IMSIS
==================================================
Introduction
------------
IMSIS is an open source image analysis package in python.
The library contains functions to quickly create simple dialog based scripts, fast image processing sequences and perform basic image analysis.
The package relies on powerful libraries such as Numpy, Scipy, OpenCV and QT.
Typical applications would be:
- Dialog based scripts where syntax editing is replaced by runtime dialogs (input dialogs, warnings, property lists, radio button lists, text dialogs etc.)
- Dialog based feature selection (spots, lines, rectangles etc.)
- Fast multi image viewing with or without histograms
- Image batch processing (sharpening, denoising, morphological operations, color operations, image conversion etc.)
- Image analysis (finding unique features, line profiles, counting features, image alignment, image comparisons, image sharpness)
- Image filtering in Fourier space
- Fast image processing for machine learning data
Requirements
------------
IMSIS requires the following packages
- numpy 1.13.3
- scipy 1.1.0
- matplotlib 2.0.0
- opencv_python 4.0.0.21
- Pillow 6.2.0
- PyQt5 5.15
Requirements documentation
--------------------------
IMSIS Documentation building requires the following additional packages
- sphinx
- sphinx_rtd_theme
- rinohtype
Documentation can be automatically generated with python build_docs.py
Installation
------------
python setup.py sdist bdist_wheel
pip install dist\imsis-1.0-py3-none-any.whl
Example
-------
A simple example of loading and displaying an image
```
import imsis as ims
fn = r".\images\bberry.jpg"
im_blueberry = ims.Image.load(fn)
ims.View.plot(im_blueberry,title="Blueberry",window_title="Plot")
```
A list of examples of every method implemented can be found in the examples folder.
Some more can be found below:
Animated transitions
--------------------
<img src="./figures/animated_transitions.jpg" width="300">
Image blending
--------------
<img src="./figures/blending.jpg" width="300">
Image denoising
---------------
<img src="./figures/denoise.jpg" width="300">
Interactive user dialogs
------------------------
<img src="./figures/dialogs.jpg" width="300">
Measurements on images
----------------------
<img src="./figures/measurements_on_image.jpg" width="300">
Feature descriptor Matching
---------------------------
<img src="./figures/feature_descriptor_matching.jpg" width="300">
Find Brightest Spot
-------------------
<img src="./figures/find_brightest_spot.jpg" width="300">
Find Edges
----------
<img src="./figures/find_edges.jpg" width="300">
Find Feature
------------
<img src="./figures/find_feature.jpg" width="300">
Frequency domain image filtering
--------------------------------
<img src="./figures/frequency_domain_filtering.jpg" width="300">
Histogram operations
--------------------
<img src="./figures/histogram_operations.jpg" width="300">
HSV color channel editing
-------------------------
<img src="./figures/hsv_Channels.jpg" width="300">
K-means clustering
------------------
<img src="./figures/k_meansclustering.jpg" width="300">
Image masking
-------------
<img src="./figures/masking.jpg" width="300">
Raw data
{
"_id": null,
"home_page": "https://github.com/rengezri",
"name": "IMSIS",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "image analysis,dialogs,batch processing",
"author": "rengezri",
"author_email": "rengezri <author@example.com>",
"download_url": "https://files.pythonhosted.org/packages/30/80/caa1a60cbea7f45257ce4b36184f4ad36a082c9d111332eb0cd564dc22e1/IMSIS-1.1.7.tar.gz",
"platform": null,
"description": "IMSIS\r\n==================================================\r\n\r\nIntroduction\r\n------------\r\n\r\nIMSIS is an open source image analysis package in python.\r\nThe library contains functions to quickly create simple dialog based scripts, fast image processing sequences and perform basic image analysis.\r\nThe package relies on powerful libraries such as Numpy, Scipy, OpenCV and QT.\r\n\r\nTypical applications would be:\r\n\r\n- Dialog based scripts where syntax editing is replaced by runtime dialogs (input dialogs, warnings, property lists, radio button lists, text dialogs etc.)\r\n- Dialog based feature selection (spots, lines, rectangles etc.)\r\n- Fast multi image viewing with or without histograms\r\n- Image batch processing (sharpening, denoising, morphological operations, color operations, image conversion etc.)