matlab-integrate-pytorch-vision

Name: matlab-integrate-pytorch-vision
Author: matlab

815stars105forks

How to install matlab-integrate-pytorch-vision?

matlab/matlab-agentic-toolkit/matlab-integrate-pytorch-vision

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-integrate-pytorch-vision

Installs into the current project.

Summary

Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation,

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SKILL.md

1	# MPyReq MATLAB Interface Builder
2
3	Build a MATLAB interface to a Python/PyTorch model repository using the MPyReq framework.
4
5	## When to Use
6
7	- User asks to interface MATLAB with a Python image processing or computer vision model (segmentation, depth estimation, object detection, image generation, super-resolution, pose estimation, optical flow, salient object detection, etc.)
8	- User provides a GitHub repository URL for a vision/image model and wants to call it from MATLAB
9	- User asks to "create an MPyReq wrapper" or "MPyReq demo" for an image/CV model
10	- User wants to run a pip-installable vision model library (e.g., Cellpose, SAM2, Depth-Pro, BiRefNet, StarDist) from MATLAB
11
12	## When Not to Use
13
14	- General-purpose Python-MATLAB interfacing (no vision/image model involved)
15	- Non-vision models: NLP, audio, tabular, reinforcement learning, time-series
16	- Model deployment, containerization, or inference servers
17	- Pure MATLAB image processing workflows with no Python dependency
18	- Creating Python code (this skill creates MATLAB code that calls Python)
19
20	## Prerequisites: MPyReq on the MATLAB Path
21
22	Before generating any demo script, verify that MPyReq is available. Run `which MPyReq` via the MATLAB MCP server (if available) or ask the user to confirm.
23
24	### If MPyReq is NOT on the MATLAB path:
25
26	1. Download MPyReq from the MATLAB File Exchange:
27	https://mathworks.com/matlabcentral/fileexchange/182230-matlab-based-python-requirements-manager
28	2. Install it — either:
29	- Open the downloaded `.mltbx` file in MATLAB (double-click), which installs it as a MATLAB Add-On automatically, or
30	- Extract the files and add the folder containing `MPyReq.m` to the MATLAB path:
31	```matlab
32	addpath("/path/to/mpyreq");
33	savepath; % persist across sessions
34	```
35	3. Verify by running `which MPyReq` in MATLAB — it should return the path to `MPyReq.m`.
36
37	Do not proceed with demo generation until MPyReq is confirmed on the path.
38
39	## Input
40
41	Ask the user for:
42	1. GitHub repository URL — the Python model repository to interface with
43	2. What the model does (optional) — to help identify the right inference example
44
45	## Step 1: Analyze the Repository
46
47	Fetch and analyze the GitHub repository to determine:
48
49	- Python version requirement — check `setup.py`, `setup.cfg`, `pyproject.toml`, or README for the required Python version. Default to `"3.12"` if not specified. Use `"3.11"` if the project needs older compatibility.
50	- Installation method — determine how the project is installed:
51	- If it uses `torch.hub.load()`: only need `torch` and `torchvision` as pip packages (model downloads automatically)
52	- If it's a pip-installable package: use `MPyReq.pipPackage()`
53	- If it's a non-packaged git repo: use `MPyReq.gitrepo()` + `MPyReq.requirementTextFile()` if a `requirements.txt` exists
54	- If it needs `pip install git+<url>`: use `MPyReq.pipPackage("git+<url>", Name="<ProjectName>")`
55	- Additional dependencies — any extra pip packages needed (e.g., `torch`, `torchvision`, etc.)
56	- Model weights — determine how weights are loaded:
57	- `torch.hub.load()` — weights download automatically, no `MPyReq.weights()` needed
58	- Direct URL download — use `MPyReq.weights()` with the checkpoint URL
59	- HuggingFace `.from_pretrained()` — weights download automatically via the library
60	- Inference example — locate the primary inference/prediction code in the README or example scripts
61	- Preprocessing requirements — check if the model requires specific input normalization (e.g., ImageNet mean/std), resizing, or center cropping
62
63	## Step 2: Generate the MPyReq Setup Script
64
65	Create a MATLAB `.m` file that sets up the Python environment. Follow these patterns from the demo files:
66
67	### MANDATORY: Installation folder setup
68	Every generated script MUST begin with `MPyReq.setInstallFolder()`. This tells MPyReq where to download Python, packages, and model weights. Without this, MPyReq will show a GUI dialog which blocks non-interactive execution. Also include `MPyReq.autoAcceptDownloadPrompts(true)` to avoid interactive confirmation prompts.
69
70	```matlab
71	% Set installation folder (SSD recommended, ~15+ GB free space)
72	% Change this path to a suitable location on your machine
73	MPyReq.set

