,
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"order": 2,
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},
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{
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"name": "ViSERA Desktop",
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"platform": "linux",
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"version": "Coming Soon",
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"download_url": "#",
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"description": "Linux version is under development.",
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"is_active": False,
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"order": 3,
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},
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]
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class Command(BaseCommand):
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help = "Seed the database with initial Tecvico / ViSERA content from visera.ca"
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def add_arguments(self, parser):
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parser.add_argument(
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"--flush",
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action="store_true",
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help="Delete all existing seed data before re-seeding",
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)
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@transaction.atomic
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def handle(self, *args, **options):
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if options["flush"]:
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self.stdout.write("Flushing existing seed data...")
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ArticleSection.objects.all().delete()
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Article.objects.all().delete()
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SubProduct.objects.all().delete()
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MainProduct.objects.all().delete()
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FAQEntry.objects.all().delete()
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DownloadItem.objects.all().delete()
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self._seed_products()
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self._seed_faq()
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self._seed_downloads()
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self.stdout.write(self.style.SUCCESS("Content seeded successfully."))
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def _seed_products(self):
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for product_data in MAIN_PRODUCTS:
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sub_products_data = product_data.pop("sub_products")
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main_product, created = MainProduct.objects.get_or_create(
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slug=product_data["slug"],
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defaults=product_data,
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)
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if not created:
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for field, value in product_data.items():
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setattr(main_product, field, value)
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main_product.save()
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action = "Created" if created else "Updated"
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self.stdout.write(f" {action} main product: {main_product.name}")
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for sub_data in sub_products_data:
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articles_data = sub_data.pop("articles")
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sub_product, sub_created = SubProduct.objects.get_or_create(
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main_product=main_product,
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slug=sub_data["slug"],
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defaults=sub_data,
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)
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if not sub_created:
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for field, value in sub_data.items():
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setattr(sub_product, field, value)
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sub_product.save()
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sub_action = "Created" if sub_created else "Updated"
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self.stdout.write(f" {sub_action} sub-product: {sub_product.name}")
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for article_data in articles_data:
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sections_data = article_data.pop("sections")
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article, art_created = Article.objects.get_or_create(
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sub_product=sub_product,
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title=article_data["title"],
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defaults=article_data,
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)
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if not art_created:
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for field, value in article_data.items():
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setattr(article, field, value)
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article.save()
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art_action = "Created" if art_created else "Updated"
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self.stdout.write(f" {art_action} article: {article.title}")
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for section_data in sections_data:
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section, _ = ArticleSection.objects.get_or_create(
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article=article,
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title=section_data["title"],
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defaults=section_data,
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)
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def _seed_faq(self):
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for entry_data in FAQ_ENTRIES:
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faq, created = FAQEntry.objects.get_or_create(
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question=entry_data["question"],
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defaults=entry_data,
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)
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if not created:
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for field, value in entry_data.items():
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setattr(faq, field, value)
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faq.save()
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action = "Created" if created else "Updated"
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self.stdout.write(f" {action} FAQ: {faq.question[:60]}...")
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def _seed_downloads(self):
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for item_data in DOWNLOAD_ITEMS:
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item, created = DownloadItem.objects.get_or_create(
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name=item_data["name"],
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platform=item_data["platform"],
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defaults=item_data,
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)
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if not created:
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for field, value in item_data.items():
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setattr(item, field, value)
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item.save()
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action = "Created" if created else "Updated"
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self.stdout.write(f" {action} download: {item}")
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