Changelog

v0.1.39 (2024-06-01)

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Changes

Validate allowed keyword arguments.
List all allowed keyword arguments in the function specs.

v0.1.38 (2024-03-30)

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Fix

[videocapture] fixed an issue where invoking Evision.VideoCapture.waitAny/{1,2} reports a NIF error "cv::VideoCapture::waitAny not loaded".

v0.1.37 (2024-02-17)

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Changes

[nerves-build] use fwup v1.10.1
[precompiled] support armv6-linux-gnueabihf target
[precompiled] precompiled binaries are now built with Erlang/OTP NIF version 2.16, and they are compatible with NIF version 2.16 and later.
[model_zoo] use permanent URLs for all models.

Added

[experimental] support aarch64-windows-msvc target
[nerves-build] added rpi5 and srhub
[model_zoo] added PP-OCR V3 text detection models
[model_zoo] added image classification mobilenet v2 models

v0.1.36 (2024-02-16)

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Fix

Allow implicitly cast to Evision.Feature2D from the following types:
- Evision.AKAZE
- Evision.AffineFeature
- Evision.AgastFeatureDetector
- Evision.BRISK
- Evision.FastFeatureDetector
- Evision.GFTTDetector
- Evision.KAZE
- Evision.MSER
- Evision.ORB
- Evision.SimpleBlobDetector
- Evision.XFeatures2D.BEBLID
- Evision.XFeatures2D.BoostDesc
- Evision.XFeatures2D.BriefDescriptorExtractor
- Evision.XFeatures2D.DAISY
- Evision.XFeatures2D.FREAK
- Evision.XFeatures2D.LATCH
- Evision.XFeatures2D.LUCID
- Evision.XFeatures2D.MSDDetector
- Evision.XFeatures2D.StarDetector
- Evision.XFeatures2D.TBMR
- Evision.XFeatures2D.TEBLID
- Evision.XFeatures2D.VGG

Added

[experimental] Support compiling for iOS. Precompiled binaries are also available for iOS, but they are not tested yet, and they require a few more steps to use. Please see this guide for more information.

v0.1.35 (2024-02-14)

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Changed

Detect and use env var HTTP_PROXY and HTTPS_PROXY when downloading precompiled binaries.
Updated to OpenCV 4.9.0. Some APIs may have changed, please see OpenCV's release note for more information.
Use embedded :evision_windows_fix instead of :dll_loader_helper.
Updated CUDA versions for precompiled binaries. Now we only have EVISION_CUDA_VERSION=118 and EVISION_CUDA_VERSION=121.

v0.1.34 (2023-11-18)

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Changed

[compatibilities] Make it compatible with newer versions of Kino. Thanks for the contribution from @jonatanklosko.

v0.1.32 (2023-08-01)

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Changed

Updated to OpenCV 4.8.0. Some APIs have changed, please see OpenCV's release note for more information.
Using manylinux2014 to build precompiled binaries for x86_64-linux-gnu (w/ and w/o contrib, except for the cuda ones). This allows us to only require glibc version to be at least 2.17.

v0.1.31 (2023-04-17)

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Changed

[Evision.DNN] Added support for passing Nx.Tensor or Evision.Mat as the input argument of bboxes in

Evision.DNN.nmsBoxes/{4,5}

iex> Evision.DNN.nmsBoxes([{0,1,2,3}], [1], 0.4, 0.3)
[0]
iex> Evision.DNN.nmsBoxes(Nx.tensor([[0,1,2,3]]), [1], 0.4, 0.3)
[0]
iex> Evision.DNN.nmsBoxes(Evision.Mat.literal([[0,1,2,3]], :f64), [1], 0.4, 0.3)
[0]

Evision.DNN.nmsBoxesBatched/{5,6}

iex> Evision.DNN.nmsBoxesBatched([{0,1,2,3}], [1], [1], 0.4, 0.3)
[0]
iex> Evision.DNN.nmsBoxesBatched(Nx.tensor([[0,1,2,3]]), [1], [1], 0.4, 0.3)
[0]
iex> Evision.DNN.nmsBoxesBatched(Evision.Mat.literal([[0,1,2,3]], :f64), [1], [1], 0.4, 0.3)
[0]

Evision.DNN.softNMSBoxes/{4,5}

iex> Evision.DNN.softNMSBoxes([{0,1,2,3}], [1], 0.4, 0.3)
{[1.0], [0]}
iex> Evision.DNN.softNMSBoxes(Nx.tensor([[0,1,2,3]]), [1], 0.4, 0.3)
{[1.0], [0]}
iex> Evision.DNN.softNMSBoxes(Evision.Mat.literal([[0,1,2,3]], :s32), [1], 0.4, 0.3)
{[1.0], [0]}

v0.1.30 (2023-03-24)

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Fixed

[smartcell] Fixed outputBlob is embedded in a list for CRNN and MobileNetV1 models.

v0.1.29 (2023-03-13)

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Fixed

[py_src] Fixed incorrect typespec for Saclar. Thanks @tusqasi and @Nicd.
[smartcell] Fixed PPResnet based models.
[smartcell] Fixed invalid charset URLs as they were removed in the upstream repo. CRNN models URLs to commit 12817b80.

v0.1.28 (2023-01-25)

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Fixed

[py_src] ArgInfo.has_default is now set to true if a.defval is f"{a.tp}()". Fixed #174.
[ci-win-precompile-core] Removed the line that deletes the priv/x64 directory. It should be removed in the last version because we will add priv/x64 to the DLL search path instead of copying all opencv dll files to the priv directory.

v0.1.27 (2023-01-23)

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Breaking Changes

[Evision.Constant] Constant values are all relocated to the Evision.Constant module. To use them, either do
```
Evision.Constant.cv_IMREAD_ANY()
```
or
```
import Evision.Constant
cv_IMREAD_ANY()
```
[Evision] Evision.__enabled_modules__/0 => Evision.enabled_modules/0. Result will now be computed using HAVE_OPENCV_{MODULE_NAME} macros.

Fixed

[Evision.Mat] fixed Evision.Mat.update_roi/3.
[py_src] fix incorrect typespecs.
[py_src] VideoCaptureAPIs should be a single number instead of a list of number.

Changed

[c_src] check if we can use existing atom from enif_make_existing_atom before calling to enif_make_atom.

Added

OpenCV contrib modules.
- For users who prefer using precompiled binaries, almost all modules in opencv_contrib is included in precompiled archives (except for CUDA related ones, please see the next bullet point), and this will be the new default for precompiled users.
  
