matlab-process-large-images

Name: matlab-process-large-images
Author: matlab

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How to install matlab-process-large-images?

matlab/matlab-agentic-toolkit/matlab-process-large-images

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-process-large-images

Installs into the current project.

Summary

Patterns for using blockedImage to process large images, harness parallel compute for image processing, and write custom adapters. Use when writing code that creates, processes, or visualizes blockedImage objects, when implementing images.blocked.Adapter subclasses, or when a use

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SKILL.md

1	## When to Use
2	- Writing code that creates, processes, or visualizes `blockedImage` objects
3	- Implementing `images.blocked.Adapter` subclasses for custom file formats
4	- User needs help with large image data that doesn't fit in memory
5	- User wants to leverage parallel computing for image processing tasks
6	- Working with whole-slide images, geospatial rasters, 3D microscopy volumes, or tiled mosaics
7	- Any time a user is working with a TIFF file — load this skill and check the file first with `imfinfo`. Use `blockedImage` if the file is large (dimensions > 10,000 pixels in any axis), has multiple channels beyond RGB, or contains multiple IFDs (could be pyramid levels, time series, or Z-stack slices)
8
9	## When NOT to Use
10	- Image fits comfortably in memory and no parallel processing is needed — use standard `imread`/`imwrite` workflows
11	- General image display without `blockedImage` — use the `matlab-display-image` skill instead
12	- Deep learning model architecture or training loop design — this skill covers data preparation with `blockedImageDatastore`, not network design
13
14	# Large Image Processing with blockedImage
15
16	Reference files (read on demand when the topic comes up):
17	- `references/visualization.md` — imageshow, overlays, synced views, overview+detail, annotations
18	- `references/training-and-labeling.md` — Image Labeler, labeled blockedImages, training datastores, DL inference
19	- `references/3d-volumes.md` — 3D blocked images, 3D inference, 3D training data
20	- `references/geospatial.md` — GeoTIFF, shapefiles, coordinate systems, spatial referencing
21	- `references/data-discovery.md` — 7-step data discovery workflow, custom adapters, format conversion adapter selection, virtual composite adapters, adapter performance tips
22	- `references/specialized-workflows.md` — writing block-by-block, coordinate systems, mask-based block selection, ExtraImages for pixel-level masking, in-place resume
23	- `references/whole-slide-images.md` — importing WSI files via OpenSlide/Bio-Formats, choosing between libraries, display, downstream processing (segmentation, classification)
24
25	## When to use blockedImage
26	blockedImage is not only for data that exceeds RAM. Even when the data fits in memory, wrapping it as a blockedImage lets you harness a parallel cluster: each worker can independently read and write different blocks, enabling parallel partial reads and parallel writes into separate file blocks.
27
28	Always ask the user what their intent with the data is. Ask about visualization, processing, DL training, archival, and whether they want to leverage parallel compute — even if the data fits in RAM.
29
30	## When the user points you at a directory or multi-file dataset
31	ALWAYS read `references/data-discovery.md` and follow its 7-step workflow before writing any code. The workflow covers: inspecting file layout, parsing naming conventions, understanding user intent, choosing between single blockedImage vs. collection, assessing random access capabilities, checking for gaps/overlaps, and recommending an approach (built-in adapter, format conversion, or custom adapter).
32
33	## Construction
34	```matlab
35	% Read-only (auto-selects adapter; reads only metadata, fast)
36	bim = blockedImage("large_image.tif");
37
38	% Multiple images at once
39	[bim1, bim2] = blockedImage(["img1.tif", "img2.tif"]);
40
41	% Writable
42	bim = blockedImage(dest, imageSize, blockSize, initVal, Mode="w");
43
44	% Override adapter
45	bim = blockedImage(source, Adapter=myAdapter);
46	```
47
48	## BlockSize
49	- Defaults to a factor of `IOBlockSize` (adapter's native read unit), targeting ~1024 or the minimum dimension size.
50	- The object's `BlockSize` is the default for all downstream processing and visualization.
51	- Override per-operation via the `BlockSize` name-value argument (e.g., when a DL network requires a fixed input size).
52	- For best performance, keep `BlockSize` as a multiple of `IOBlockSize` and as large as practical.
53
54	## Reading Data
55	- `getBlock(bim, blocksub)` — read one block by block subscript. Returns `[data, blockinfo]`.
56	- `getRegion(bim, pixelStart, pixelEnd)` — read an arbitrary pixel region.
57	- `gather(bim)` — load entire image into memory (only if it fits in RAM). Use `Level` to select resolution.
58
59	## Processing with `apply()`
60	`apply(bim, @fcn)` calls `fcn` on each block one at a time. Scales to arbitrarily large data. Supports `UseParallel=true` for cluster execution.
61
62	Key name-value arguments: `BlockSize`,