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matlab-integrate-pytorch-vision

815stars105forks

View on GitHub

How to install matlab-integrate-pytorch-vision?

matlab/matlab-agentic-toolkit/matlab-integrate-pytorch-vision

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-integrate-pytorch-vision

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# MPyReq MATLAB Interface Builder
2
3	Build a MATLAB interface to a Python/PyTorch model repository using the MPyReq framework.
4
5	## When to Use
6
7	- User asks to interface MATLAB with a Python image processing or computer vision model (segmentation, depth estimation, object detection, image generation, super-resolution, pose estimation, optical flow, salient object detection, etc.)
8	- User provides a GitHub repository URL for a vision/image model and wants to call it from MATLAB
9	- User asks to "create an MPyReq wrapper" or "MPyReq demo" for an image/CV model
10	- User wants to run a pip-installable vision model library (e.g., Cellpose, SAM2, Depth-Pro, BiRefNet, StarDist) from MATLAB
11
12	## When Not to Use
13
14	- General-purpose Python-MATLAB interfacing (no vision/image model involved)
15	- Non-vision models: NLP, audio, tabular, reinforcement learning, time-series
16	- Model deployment, containerization, or inference servers
17	- Pure MATLAB image processing workflows with no Python dependency
18	- Creating Python code (this skill creates MATLAB code that calls Python)
19
20	## Prerequisites: MPyReq on the MATLAB Path
21
22	Before generating any demo script, verify that MPyReq is available. Run `which MPyReq` via the MATLAB MCP server (if available) or ask the user to confirm.
23
24	### If MPyReq is NOT on the MATLAB path:
25
26	1. Download MPyReq from the MATLAB File Exchange:
27	https://mathworks.com/matlabcentral/fileexchange/182230-matlab-based-python-requirements-manager
28	2. Install it — either:
29	- Open the downloaded `.mltbx` file in MATLAB (double-click), which installs it as a MATLAB Add-On automatically, or
30	- Extract the files and add the folder containing `MPyReq.m` to the MATLAB path:
31	```matlab
32	addpath("/path/to/mpyreq");
33	savepath; % persist across sessions
34	```
35	3. Verify by running `which MPyReq` in MATLAB — it should return the path to `MPyReq.m`.
36
37	Do not proceed with demo generation until MPyReq is confirmed on the path.
38
39	## Input
40
41	Ask the user for:
42	1. GitHub repository URL — the Python model repository to interface with
43	2. What the model does (optional) — to help identify the right inference example
44
45	## Step 1: Analyze the Repository
46
47	Fetch and analyze the GitHub repository to determine:
48
49	- Python version requirement — check `setup.py`, `setup.cfg`, `pyproject.toml`, or README for the required Python version. Default to `"3.12"` if not specified. Use `"3.11"` if the project needs older compatibility.
50	- Installation method — determine how the project is installed:
51	- If it uses `torch.hub.load()`: only need `torch` and `torchvision` as pip packages (model downloads automatically)
52	- If it's a pip-installable package: use `MPyReq.pipPackage()`
53	- If it's a non-packaged git repo: use `MPyReq.gitrepo()` + `MPyReq.requirementTextFile()` if a `requirements.txt` exists
54	- If it needs `pip install git+<url>`: use `MPyReq.pipPackage("git+<url>", Name="<ProjectName>")`
55	- Additional dependencies — any extra pip packages needed (e.g., `torch`, `torchvision`, etc.)
56	- Model weights — determine how weights are loaded:
57	- `torch.hub.load()` — weights download automatically, no `MPyReq.weights()` needed
58	- Direct URL download — use `MPyReq.weights()` with the checkpoint URL
59	- HuggingFace `.from_pretrained()` — weights download automatically via the library
60	- Inference example — locate the primary inference/prediction code in the README or example scripts
61	- Preprocessing requirements — check if the model requires specific input normalization (e.g., ImageNet mean/std), resizing, or center cropping
62
63	## Step 2: Generate the MPyReq Setup Script
64
65	Create a MATLAB `.m` file that sets up the Python environment. Follow these patterns from the demo files:
66
67	### MANDATORY: Installation folder setup
68	Every generated script MUST begin with `MPyReq.setInstallFolder()`. This tells MPyReq where to download Python, packages, and model weights. Without this, MPyReq will show a GUI dialog which blocks non-interactive execution. Also include `MPyReq.autoAcceptDownloadPrompts(true)` to avoid interactive confirmation prompts.
69
70	```matlab
71	% Set installation folder (SSD recommended, ~15+ GB free space)
72	% Change this path to a suitable location on your machine
73	MPyReq.set

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6 picks

Type