  However, we do provide precompiled binaries that only include core modules. Please see the next paragraph.
- For all users, to use core modules only, please set env var EVISION_ENABLE_CONTRIB=false.
CUDA 11 + cudnn 8 support with precompiled binaries.

Note that CUDA 11 and cudnn 8 runtime libraries/dll files are not included in the precompiled archive.

Please following the installation guide on NVIDIA's website.
- For precompiled binaries users, please set EVISION_ENABLE_CUDA to true. Besides that, there are 3 CUDA versions to choose:
  - EVISION_CUDA_VERSION=111
  - EVISION_CUDA_VERSION=114
  - EVISION_CUDA_VERSION=118
  Please choose the one that matches your local CUDA versions, and set this EVISION_CUDA_VERSION env var correspondingly.
- For users who prefer compiling from source, you'll only need to set EVISION_ENABLE_CUDA to true, and OpenCV will detect and use (if possible) your local CUDA/cudnn runtime.
Lastly, if EVISION_ENABLE_CUDA is true while EVISION_ENABLE_CONTRIB is false, CUDA related modules will not be compiled/downloaded.

v0.1.26 (2023-01-21)

Please use v0.1.27 as precompilation binaries for targets x86_64-windows-msvc-contrib-cuda* and x86_64-linux-gnu-contrib-cuda* were incorrect.

v0.1.25 (2022-12-18)

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Fixed

[smartcell] fixed a typo in SFace.

v0.1.23/v0.1.24 (2022-12-17)

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Fixed

[smartcell] fixed charset loading when initialising FP16/INT8 CRNN models. #144
[smartcell] fixed OpenCL target label.

Changed

[smartcell] register the model zoo smart cell (Evision.SmartCell.Zoo) on starting. Thanks to @josevalim.
[smartcell] make :kino and :progress_bar optional dependencies.
[ci] added one more step to make sure it compiles without optional deps. Thanks to @josevalim.
[smartcell] hide all FP16 models of CRNN because they were not supported until opencv/opencv #22337, which was after the release date of OpenCV 4.6.0.

See more on opencv/opencv#18735 (comment).
[smartcell] hide CRNN CH (INT8) and CRNN EN (INT8) because OpenCV 4.6.0 seemed to have problems loading/parsing them even with the demo.py script in the official opencv_zoo repo.

v0.1.22 (2022-12-16)

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Added

[smartcell] OpenCV Model Zoo. Evision.SmartCell.Zoo

v0.1.21 (2022-11-25)

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Fixed

[py_src] fixed functions in dnn that return *this.

For this part, this original code (as in python-opencv) would case a new object to be allocated in C++ like

TextDetectionModel_DB retval;
retval = self.setSomeValue(...)
return pyopencv_from(retval);

Noticing the address of the object has changed (because it's a new one) after calling m.setBinaryThreshold.

>>> import cv2'
>>> m = cv2.dnn_TextDetectionModel_DB("DB_IC15_resnet18.onnx")
>>> m
< cv2.dnn.TextDetectionModel_DB 0x1064cf210>
>>> m.setBinaryThreshold(0.5)
< cv2.dnn.TextDetectionModel_DB 0x11ecda7f0>

v0.1.20 (2022-11-24)

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Fixed

[Precompiled] fixed incorrect checksum for x86_64-linux-gnu.

v0.1.19 (2022-11-14)

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Changed

[py_src/c_src] Added has_default field to ArgInfo.

v0.1.18 (2022-11-12)

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Fixes

[precompile] Fixed Mix.Tasks.Compile.EvisionPrecompiled.read_checksum_map/1
[py_src] Fixed code generation for derived classes in namespace cv::dnn
[test] added test for Evision.DNN.DetectionModel.

v0.1.17 (2022-11-11)

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Fixes

[py_src] Fixed a code generation bug when all the input arguments of a function are optional.

Changed

[example] Req.get! should only raise on 4xx and 5xx. Thanks @wojtekmach

Added

[example] Added two examples:
- find and draw contours in an image.
- extracting sudoku puzzle from an image.
[erlang] Structurised/recordized all #references that have their own Erlang module.
[erlang] Download precompiled binaries using evision_precompiled.erl.
[erlang] Generate typespecs.

v0.1.16 (2022-10-30)

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Fixes

[deps] :kino will be an optional dependency, if we use if before defmodule. This reverts the changes in in v0.1.15.

Thanks @josevalim for helping me figuring out why using if before defmodule would solve the problem. More details can be found here.

Changes

[config.exs] Added configurable parameters related to rendering Evision.Mat in Kino. (They are optional and can also be adjusted in runtime)
- config :evision, kino_render_image_encoding: :png
- config :evision, kino_render_image_max_size: {8192, 8192}
- config :evision, kino_render_tab_order: [:image, :raw, :numerical]

Added

[Evision.Mat] Added a few functions related to Kino.Render

Function	Description
`Evision.Mat.kino_render_tab_order/0`	Get preferred order of Kino.Layout tabs for `Evision.Mat` in Livebook.
`Evision.Mat.set_kino_render_tab_order/1`	Set preferred order of Kino.Layout tabs for `Evision.Mat` in Livebook.
`Evision.Mat.kino_render_image_max_size/0`	Get the maximum allowed image size to render in Kino.
`Evision.Mat.set_kino_render_image_max_size/1`	Set the maximum allowed image size to render in Kino.
`Evision.Mat.kino_render_image_encoding/0`	Get preferred image encoding when rendering in Kino.
`Evision.Mat.set_kino_render_image_encoding/1`	Set preferred image encoding when rendering in Kino.

v0.1.15 (2022-10-26)

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Changes

[mix compile] Suppress logs if evision.so is already presented when compiling from source.
[Precompile] Added precompile target aarch64-windows-msvc.

Fixes

[deps] :kino should be a required dependency

v0.1.14 (2022-10-22)

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Breaking Changes

[Precompile] Linux: remove GTK support in precompiled binaries. (This change only affects users on Linux.)

This means functions in the Evision.HighGui module will return error if you are using precompiled binaries. This follows the convention in opencv-python.

Workarounds for this:
1. compile evision from source so that OpenCV will try to use the GUI backends they support on your system.
2. use Evision.Wx. still in development, but basic functions like imshow/2 are available. However, it requires Erlang to be compiled with wxWidgets.
3. use Livebook with :kino >= 0.7. evision has built-in support for Kino.Render which can automatically give a visualised result in Livebook. This requires :kino >= 0.7.

[Evision.Nx] Module Evision.Nx is now removed. Functions in Evision.Nx were moved to Evision.Mat in v0.1.13. Many thanks to @zacky1972 and @josevalim for their contributions to this module in very early days of the development.