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matlab-process-large-images

815stars105forks

View on GitHub

How to install matlab-process-large-images?

matlab/matlab-agentic-toolkit/matlab-process-large-images

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-process-large-images

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## When to Use
2	- Writing code that creates, processes, or visualizes `blockedImage` objects
3	- Implementing `images.blocked.Adapter` subclasses for custom file formats
4	- User needs help with large image data that doesn't fit in memory
5	- User wants to leverage parallel computing for image processing tasks
6	- Working with whole-slide images, geospatial rasters, 3D microscopy volumes, or tiled mosaics
7	- Any time a user is working with a TIFF file — load this skill and check the file first with `imfinfo`. Use `blockedImage` if the file is large (dimensions > 10,000 pixels in any axis), has multiple channels beyond RGB, or contains multiple IFDs (could be pyramid levels, time series, or Z-stack slices)
8
9	## When NOT to Use
10	- Image fits comfortably in memory and no parallel processing is needed — use standard `imread`/`imwrite` workflows
11	- General image display without `blockedImage` — use the `matlab-display-image` skill instead
12	- Deep learning model architecture or training loop design — this skill covers data preparation with `blockedImageDatastore`, not network design
13
14	# Large Image Processing with blockedImage
15
16	Reference files (read on demand when the topic comes up):
17	- `references/visualization.md` — imageshow, overlays, synced views, overview+detail, annotations
18	- `references/training-and-labeling.md` — Image Labeler, labeled blockedImages, training datastores, DL inference
19	- `references/3d-volumes.md` — 3D blocked images, 3D inference, 3D training data
20	- `references/geospatial.md` — GeoTIFF, shapefiles, coordinate systems, spatial referencing
21	- `references/data-discovery.md` — 7-step data discovery workflow, custom adapters, format conversion adapter selection, virtual composite adapters, adapter performance tips
22	- `references/specialized-workflows.md` — writing block-by-block, coordinate systems, mask-based block selection, ExtraImages for pixel-level masking, in-place resume
23	- `references/whole-slide-images.md` — importing WSI files via OpenSlide/Bio-Formats, choosing between libraries, display, downstream processing (segmentation, classification)
24
25	## When to use blockedImage
26	blockedImage is not only for data that exceeds RAM. Even when the data fits in memory, wrapping it as a blockedImage lets you harness a parallel cluster: each worker can independently read and write different blocks, enabling parallel partial reads and parallel writes into separate file blocks.
27
28	Always ask the user what their intent with the data is. Ask about visualization, processing, DL training, archival, and whether they want to leverage parallel compute — even if the data fits in RAM.
29
30	## When the user points you at a directory or multi-file dataset
31	ALWAYS read `references/data-discovery.md` and follow its 7-step workflow before writing any code. The workflow covers: inspecting file layout, parsing naming conventions, understanding user intent, choosing between single blockedImage vs. collection, assessing random access capabilities, checking for gaps/overlaps, and recommending an approach (built-in adapter, format conversion, or custom adapter).
32
33	## Construction
34	```matlab
35	% Read-only (auto-selects adapter; reads only metadata, fast)
36	bim = blockedImage("large_image.tif");
37
38	% Multiple images at once
39	[bim1, bim2] = blockedImage(["img1.tif", "img2.tif"]);
40
41	% Writable
42	bim = blockedImage(dest, imageSize, blockSize, initVal, Mode="w");
43
44	% Override adapter
45	bim = blockedImage(source, Adapter=myAdapter);
46	```
47
48	## BlockSize
49	- Defaults to a factor of `IOBlockSize` (adapter's native read unit), targeting ~1024 or the minimum dimension size.
50	- The object's `BlockSize` is the default for all downstream processing and visualization.
51	- Override per-operation via the `BlockSize` name-value argument (e.g., when a DL network requires a fixed input size).
52	- For best performance, keep `BlockSize` as a multiple of `IOBlockSize` and as large as practical.
53
54	## Reading Data
55	- `getBlock(bim, blocksub)` — read one block by block subscript. Returns `[data, blockinfo]`.
56	- `getRegion(bim, pixelStart, pixelEnd)` — read an arbitrary pixel region.
57	- `gather(bim)` — load entire image into memory (only if it fits in RAM). Use `Level` to select resolution.
58
59	## Processing with `apply()`
60	`apply(bim, @fcn)` calls `fcn` on each block one at a time. Scales to arbitrarily large data. Supports `UseParallel=true` for cluster execution.
61
62	Key name-value arguments: `BlockSize`,

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