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v0-1-10-2022-10-13
v0.1.10 (2022-10-13)
Browse the Repository | Released Assets
breaking-changes
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.
- I've managed to structurise all
[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 betrue
orfalse
.# 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> true = 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
andcv::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
#reference
s 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>
andString
in Elixir,Evision.DNN::readNet*
functions that load a model from in-memoy buffer will be renamed toEvision.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
Changed
[Evision.Backend] raise a better error message for callbacks that haven't been implemented in
Evision.Backend
. Thanks to @josevalimAn 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
Fixed
- Docs: included
retval
andself
in theReturn
section.
added
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 istrue
, then it will detect if current session is running iniTerm2
(by checking the environment variableLC_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
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
v0.1.9 (2022-10-09)
Browse the Repository | Released Assets
bug-fixes
Bug Fixes
Mix.Tasks.Compile.EvisionPrecompiled
: usingFile.cp_r/2
instead of callingcp -a
viaSystem.cmd/3
.Fixed TLS warnings when downloading precompiled tarball file. Thanks to @kipcole9!
Only include
evision_custom_headers/evision_ml.hpp
if theHAVE_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-1
Changed
- Display
RotatedRect
type as{centre={x, y}, size={s1, s2}, angle}
in docs.
v0-1-8-2022-10-08
v0.1.8 (2022-10-08)
Browse the Repository | Released Assets
changed-2
Changed
CMake
andmake
(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
v0.1.7 (2022-10-07)
Browse the Repository | Released Assets
breaking-changes-1
Breaking Changes
EVISION_PREFER_PRECOMPILED
is set totrue
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-3
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 anEvision.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
usingmix
; - Only generate binding code for erlang when compiling
:evision
usingrebar
;
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"
- Only generate binding code for Elixir when compiling
Better inline docs.
Inline docs will have a section for
Positional Arguments
and a section forKeyword 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 typeB
, and there is no known conversion fromB
toA
, then it will return an error-tupleiex> 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 returnediex> 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
(inc_src/erlcompat.hpp
) should just return true or false.Returning a
ERL_NIF_TERM
(enif_make_badarg
) in the macro (whenenif_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-1
Added
Added
Evision.Mat.literal/{1,2,3}
to createEvision.Mat
from list literals.Creating
Evision.Mat
from empty list literal ([]
) is the same as callingEvision.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 vaild 2D image if the keyword argument,as_2d
, is set totrue
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 a1
-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 todeps
, 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 anEvision.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 beImage
, which displays the image.For all
Evision.Mat
, the last tab will beNumerical
, which shows the numbers behind the scene. Of course, for large sizeEvision.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
v0.1.6 (2022-09-29)
Browse the Repository | Released Assets
breaking-changes-2
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
v0.1.5 (2022-09-27)
Browse the Repository | Released Assets
changed-4
Changed
- Always use
Evision.Mat.from_binary_by_shape/3
forEvision.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 ifcv::Mat::Mat.type()
did not encode any channel information.
bug-fixes-1
Bug Fixes
- Fixed
Evision.Mat.transpose
: should callshape!
instead ofshape
. Thanks to @kipcole9 ! #77
added-2
Added
Added
Evision.Mat.last_dim_as_channel/1
.This method convert a tensor-like
Mat
to a "valid 2D image" with itschannels
equals to3
or1
.Added
Evision.Nx.to_mat/2
.This method convert a
Nx.Tensor
to aMat
. The second argument indicates the wanted/actual shape of the tensor.Added more Mat functions:
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
v0.1.4 (2022-09-10)
Browse the Repository | Released Assets
changed-5
Changed
- Default to
Evision.Backend
forEvision.Nx.to_nx/2
.
bug-fixes-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-3
Added
- Added decision tree and random forest example.
v0-1-3-2022-09-01
v0.1.3 (2022-09-01)
Browse the Repository | Released Assets
bug-fixes-3
Bug Fixes
- Fixed issues in restoring files from precompiled package for macOS and Linux.
- Paths are now quoted.
- using
cp -RPf
on macOS whilecp -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
v0.1.2 (2022-08-26)
Browse the Repository | Released Assets
bug-fixes-4
Bug Fixes
- Fixed transpose.
added-4
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
v0.1.1 (2022-08-25)
Browse the Repository | Released Assets
changed-6
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 setEVISION_PREFER_PRECOMPILED
totrue
by default.
added-5
Added
- Implemented a few nx callbacks (remaining ones will be implemented in the next release).
v0-1-0-2022-07-23
v0.1.0 (2022-07-23)
First release.