Old	New
`Evision.Nx.to_mat/{1,2}`	`Evision.Mat.from_nx/{1,2}`
`Evision.Nx.to_mat/5`	`Evision.Mat.from_binary/5`
`Evision.Nx.to_mat_2d/1`	`Evision.Mat.from_nx_2d/1`
`Evision.Nx.to_nx/1`	`Evision.Mat.to_nx/1`

Added

[Evision.Wx] implemented imshow/2, destroyWindow/1 and destroyAllWindows/0.
[SmartCell] Added SmartCells. They are optional and :kino >= 0.7 will be required to use them.

If you'd like to use smartcells, please add :kino to deps in the mix.exs file.
```
defp deps do
  [
    # ...
    {:kino, "~> 0.7"},
    # ...
  ]
end
```
And then please register smartcells to :kino by invoking Evision.SmartCell.register_smartcells().

Evision.SmartCell.available_smartcells/0 will return all available smartcells.

(Optional step) It's also possible to add only some of these smartcells, for example,
```
Evision.SmartCell.register_smartcells([
  Evision.SmartCell.ML.TrainData,
  Evision.SmartCell.ML.SVM
])
```

v0.1.13 (2022-10-19)

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Fixes

[c_src] Specialised function evision_to [with Tp_=cv::UMat].
[Evision.Backend] ensure that an Evision.Mat is returned from reject_error/1.

Changed

[c_src] parseSequence will only handle tuples.
[Evision.Mat] Evision.Mat.quicklook will use alternative escaping sequence to avoid having a dedicate function in NIF. Thanks to @akash-akya and @kipcole9 (vix#68).
```
ST means either BEL (hex code 0x07) or ESC \\.
```

[nx-integration] Functions in Evision.Nx are now moved to Evision.Mat.

Old	New
`Evision.Nx.to_mat/{1,2}`	`Evision.Mat.from_nx/{1,2}`
`Evision.Nx.to_mat/5`	`Evision.Mat.from_binary/5`
`Evision.Nx.to_mat_2d/1`	`Evision.Mat.from_nx_2d/1`
`Evision.Nx.to_nx/1`	`Evision.Mat.to_nx/1`

As of v0.1.13, calls to these old functions will be forwarded to the corresponding new ones.

In the next release (v0.1.14), Evision.Nx will be removed.

[Evision.Mat] Evision.Mat.tranpose will use cv::transposeND if possible.
[Precompile] Try to compile OpenCV with gtk3 support.

Added

[test] Added a test for Evision.warpPerspective.
[example] Added an example for Evision.warpPerspective.
[example] Added some examples for Evision.warpPolar.
[example] Added QRCode encoding and decoding example.
[docs] Added a cheatsheet.

v0.1.12 (2022-10-15)

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Breaking Changes

[Evision.QRCodeEncoder.Params] Renamed Evision.QRCodeEncoder.Params.qrcodeencoder_params/0 to Evision.QRCodeEncoder.Params.params/0.

Fixes

Function guard should also allow Nx.Tensor when the corresponding input argument is Evision.Mat.maybe_mat_in().
[Evision.Mat] Evision.Mat.quicklook/1 should also check the number of channels is one of [1, 3, 4] when dims == 2.
[c_src] evision_cv_mat_broadcast_to should call enif_free((void *)dst_data); if void * tmp_data = (void *)enif_alloc(elem_size * count_new_elem); failed.
[py_src] Fixed the template of simple call constructor.

Changed

[Docs] Example Livebooks is now included in docs as extras.
[Evision.Mat] Evision.Mat.roi/{2,3} now supports Elixir Range.

[Evision.Mat] Implemented Access behaviour.

Access.fetch/2 examples:

iex> img = Evision.imread("test/qr_detector_test.png")
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {300, 300, 3},
  ref: #Reference<0.809884129.802291734.78316>
}

# Same behaviour as Nx. 
# Also, img[0] gives the same result as img[[0]]
# For this example, they are both equvilent of img[[0, :all, :all]]
iex> img[[0]]
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {1, 300, 3},
  ref: #Reference<0.809884129.802291731.77296>
}

# same as img[[0..100, 50..200, :all]]
# however, currently we only support ranges with step size 1
#
# **IMPORTANT NOTE**
#
# also, please note that we are using Elixir.Range here
# and Elixir.Range is **inclusive**, i.e, [start, end] 
# while cv::Range `{integer(), integer()}` is `[start, end)`
# the difference can be observed in the `shape` field
iex> img[[0..100, 50..200]]
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {101, 151, 3},
  ref: #Reference<0.809884129.802291731.77297>
}
iex> img[[{0, 100}, {50, 200}]]
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {100, 150, 3},
  ref: #Reference<0.809884129.802291731.77297>
}

# for this example, the result is the same as `Evision.extractChannel(img, 0)`
iex> img[[:all, :all, 0]]
%Evision.Mat{
  channels: 1,
  dims: 2,
  type: {:u, 8},
  raw_type: 0,
  shape: {300, 300},
  ref: #Reference<0.809884129.802291731.77298>
}
iex> img[[:all, :all, 0..1]]
%Evision.Mat{
  channels: 2,
  dims: 2,
  type: {:u, 8},
  raw_type: 8,
  shape: {300, 300, 2},
  ref: #Reference<0.809884129.802291731.77299>
}

# when index is out of bounds
iex> img[[:all, :all, 42]]
{:error, "index 42 is out of bounds for axis 2 with size 3"}

# it works the same way for any dimensional Evision.Mat
iex> mat = Evision.Mat.ones({10, 10, 10, 10, 10}, :u8)
iex> mat[[1..7, :all, 2..6, 3..9, :all]]
%Evision.Mat{
  channels: 1,
  dims: 5,
  type: {:u, 8},
  raw_type: 0,
  shape: {7, 10, 5, 7, 10},
  ref: #Reference<0.3015448455.3766878228.259075>
}

Access.get_and_update/3 examples:

iex> mat = Evision.Mat.zeros({5, 5}, :u8)
iex> Evision.Nx.to_nx(mat)
#Nx.Tensor<
  u8[5][5]
  Evision.Backend
  [
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0]
  ]
>
iex> {old, new} = Evision.Mat.get_and_update(mat, [1..3, 1..3], fn roi ->
    {roi, Nx.broadcast(Nx.tensor(255, type: roi.type), roi.shape)}
end)
iex> Evision.Nx.to_nx(new)
#Nx.Tensor<
  u8[5][5]
  Evision.Backend
  [
    [0, 0, 0, 0, 0],
    [0, 255, 255, 255, 0],
    [0, 255, 255, 255, 0],
    [0, 255, 255, 255, 0],
    [0, 0, 0, 0, 0]
  ]
>

v0.1.11 (2022-10-13)

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Important Note

In v0.1.10, an invalid checksum file was pushed to hex.pm, please read the changelog, especially the breaking changes in v0.1.10. Changelog for v0.1.10.

