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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: 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

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-1

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

v0.1.7 (2022-10-07)

Browse the Repository | Released Assets

breaking-changes

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-2

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)

    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 vaild 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](https://github.com/cocoa-xu/evision/tree/v0.1.6) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.6)
    ### 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](https://github.com/cocoa-xu/evision/tree/v0.1.5) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.5)
    ### 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](https://github.com/cocoa-xu/evision/tree/v0.1.4) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.4)
    ### 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](https://github.com/cocoa-xu/evision/tree/v0.1.3) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.3)
    ### 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](https://github.com/cocoa-xu/evision/tree/v0.1.2) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.2)
    ### 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](https://github.com/cocoa-xu/evision/tree/v0.1.1) | [Released Assets](https://github.com/cocoa-xu/evision/releases/tag/v0.1.1)
    ### 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.