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Hi JHPJHP!

You have at least one (it's me ) who want to see more examples. Please add its to the package.
You le_stitching.pb example is very impressive! I will take photos over the weekend to experiment more.

Good luck!

Hi AAT,

I spent the time and updated the new example to conform to the existing standards - I hope you have some fun with it.

Thanks for responding, your appreciation makes all the effort worth while.

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Updated:
- renamed 1 example
-- le_stitching.pb to le_stitching_1.pb
- added 1 example
-- le_stitching_2.pb: stitches images together; based on the SIFT (Scale Invariant Feature Transform) algorithm
- added 14 images

Also includes some minor updates to other examples - not worth documenting.

Unlike the first stitching example which demonstrated each stage of the SIFT process, this example takes a group of images (2, 3, 4 and 5 exclusively) and stitches them together directly.
- when the variance between multiple image perspectives is high, there is a greater degree of distortion
- another thing to be aware of is the order of images, which need to be loaded from right to left

5 image stitch: I cut a panoramic image into 5 overlapping images, the resulting stitched images are near prefect due to their identical perspectives
3 image stitch: you should see in the third image (right side), that the size of the double doors is distorted compared with the original image
- with three or more images, and depending on their perspective angles, the resulting output can be a radically distorted image
-- fixing this should be fairly simple with a few changes to the code

Some setting to be aware of (Feature Detection and Description):
- #PARAMS_EXTENDED
-- 0 means that the basic descriptors (64 elements each) shall be computed
-- 1 means that the extended descriptors (128 elements each) shall be computed
- #PARAMS_THRESHOLD
-- threshold for the keypoint detector
--- a good default value could be from 300 to 500, depending from the image contrast

Some setting to be aware of (Feature Detection and Description):

Just a minor error on the link that causes a 404
feature_detection.html)
just remove the extra ) at the end of the link and its ok.
Thanks for the link.

Hi yrreti,

The link is now fixed... I wondered if anyone was reading the posts.

Thank you.

I'm reading the posts. Good job you have been doing here.

Hi again Thunder93,

Thanks for the kudos, your acknowledgement is very much appreciated.

Cheers!

Updated:
- updated 2 examples
-- le_stitching_1.pb: stitch together 2 images in stages; based on the SIFT (Scale Invariant Feature Transform) algorithm
-- le_stitching_2.pb: stitch together 2, 3, 4, or 5 images; based on the SIFT (Scale Invariant Feature Transform) algorithm
- deleted 4 images
- replaced 7 images

Added a Procedure to better frame the stitched image, including minor improvements and some bug fixes.

Also includes some minor updates to other examples - not worth documenting.

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Updated:
- added 1 example
-- cv_retro.pb: retro-style photo effect done by adding noise to the luminance channel and reducing intensity of the chroma channels
- added 2 images

Original Code: image.cpp found in the current version of OpenCV.

NB*: Helpful information: Transform CvArr, CvMat and IplImage.

Thanks again for your work ... it's amazing to see what you did and how it extends PureBasic. Keep on

Hi Morty,

Thank you for your kind words - I really appreciate the acknowledgement.

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Updated:
- added 1 example
-- cv_calcbackproject.pb: calculating the back projection of a histogram, frequent density values are displayed as bright pixels

Updated:
- updated Functions
- added 2 examples
-- cv_cam_pixelate.pb: pixelate a webcam stream by averaging NxN block regions, and applying the color values to grid squares
-- cv_imagedots.pb: creates an image by averaging 10x10 block regions, and applying the color values to a dot matrix

cv_cam_pixelate.pb:

hi JHPJHP,

first at all, thanks again for all your nice work,
second, do you ever sleep ?

marc,

Hi marc_256,

You're welcome, and thank you for the acknowledgement. As for your question on whether I sleep or not...

Sleep, those little slices of death; Oh how I loathe them.
~Edgar Allan Poe

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Updated:
- added 3 examples
-- cv_logpolar.pb: remaps an image to log-polar space
-- le_delaunay_1.pb: manual construction of delaunay triangulation
-- le_delaunay_2.pb: random construction of delaunay triangulation
- changed the naming convention from 1a, 1b, etc. to 1, 2, etc.

le_delaunay_*.pb: based on script found here.

Updated:
- added Constants, Structures, Functions
- added 2 examples
-- cv_sepia.pb: convert a color image to sepia
-- le_mov_bgstatmodel.pb: using a Gaussian mixture-based background/foreground detection algorithm, foreground objects are highlighted by comparing the current frame with a static frame
- added 1 movie file: walking.avi

NB*: New Functions are from the legacy DLL - all others have been declared.

Even though the functions used in le_mov_bgstatmodel.pb are from the legacy DLL, there are numerous object-detection possibilities; I may play around with this some more.

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Updated:
- renamed 1 example
-- le_mov_bgstatmodel.pb to le_mov_bgstatmodel_1.pb
- added 1 example
-- le_mov_bgstatmodel_2.pb: using a background/foreground detection algorithm, foreground objects are highlighted/removed by comparing the current frame with a static frame

I found a bug in the previous example, while fixing that problem I decided to add an additional example using the BGStatModel functions.

Cheers!

Hi, JHPJHP

Thanks for your greate new examples!
The example le_mov_bgstatmodel_1.pb is very interesting! I saw it but I didn't knew how to translate it into PureBasic. Great!

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I had a need to keep the images processed with opencv in a database (in BLOB). The code with saving the image into a temporary file works too slow, so i found the solution how to make conversion in the memory. For example: P.S. The code works fine but jpeg compression level is too high. I don't know how to regulate the compression level.

Good luck!

Hi AAT,

You're welcome, I'm glad you're enjoying the examples.

Conversion in memory is definitely the way to go, I wrote a Comic Book reader using the same functions because of the speed improvement over file access.

jpeg compression level is too high. I don't know how to regulate the compression level

It's done the same way as with the cvSaveImage Function, the new example should give you a clearer picture (ignore the pun) .

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Updated:
- updated 1 Function
- added Procedures
- added 1 example
-- cv_cam_pbdatabase.pb: captures and saves webcam frames to a database, displaying a thumbnail version of the image loaded from the database

I usually like to work with PostgreSQL, but that obviously wouldn't work in this case, so the database I'm using is SQLite - I'm impressed with the size and speed.

Creating a database entry: ImportImage(*image, name.s, extension.s = ".jpg")
- first parameter is an OpenCV image
- second parameter is part of the database entry name (string)
-- the rest of the name is automatically generated: dd_mm_yyyy_hh_mm_ss_fff (milliseconds used for fast captures)
-- this should allow for organizing/sorting the database
- third parameter is optional
- returns the database entry name (string)

Retrieving a database entry: GetDBImage(dbName.s)
- parameter is database entry name (string)
- returns an OpenCV image

Note:
- after pressing the SPACEBAR if the thumbnail image shows/changes, then the database update/retrieval is working properly.

NB*: Image compression is static, but can be changed from the Procedure: ImportImage().