Fixed

[Precompile] Mix.Tasks.Evision.Fetch should always download and oerwrite existing files.

v0.1.10 (2022-10-13)

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Important Note

Invalid checksum file was pushed to hex.pm, please use v0.1.11 instead.

Breaking Changes

Say goodbye to the bang(!) version functions!

Thanks to @josevalim who wrote me this Errorize module back in Feb 2022, and in v0.1.10 this module will be removed. There are two main reasons for this:
- I've managed to structurise all #references that have their own modules in #101.
- After generating function specs, dialyzer seems to be really upset about these bang(!) version functions, and would emit a few thousand warnings.
[Precompile] Include NIF version in precompiled tarball filename.
```
"evision-nif_#{nif_version}-#{target}-#{version}"
```

Return value changed if the first return type of the function is bool

If the function only returns a bool, the updated return value will simple be true or false.

# before
iex> :ok = Evision.imwrite("/path/to/image.png", img)
iex> :error = Evision.imwrite("/path/to/image.png", invalid_img)
# after
iex> true = Evision.imwrite("/path/to/image.png", img)
iex> false = Evision.imwrite("/path/to/image.png", invalid_img)

If the first return type is bool, and there is another value to return:

# before
iex> frame = Evision.VideoCapture.read(capture) # has a frame available
iex> :error = Evision.VideoCapture.read(capture) # cannot read / no more available frames
# after
iex> frame = Evision.VideoCapture.read(capture) # has a frame available
iex> false = Evision.VideoCapture.read(capture) # cannot read / no more available frames

If the first return type is bool, and there are more than one value to return:

# before
iex> {val1, val2} = Evision.SomeModule.some_function(arg1) # when succeeded
iex> :error = Evision.SomeModule.some_function(capture) # when failed
# after
iex> {val1, val2} = Evision.SomeModule.some_function(arg1) # when succeeded
iex> false = Evision.SomeModule.some_function(capture) # when failed

std::string and cv::String will be wrapped in a binary term instead of a list.

For example,

# before
iex> {'detected text', _, _} = Evision.QRCodeDetector.detectAndDecode(qr, img)
# after
iex> {"detected text", _, _} = Evision.QRCodeDetector.detectAndDecode(qr, img)

Structurised all #references that have their own module.

A list of modules that are now wrapped in structs can be found in the appendix section.

[Evision.DNN] As it's not possible to distinguish std::vector<uchar> and String in Elixir, Evision.DNN::readNet* functions that load a model from in-memoy buffer will be renamed to Evision.DNN::readNet*Buffer.

For example,

@spec readNetFromONNX(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}
def readNetFromONNX(onnxFile)

@spec readNetFromONNXBuffer(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}
def readNetFromONNXBuffer(buffer)

Changed

[Evision.Backend] raise a better error message for callbacks that haven't been implemented in Evision.Backend. Thanks to @josevalim

An example of the updated error message:

iex> Evision.Backend.slice(1,2,3,4,5)
** (RuntimeError) operation slice is not yet supported on Evision.Backend.
Please use another backend like Nx.BinaryBackend or Torchx.Backend.
  To use Torchx.Backend, :torchx should be added to your app's deps.
  Please see https://github.com/elixir-nx/nx/tree/main/torchx for more information on how to install and use it.
To convert the tensor to another backend, please use Evision.Nx.to_nx(tensor, Backend.ModuleName)
  for example, Evision.Nx.to_nx(tensor, Nx.BinaryBackend) or Evision.Nx.to_nx(tensor, Torchx.Backend).
Pull request would be more than welcomed if you'd like to implmenent this function and make contributions.
    (evision 0.1.10-dev) lib/evision/backend.ex:815: Evision.Backend.slice/5
    iex:1: (file)

[Docs] Improved cross reference in inline docs. For example,

Before
```
@doc """
...
@see setCVFolds
...
"""
def getCVFolds(self) do
```
After
```
@doc """
...
@see `setCVFolds/2`
...
"""
def getCVFolds(self) do
```
In this way, you can navigate to the referenced function in the generated html docs.

Fixed

Docs: included retval and self in the Return section.

Added

[Spec] Function spec for all Elixir functions, including generated ones.

[Evision.Mat] Added Evision.Mat.roi/{2,3}.

iex> img = Evision.imread("test/qr_detector_test.png")
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {300, 300, 3},
  ref: #Reference<0.3957900973.802816029.173984>
}

# Mat operator()( const Rect& roi ) const;
iex> sub_img = Evision.Mat.roi(img, {10, 10, 100, 200})
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {200, 100, 3},
  ref: #Reference<0.3957900973.802816020.173569>
}

# Mat operator()( Range rowRange, Range colRange ) const;
iex> sub_img = Evision.Mat.roi(img, {10, 100}, {20, 200}) 
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {90, 180, 3},
  ref: #Reference<0.3957900973.802816020.173570>
}
iex> sub_img = Evision.Mat.roi(img, :all, {20, 200}) 
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {300, 180, 3},
  ref: #Reference<0.3957900973.802816020.173571>
}

# Mat operator()(const std::vector<Range>& ranges) const;
iex> sub_img = Evision.Mat.roi(img, [{10, 100}, {10, 100}])
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {90, 90, 3},
  ref: #Reference<0.3957900973.802816020.173567>
}
iex> sub_img = Evision.Mat.roi(img, [{10, 100}, :all])
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {90, 300, 3},
  ref: #Reference<0.3957900973.802816020.173568>
}

[Evision.Mat] Added Evision.Mat.quicklook/1.

This function will check the value of :display_inline_image_iterm2 in the application config. If is true, then it will detect if current session is running in iTerm2 (by checking the environment variable LC_TERMINAL).

If both are true, we next check if the image is a 2D image, also if its size is within the limits. The maximum size can be set in the application config, for example,
```
config :evision, display_inline_image_iterm2: true
config :evision, display_inline_image_max_size: {8192, 8192}
```
If it passes all the checks, then it will be displayed as an inline image in iTerm2.

Appendix

List of modules that are now wrapped in structs.