\r\n- Image analysis (finding unique features, line profiles, counting features, image alignment, image comparisons, image sharpness)\r\n- Image filtering in Fourier space\r\n- Fast image processing for machine learning data\r\n\r\nRequirements\r\n------------\r\n\r\nIMSIS requires the following packages\r\n\r\n- numpy 1.13.3\r\n- scipy 1.1.0\r\n- matplotlib 2.0.0\r\n- opencv_python 4.0.0.21\r\n- Pillow 6.2.0\r\n- PyQt5 5.15\r\n\r\nRequirements documentation\r\n--------------------------\r\n\r\nIMSIS Documentation building requires the following additional packages\r\n\r\n- sphinx\r\n- sphinx_rtd_theme\r\n- rinohtype\r\n\r\nDocumentation can be automatically generated with python build_docs.py\r\n\r\nInstallation\r\n------------\r\n\r\npython setup.py sdist bdist_wheel\r\n\r\npip install dist\\imsis-1.0-py3-none-any.whl\r\n\r\nExample\r\n-------\r\n\r\nA simple example of loading and displaying an image\r\n\r\n```\r\nimport imsis as ims\r\n\r\nfn = r\".\\images\\bberry.jpg\"\r\nim_blueberry = ims.Image.load(fn)\r\n\r\nims.View.plot(im_blueberry,title=\"Blueberry\",window_title=\"Plot\")\r\n```\r\n\r\nA list of examples of every method implemented can be found in the examples folder.\r\n\r\nSome more can be found below:\r\n\r\n\r\nAnimated transitions\r\n--------------------\r\n<img src=\"./figures/animated_transitions.jpg\" width=\"300\">\r\n\r\nImage blending\r\n--------------\r\n<img src=\"./figures/blending.jpg\" width=\"300\">\r\n\r\nImage denoising\r\n---------------\r\n<img src=\"./figures/denoise.jpg\" width=\"300\">\r\n\r\nInteractive user dialogs\r\n------------------------\r\n<img src=\"./figures/dialogs.jpg\" width=\"300\">\r\n\r\nMeasurements on images\r\n----------------------\r\n<img src=\"./figures/measurements_on_image.jpg\" width=\"300\">\r\n\r\nFeature descriptor Matching\r\n---------------------------\r\n<img src=\"./figures/feature_descriptor_matching.jpg\" width=\"300\">\r\n\r\nFind Brightest Spot\r\n-------------------\r\n<img src=\"./figures/find_brightest_spot.jpg\" width=\"300\">\r\n\r\nFind Edges\r\n----------\r\n<img src=\"./figures/find_edges.jpg\" width=\"300\">\r\n\r\nFind Feature\r\n------------\r\n<img src=\"./figures/find_feature.jpg\" width=\"300\">\r\n\r\nFrequency domain image filtering\r\n--------------------------------\r\n<img src=\"./figures/frequency_domain_filtering.jpg\" width=\"300\">\r\n\r\nHistogram operations\r\n--------------------\r\n<img src=\"./figures/histogram_operations.jpg\" width=\"300\">\r\n\r\nHSV color channel editing\r\n-------------------------\r\n<img src=\"./figures/hsv_Channels.jpg\" width=\"300\">\r\n\r\nK-means clustering\r\n------------------\r\n<img src=\"./figures/k_meansclustering.jpg\" width=\"300\">\r\n\r\nImage masking\r\n-------------\r\n<img src=\"./figures/masking.jpg\" width=\"300\">\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "IMSIS",
"version": "1.1.7",
"project_urls": {
"Bug Tracker": "https://github.com/rengezri/imsis/issues",
"Download": "https://github.com/rengezri/imsis/archive/refs/tags/v1.1.7.tar.gz",
"Homepage": "https://github.com/rengezri/imsis"
},
"split_keywords": [
"image analysis",
"dialogs",
"batch processing"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e82fe72a070dc4bc7d6ed62e884cae79c5f331944ecadb29a7e2950a45d00d47",
"md5": "59f5609eba1f55a94f14f5a3f50ce872",
"sha256": "c5843fe70c30c486d7be660b13ed322f87f420658830df5c86a67a9603931afb"
},
"downloads": -1,
"filename": "IMSIS-1.1.7-py3-none-any.whl",
"has_sig": false,
"md5_digest": "59f5609eba1f55a94f14f5a3f50ce872",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 73858,
"upload_time": "2024-02-28T23:09:26",
"upload_time_iso_8601": "2024-02-28T23:09:26.723512Z",
"url": "https://files.pythonhosted.org/packages/e8/2f/e72a070dc4bc7d6ed62e884cae79c5f331944ecadb29a7e2950a45d00d47/IMSIS-1.1.7-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "3080caa1a60cbea7f45257ce4b36184f4ad36a082c9d111332eb0cd564dc22e1",
"md5": "1564126c811111a37f798a48f93d80f6",
"sha256": "fbb6e53aacfbf748d921108f1a21082c96f0d6b86ccb8b13d3e8e63a55bc392c"
},
"downloads": -1,
"filename": "IMSIS-1.1.7.tar.gz",
"has_sig": false,
"md5_digest": "1564126c811111a37f798a48f93d80f6",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 70806,
"upload_time": "2024-02-28T23:08:59",
"upload_time_iso_8601": "2024-02-28T23:08:59.861154Z",
"url": "https://files.pythonhosted.org/packages/30/80/caa1a60cbea7f45257ce4b36184f4ad36a082c9d111332eb0cd564dc22e1/IMSIS-1.1.7.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-02-28 23:08:59",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "rengezri",
"github_project": "imsis",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "imsis"
}
JSON