Evision.AKAZE
Evision.AffineFeature
Evision.AgastFeatureDetector
Evision.Algorithm
Evision.AlignExposures
Evision.AlignMTB
Evision.AsyncArray
Evision.BFMatcher
Evision.BOWImgDescriptorExtractor
Evision.BOWKMeansTrainer
Evision.BOWTrainer
Evision.BRISK
Evision.BackgroundSubtractor
Evision.BackgroundSubtractorKNN
Evision.BackgroundSubtractorMOG2
Evision.CLAHE
Evision.CUDA
Evision.CUDA.BufferPool
Evision.CUDA.DeviceInfo
Evision.CUDA.Event
Evision.CUDA.GpuMat
Evision.CUDA.HostMem
Evision.CUDA.Stream
Evision.CUDA.TargetArchs
Evision.CalibrateCRF
Evision.CalibrateDebevec
Evision.CalibrateRobertson
Evision.CascadeClassifier
Evision.CirclesGridFinderParameters
Evision.DISOpticalFlow
Evision.DMatch
Evision.DNN.ClassificationModel
Evision.DNN.DetectionModel
Evision.DNN.DictValue
Evision.DNN.KeypointsModel
Evision.DNN.Layer
Evision.DNN.Model
Evision.DNN.Net
Evision.DNN.SegmentationModel
Evision.DNN.TextDetectionModel
Evision.DNN.TextDetectionModelDB
Evision.DNN.TextDetectionModelEAST
Evision.DNN.TextRecognitionModel
Evision.DenseOpticalFlow
Evision.DescriptorMatcher
Evision.Detail.AffineBasedEstimator
Evision.Detail.AffineBestOf2NearestMatcher
Evision.Detail.BestOf2NearestMatcher
Evision.Detail.BestOf2NearestRangeMatcher
Evision.Detail.Blender
Evision.Detail.BlocksChannelsCompensator
Evision.Detail.BlocksCompensator
Evision.Detail.BlocksGainCompensator
Evision.Detail.BundleAdjusterAffine
Evision.Detail.BundleAdjusterAffinePartial
Evision.Detail.BundleAdjusterBase
Evision.Detail.BundleAdjusterRay
Evision.Detail.BundleAdjusterReproj
Evision.Detail.CameraParams
Evision.Detail.ChannelsCompensator
Evision.Detail.DpSeamFinder
Evision.Detail.Estimator
Evision.Detail.ExposureCompensator
Evision.Detail.FeatherBlender
Evision.Detail.FeaturesMatcher
Evision.Detail.GainCompensator
Evision.Detail.GraphCutSeamFinder
Evision.Detail.HomographyBasedEstimator
Evision.Detail.ImageFeatures
Evision.Detail.MatchesInfo
Evision.Detail.MultiBandBlender
Evision.Detail.NoBundleAdjuster
Evision.Detail.NoExposureCompensator
Evision.Detail.NoSeamFinder
Evision.Detail.PairwiseSeamFinder
Evision.Detail.SeamFinder
Evision.Detail.SphericalProjector
Evision.Detail.Timelapser
Evision.Detail.VoronoiSeamFinder
Evision.FaceDetectorYN
Evision.FaceRecognizerSF
Evision.FarnebackOpticalFlow
Evision.FastFeatureDetector
Evision.Feature2D
Evision.FileNode
Evision.FileStorage
Evision.Flann.Index
Evision.FlannBasedMatcher
Evision.GFTTDetector
Evision.GeneralizedHough
Evision.GeneralizedHoughBallard
Evision.GeneralizedHoughGuil
Evision.HOGDescriptor
Evision.KAZE
Evision.KalmanFilter
Evision.KeyPoint
Evision.LineSegmentDetector
Evision.ML.ANNMLP
Evision.ML.Boost
Evision.ML.DTrees
Evision.ML.EM
Evision.ML.KNearest
Evision.ML.LogisticRegression
Evision.ML.NormalBayesClassifier
Evision.ML.ParamGrid
Evision.ML.RTrees
Evision.ML.SVM
Evision.ML.SVMSGD
Evision.ML.StatModel
Evision.ML.TrainData
Evision.MSER
Evision.MergeDebevec
Evision.MergeExposures
Evision.MergeMertens
Evision.MergeRobertson
Evision.OCL
Evision.OCL.Device
Evision.ORB
Evision.Parallel
Evision.PyRotationWarper
Evision.QRCodeDetector
Evision.QRCodeEncoder
Evision.QRCodeEncoder.Params
Evision.SIFT
Evision.Samples
Evision.Segmentation.IntelligentScissorsMB
Evision.SimpleBlobDetector
Evision.SimpleBlobDetector.Params
Evision.SparseOpticalFlow
Evision.SparsePyrLKOpticalFlow
Evision.StereoBM
Evision.StereoMatcher
Evision.StereoSGBM
Evision.Stitcher
Evision.Subdiv2D
Evision.TickMeter
Evision.Tonemap
Evision.TonemapDrago
Evision.TonemapMantiuk
Evision.TonemapReinhard
Evision.Tracker
Evision.TrackerDaSiamRPN
Evision.TrackerDaSiamRPN.Params
Evision.TrackerGOTURN
Evision.TrackerGOTURN.Params
Evision.TrackerMIL
Evision.TrackerMIL.Params
Evision.UMat
Evision.UsacParams
Evision.Utils.Nested.OriginalClassName
Evision.Utils.Nested.OriginalClassName.Params
Evision.VariationalRefinement
Evision.VideoCapture
Evision.VideoWriter

v0.1.9 (2022-10-09)

Browse the Repository | Released Assets

Bug Fixes

Mix.Tasks.Compile.EvisionPrecompiled: using File.cp_r/2 instead of calling cp -a via System.cmd/3.
Fixed TLS warnings when downloading precompiled tarball file. Thanks to @kipcole9!
Only include evision_custom_headers/evision_ml.hpp if the HAVE_OPENCV_ML macro is defined.

Support parsing RefWrapper<T> (&value)[N] from list or tuple. (#99)

See the function in c_src/evision.cpp.

bool parseSequence(ErlNifEnv *env, ERL_NIF_TERM obj, RefWrapper<T> (&value)[N], const ArgInfo& info)

# `RotatedRect` has to be a tuple, {centre, size, angle}
Evision.boxPoints!({{224.0, 262.5}, {343.0, 344.0}, 90.0})

# while `Point`/`Size` can be either a list, `[x, y]`, or a tuple, `{x, y}`
Evision.boxPoints!({[224.0, 262.5], [343.0, 344.0], 90.0})

Fixed the mapping from a type to the corresponding function guard in py_src/helper.py. (#99)

Changed

Display RotatedRect type as {centre={x, y}, size={s1, s2}, angle} in docs.

v0.1.8 (2022-10-08)

Browse the Repository | Released Assets

Changed

CMake and make (nmake if on Windows) will not be used to download and deploy precompiled binaries for Elixir users.

This means that evision can be downloaded and deployed once Erlang and Elixir are properly installed on the system.

v0.1.7 (2022-10-07)

Browse the Repository | Released Assets

Breaking Changes

EVISION_PREFER_PRECOMPILED is set to true by default.

:evision will try to use precompiled binaries if available. Otherwise, it will fallback to building from source.

Precompiled binary filename changed:

arm64-apple-darwin => aarch64-apple-darwin
amd64-windows-msvc => x86_64-windows-msvc

Changed

cv::VideoCapture will be wrapped in struct. For example:

iex> cap = Evision.VideoCapture.videoCapture!("test/videocapture_test.mp4")
%Evision.VideoCapture{
  fps: 43.2,
  frame_count: 18.0,
  frame_width: 1920.0,
  frame_height: 1080.0,
  isOpened: true,
  ref: #Reference<0.3650318819.3952214034.37793>
}
iex> frame = Evision.VideoCapture.read!(cap)
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {1080, 1920, 3},
  ref: #Reference<0.3650318819.3952214042.38343>
}

Evision.Mat.empty/0 will also return an Evision.Mat struct (was returning #Reference<some random numbers>).

iex> Evision.Mat.empty!()
%Evision.Mat{
  channels: 1,
  dims: 0,
  type: {:u, 8},
  raw_type: 0,
  shape: {},
  ref: #Reference<0.2351084001.2568618002.207930>
}

raise RuntimeError for all unimplemented :nx callbacks.
```
raise RuntimeError, "not implemented yet"
```
Elixir functions that have the same name and arity will be grouped together now.

This should massively reduce the number of warnings emitted by the elixir compiler.
Only generate corresponding binding code.
- Only generate binding code for Elixir when compiling :evision using mix;
- Only generate binding code for erlang when compiling :evision using rebar;
It's possible to generate erlang and Elixir at the same time. However, currently it's only possible to do so when compiling evision using mix.
```
# default value is `elixir` when compiling evision using `mix`
# default value is `erlang` when compiling evision using `rebar`
#
# expected format is a comma-separated string
export EVISION_GENERATE_LANG="erlang,elixir"
```

Better inline docs.

Inline docs will have a section for Positional Arguments and a section for Keyword Arguments. For example,

@doc """
### Positional Arguments
- **bboxes**: vector_Rect2d. 
- **scores**: vector_float. 
- **score_threshold**: float. 
- **nms_threshold**: float. 

### Keyword Arguments
- **eta**: float.
- **top_k**: int.

Performs non maximum suppression given boxes and corresponding scores.

Python prototype (for reference): 
```
NMSBoxes(bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]) -> indices
```
"""
@doc namespace: :"cv.dnn"
def nmsBoxes(bboxes, scores, score_threshold, nms_threshold, opts)

If a function (same name and arity) has multiple variants, the inline docs will show each of them in section ## Variant VAR_INDEX. For example,

@doc """
#### Variant 1:

##### Positional Arguments
- **dx**: UMat. 
- **dy**: UMat. 
- **threshold1**: double. 
- **threshold2**: double. 

##### Keyword Arguments
- **edges**: UMat.
- **l2gradient**: bool.

  \\overload
  Finds edges in an image using the Canny algorithm with custom image gradient.
    \\f$=\\sqrt{(dI/dx)^2 + (dI/dy)^2}\\f$ should be used to calculate the image gradient magnitude (
    L2gradient=true ), or whether the default \\f$L\\_1\\f$ norm \\f$=|dI/dx|+|dI/dy|\\f$ is enough (
    L2gradient=false ).

Python prototype (for reference): 
```
Canny(dx, dy, threshold1, threshold2[, edges[, L2gradient]]) -> edges
```
#### Variant 2:

##### Positional Arguments
- **image**: UMat. 
- **threshold1**: double. 
- **threshold2**: double. 

##### Keyword Arguments
- **edges**: UMat.
- **apertureSize**: int.
- **l2gradient**: bool.

Finds edges in an image using the Canny algorithm @cite Canny86 .
  The function finds edges in the input image and marks them in the output map edges using the
  Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
  largest value is used to find initial segments of strong edges. See
  <http://en.wikipedia.org/wiki/Canny_edge_detector>
    \\f$=\\sqrt{(dI/dx)^2 + (dI/dy)^2}\\f$ should be used to calculate the image gradient magnitude (
    L2gradient=true ), or whether the default \\f$L\\_1\\f$ norm \\f$=|dI/dx|+|dI/dy|\\f$ is enough (
    L2gradient=false ).

Python prototype (for reference): 
```
Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) -> edges
```

"""
@doc namespace: :cv
def canny(image, threshold1, threshold2, opts)
when (is_reference(image) or is_struct(image)) and is_number(threshold1) and is_number(threshold2) and is_list(opts) and (opts == [] or is_tuple(hd(opts))), do: # variant 2

def canny(dx, dy, threshold1, threshold2)
when (is_reference(dx) or is_struct(dx)) and (is_reference(dy) or is_struct(dy)) and is_number(threshold1) and is_number(threshold2), do: # variant 1

Better integration with :nx.

iex> t = Nx.tensor([[[0,0,0], [255, 255, 255]]], type: :u8)
#Nx.Tensor<
  u8[1][2][3]
  [
    [
      [0, 0, 0],
      [255, 255, 255]
    ]
  ]
>
iex> mat = Evision.imread!("test.png")
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {1, 2, 3},
  ref: #Reference<0.2067356221.74055707.218654>
}
iex> mat = Evision.Mat.channel_as_last_dim!(mat)
%Evision.Mat{
  channels: 1,
  dims: 3,
  type: {:u, 8},
  raw_type: 0,
  shape: {1, 2, 3},
  ref: #Reference<0.2067356221.74055698.218182>
}
iex> result = Evision.Mat.add!(t, mat)
%Evision.Mat{
  channels: 1,
  dims: 3,
  type: {:u, 8},
  raw_type: 0,
  shape: {1, 2, 3},
  ref: #Reference<0.2067356221.74055698.218184>
}
iex> Evision.Nx.to_nx!(result)
#Nx.Tensor<
  u8[1][2][3]
  Evision.Backend
  [
    [
      [255, 255, 255],
      [255, 255, 255]
    ]
  ]
>

Implemented property setter for cv::Ptr<> wrapped types. For example,

iex> k = Evision.KalmanFilter.kalmanFilter!(1, 1)
#Reference<0.382162378.457572372.189094>
iex> Evision.KalmanFilter.get_gain!(k) |> Evision.Nx.to_nx!
#Nx.Tensor<
  f32[1][1]
  Evision.Backend
  [
    [0.0]
  ]
>
iex> Evision.KalmanFilter.set_gain!(k, Evision.Mat.literal!([1.0], :f32))
#Reference<0.382162378.457572372.189094>
iex> Evision.KalmanFilter.get_gain!(k) |> Evision.Nx.to_nx!
#Nx.Tensor<
  f32[1][1]
  Evision.Backend
  [
    [1.0]
  ]
>

More detailed error message for property getter/setter. For example,

When setting a property that is type A and value passed to the setter is type B, and there is no known conversion from B to A, then it will return an error-tuple

iex> k = Evision.KalmanFilter.kalmanFilter!(1, 1)
iex> Evision.KalmanFilter.set_gain(k, :p)
{:error, "cannot assign new value, mismatched type?"}
iex> Evision.KalmanFilter.set_gain(k, :p)
** (RuntimeError) cannot assign new value, mismatched type?
    (evision 0.1.7) lib/generated/evision_kalmanfilter.ex:175: Evision.KalmanFilter.set_gain!/2
    iex:7: (file)

For property getter/setter, if the self passed in is a different type than what is expected, an error-tuple will be returned

iex> mat = Evision.Mat.literal!([1.0], :f32)
%Evision.Mat{
  channels: 1,
  dims: 2,
  type: {:f, 32},
  raw_type: 5,
  shape: {1, 1},
  ref: #Reference<0.1499445684.3682467860.58544>
}
iex> Evision.KalmanFilter.set_gain(mat, mat) 
{:error,
"cannot get `Ptr<cv::KalmanFilter>` from `self`: mismatched type or invalid resource?"}
iex> Evision.KalmanFilter.set_gain!(mat, mat)
** (RuntimeError) cannot get `Ptr<cv::KalmanFilter>` from `self`: mismatched type or invalid resource?
    (evision 0.1.7) lib/generated/evision_kalmanfilter.ex:175: Evision.KalmanFilter.set_gain!/2
    iex:2: (file)

evision_##NAME##_getp (in c_src/erlcompat.hpp) should just return true or false.

Returning a ERL_NIF_TERM (enif_make_badarg) in the macro (when enif_get_resource fails) will prevent the caller from returning an error-tuple with detailed error message.
Improved the quality of generated inline docs.

Also displays what variable(s) will be returned (when applicable) in the ##### Return section.

Added

Added Evision.Mat.literal/{1,2,3} to create Evision.Mat from list literals.

Creating Evision.Mat from empty list literal ([]) is the same as calling Evision.Mat.empty().

iex> Evision.Mat.literal!([])
%Evision.Mat{
  channels: 1,
  dims: 0,
  type: {:u, 8},
  raw_type: 0,
  shape: {},
  ref: #Reference<0.1204050731.2031747092.46781>
}

By default, the shape of the Mat will stay as is.

iex> Evision.Mat.literal!([[[1,1,1],[2,2,2],[3,3,3]]], :u8)
%Evision.Mat{
  channels: 1,
  dims: 3,
  type: {:u, 8},
  raw_type: 0,
  shape: {1, 3, 3},
  ref: #Reference<0.512519210.691404819.106300>
}

Evision.Mat.literal/3 will return a valid 2D image if the keyword argument, as_2d, is set to true and if the list literal can be represented as a 2D image.

iex> Evision.Mat.literal!([[[1,1,1],[2,2,2],[3,3,3]]], :u8, as_2d: true)
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {1, 3, 3},
  ref: #Reference<0.512519210.691404820.106293>
}

Added Evision.Mat.channel_as_last_dim/1.

This function does the opposite as to Evision.Mat.last_dim_as_channel/1.

If the number of channels of the input Evision.Mat is greater than 1, then this function would convert the input Evision.Mat with dims dims=list(int()) to a 1-channel Evision.Mat with dims [dims | channels].

If the number of channels of the input Evision.Mat is equal to 1,
- if dims == shape, then nothing happens
- otherwise, a new Evision.Mat that has dims=[dims | channels] will be returned
For example,
```
iex> mat = Evision.imread!("test.png")
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {1, 2, 3},
  ref: #Reference<0.2067356221.74055707.218654>
}
iex> mat = Evision.Mat.channel_as_last_dim!(mat)
%Evision.Mat{
  channels: 1,
  dims: 3,
  type: {:u, 8},
  raw_type: 0,
  shape: {1, 2, 3},
  ref: #Reference<0.2067356221.74055698.218182>
}
```
Automatically displays a tabbed output in Livebook if the type of evaluated result is Evision.Mat.

This is an optional feature. To enable it, :kino should be added to deps, e.g.,
```
defp deps do
  [
    # ...
    {:kino, "~> 0.7"},
    # ...
  ]
end
```
Now, with :kino >= v0.7 available, a tabbed output will shown in Livebook if the evaluated result is an Evision.Mat.

A Raw tab will always be the first one, e.g.,
```
%Evision.Mat{
  channels: 1,
  dims: 3,
  type: {:u, 8},
  raw_type: 0,
  shape: {1, 2, 3},
  ref: #Reference<0.3310236255.1057357843.168932>
}
```
For 2D images (dims == 2), the second tab will be Image, which displays the image.

For all Evision.Mat, the last tab will be Numerical, which shows the numbers behind the scene. Of course, for large size Evision.Mat, only part of the data will be shown. A example output in this tab:
```
#Nx.Tensor<
  u8[1][2][3]
  Evision.Backend
  [
    [
      [1, 2, 3],
      [1, 2, 3]
    ]
  ]
>
```

v0.1.6 (2022-09-29)

Browse the Repository | Released Assets

Breaking Changes

Evision.imencode/{2,3} will now return encoded image as binary instead of a list.

cv::Mat will be wrapped in struct. For example:

iex> Evision.imread!("path/to/image.png")
%Evision.Mat{
  channels: 3,
  dims: 2,
  type: {:u, 8},
  raw_type: 16,
  shape: {512, 512, 3},
  ref: #Reference<0.2992585850.4173463580.172624>
}

This should close #76.

v0.1.5 (2022-09-27)

Browse the Repository | Released Assets

Changed

Always use Evision.Mat.from_binary_by_shape/3 for Evision.Nx.to_mat.
Check cv::Mat::Mat.type() when fetching the shape of a Mat. The number of channels will be included as the last dim of the shape if and only if cv::Mat::Mat.type() did not encode any channel information.

Bug Fixes

Fixed Evision.Mat.transpose: should call shape! instead of shape. Thanks to @kipcole9 ! #77

Added

Added Evision.Mat.last_dim_as_channel/1.

This method convert a tensor-like Mat to a "valid 2D image" with its channels equals to 3 or 1.
Added Evision.Nx.to_mat/2.

This method convert a Nx.Tensor to a Mat. The second argument indicates the wanted/actual shape of the tensor.
Added more Mat functions:
- Evision.Mat.as_shape/2.
- Evision.Mat.size/1.
- Evision.Mat.channels/1.
- Evision.Mat.depth/1.
- Evision.Mat.raw_type/1.
- Evision.Mat.isSubmatrix/1.
- Evision.Mat.isContinuous/1.
- Evision.Mat.elemSize/1.
- Evision.Mat.elemSize1/1.
- Evision.Mat.total/{1,2,3}.
Added OpenCV types:
- Evision.cv_cn_shift/0.
- Evision.cv_depth_max/0.
- Evision.cv_mat_depth_mask/0.
- Evision.cv_maketype/2.
- Evision.cv_8U/0.
- Evision.cv_8UC/1.
- Evision.cv_8UC1/0.
- Evision.cv_8UC2/0.
- Evision.cv_8UC3/0.
- Evision.cv_8UC4/0.
- Evision.cv_8S/0.
- Evision.cv_8SC/1.
- Evision.cv_8SC1/0.
- Evision.cv_8SC2/0.
- Evision.cv_8SC3/0.
- Evision.cv_8SC4/0.
- Evision.cv_16U/0.
- Evision.cv_16UC/1.
- Evision.cv_16UC1/0.
- Evision.cv_16UC2/0.
- Evision.cv_16UC3/0.
- Evision.cv_16UC4/0.
- Evision.cv_16S/0.
- Evision.cv_16SC/1.
- Evision.cv_16SC1/0.
- Evision.cv_16SC2/0.
- Evision.cv_16SC3/0.
- Evision.cv_16SC4/0.
- Evision.cv_32S/0.
- Evision.cv_32SC/1.
- Evision.cv_32SC1/0.
- Evision.cv_32SC2/0.
- Evision.cv_32SC3/0.
- Evision.cv_32SC4/0.
- Evision.cv_32F/0.
- Evision.cv_32FC/1.
- Evision.cv_32FC1/0.
- Evision.cv_32FC2/0.
- Evision.cv_32FC3/0.
- Evision.cv_32FC4/0.
- Evision.cv_64F/0.
- Evision.cv_64FC/1.
- Evision.cv_64FC1/0.
- Evision.cv_64FC2/0.
- Evision.cv_64FC3/0.
- Evision.cv_64FC4/0.
- Evision.cv_16F/0.
- Evision.cv_16FC/1.
- Evision.cv_16FC1/0.
- Evision.cv_16FC2/0.
- Evision.cv_16FC3/0.
- Evision.cv_16FC4/0.

v0.1.4 (2022-09-10)

Browse the Repository | Released Assets

Changed

Default to Evision.Backend for Evision.Nx.to_nx/2.

Bug Fixes

Fixed class inheritance issue in py_src/class_info.py.
Fixed missing comma in example livebooks' Mix.install. Thanks to @dbii.

Added

Added decision tree and random forest example.

v0.1.3 (2022-09-01)

Browse the Repository | Released Assets

Bug Fixes

Fixed issues in restoring files from precompiled package for macOS and Linux.
- Paths are now quoted.
- using cp -RPf on macOS while cp -a on Linux.
Fixed destroyAllWindows in NIF. It was generated as 'erlang:destroyAllWindows/1' but it should be 'erlang:destroyAllWindows/0'.

v0.1.2 (2022-08-26)

Browse the Repository | Released Assets

Bug Fixes

Fixed transpose.

Added

Added x86_64 musl compilation CI test.
Added a few precompilation musl targets:
- x86_64-linux-musl
- aarch64-linux-musl
- armv7l-linux-musleabihf
- riscv64-linux-musl

v0.1.1 (2022-08-25)

Browse the Repository | Released Assets

Changed

Use OpenCV 4.6.0 by default.
Deprecated the use of the EVISION_PRECOMPILED_VERSION environment variable. The version information will be implied by the tag:
```
def deps do
[
    {:evision, "~> 0.1.1", github: "cocoa-xu/evision", tag: "v0.1.1"}
]
end
```
The value of the environment variable EVISION_PREFER_PRECOMPILED decides whether the precompiled artefacts will be used or not.

~~From the next version (>=0.1.2), evision will set EVISION_PREFER_PRECOMPILED to true by default.~~

Added

Implemented a few nx callbacks (remaining ones will be implemented in the next release).

v0.1.0 (2022-07-23)

First release.

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v0.1.39 (2024-06-01)

Changes

v0.1.38 (2024-03-30)

Fix

v0.1.37 (2024-02-17)

Changes

Added

v0.1.36 (2024-02-16)

Fix

Added

v0.1.35 (2024-02-14)

Changed

v0.1.34 (2023-11-18)

Changed

v0.1.32 (2023-08-01)

Changed

v0.1.31 (2023-04-17)

Changed

v0.1.30 (2023-03-24)

Fixed

v0.1.29 (2023-03-13)

Fixed

v0.1.28 (2023-01-25)

Fixed

v0.1.27 (2023-01-23)

Breaking Changes

Fixed

Changed

Added

v0.1.26 (2023-01-21)

v0.1.25 (2022-12-18)

Fixed

v0.1.23/v0.1.24 (2022-12-17)

Fixed

Changed

v0.1.22 (2022-12-16)

Added

v0.1.21 (2022-11-25)

Fixed

v0.1.20 (2022-11-24)

Fixed

v0.1.19 (2022-11-14)

Changed

v0.1.18 (2022-11-12)

Fixes

v0.1.17 (2022-11-11)

Fixes

Changed

Added

v0.1.16 (2022-10-30)

Fixes

Changes

Added

v0.1.15 (2022-10-26)

Changes

Fixes

v0.1.14 (2022-10-22)

Breaking Changes

Added

v0.1.13 (2022-10-19)

Fixes

Changed

Added

v0.1.12 (2022-10-15)

Breaking Changes

Fixes

Changed

v0.1.11 (2022-10-13)

Important Note

Fixed

v0.1.10 (2022-10-13)

Important Note

Breaking Changes