Archit Rungta
5 years ago
parent
0c99a85a98
commit
ff581614c8
14 changed files with 1166 additions and 38 deletions
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This is a compilation of the general functions implemented in this module. |
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``` |
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function KeyPoint(x::Float32, y::Float32, _size::Float32, _angle::Float32, _response::Float32, _octave::Int32, _class_id::Int32) |
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KeyPoint(x::Float32, y::Float32, _size::Float32; _angle::Float32 = Float32(-1), _response::Float32 = Float32(0), _octave::Int32 = Int32(0), _class_id::Int32 = Int32(-1)) = KeyPoint(x, y, _size, _angle, _response, _octave, _class_id) |
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function VideoCapture(filename::String, apiPreference::Int32) |
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VideoCapture(filename::String; apiPreference::Int32 = Int32(CAP_ANY)) = VideoCapture(filename, apiPreference) |
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function VideoCapture(index::Int32, apiPreference::Int32) |
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VideoCapture(index::Int32; apiPreference::Int32 = Int32(CAP_ANY)) = VideoCapture(index, apiPreference) |
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function CascadeClassifier(filename::String) |
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function detect(cobj::cv_Ptr{T}, image::InputArray, mask::InputArray) where {T <: Feature2D} |
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detect(cobj::cv_Ptr{T}, image::InputArray; mask::InputArray = (CxxMat())) where {T <: Feature2D} = detect(cobj, image, mask) |
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function detectMultiScale(cobj::CascadeClassifier, image::InputArray, scaleFactor::Float64, minNeighbors::Int32, flags::Int32, minSize::Size{Int32}, maxSize::Size{Int32}) |
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detectMultiScale(cobj::CascadeClassifier, image::InputArray; scaleFactor::Float64 = Float64(1.1), minNeighbors::Int32 = Int32(3), flags::Int32 = Int32(0), minSize::Size{Int32} = (Size{Int32}(0,0)), maxSize::Size{Int32} = (Size{Int32}(0,0))) = detectMultiScale(cobj, image, scaleFactor, minNeighbors, flags, minSize, maxSize) |
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function empty(cobj::CascadeClassifier) |
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function read(cobj::VideoCapture, image::InputArray) |
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read(cobj::VideoCapture; image::InputArray = (CxxMat())) = read(cobj, image) |
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function release(cobj::VideoCapture) |
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function SimpleBlobDetector_create(parameters::SimpleBlobDetector_Params) |
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SimpleBlobDetector_create(; parameters::SimpleBlobDetector_Params = (SimpleBlobDetector_Params())) = SimpleBlobDetector_create(parameters) |
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function imread(filename::String, flags::Int32) |
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imread(filename::String; flags::Int32 = Int32(IMREAD_COLOR)) = imread(filename, flags) |
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function imshow(winname::String, mat::InputArray) |
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function namedWindow(winname::String, flags::Int32) |
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namedWindow(winname::String; flags::Int32 = Int32(WINDOW_AUTOSIZE)) = namedWindow(winname, flags) |
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function waitKey(delay::Int32) |
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waitKey(; delay::Int32 = Int32(0)) = waitKey(delay) |
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function rectangle(img::InputArray, pt1::Point{Int32}, pt2::Point{Int32}, color::Scalar, thickness::Int32, lineType::Int32, shift::Int32) |
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rectangle(img::InputArray, pt1::Point{Int32}, pt2::Point{Int32}, color::Scalar; thickness::Int32 = Int32(1), lineType::Int32 = Int32(LINE_8), shift::Int32 = Int32(0)) = rectangle(img, pt1, pt2, color, thickness, lineType, shift) |
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function cvtColor(src::InputArray, code::Int32, dst::InputArray, dstCn::Int32) |
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cvtColor(src::InputArray, code::Int32; dst::InputArray = (CxxMat()), dstCn::Int32 = Int32(0)) = cvtColor(src, code, dst, dstCn) |
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function equalizeHist(src::InputArray, dst::InputArray) |
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equalizeHist(src::InputArray; dst::InputArray = (CxxMat())) = equalizeHist(src, dst) |
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function destroyAllWindows() |
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function getTextSize(text::String, fontFace::Int32, fontScale::Float64, thickness::Int32) |
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function putText(img::InputArray, text::String, org::Point{Int32}, fontFace::Int32, fontScale::Float64, color::Scalar, thickness::Int32, lineType::Int32, bottomLeftOrigin::Bool) |
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putText(img::InputArray, text::String, org::Point{Int32}, fontFace::Int32, fontScale::Float64, color::Scalar; thickness::Int32 = Int32(1), lineType::Int32 = Int32(LINE_8), bottomLeftOrigin::Bool = (false)) = putText(img, text, org, fontFace, fontScale, color, thickness, lineType, bottomLeftOrigin) |
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``` |
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This is a list of functions implemented from the `dnn` module. |
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``` |
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function finalize(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}, outputs::Array{InputArray, 1}) where {T <: dnn_Layer} |
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finalize(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}; outputs::Array{InputArray, 1} = (Array{InputArray, 1}())) where {T <: dnn_Layer} = finalize(cobj, inputs, outputs) |
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function outputNameToIndex(cobj::cv_Ptr{T}, outputName::String) where {T <: dnn_Layer} |
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function empty(cobj::dnn_Net) |
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function dump(cobj::dnn_Net) |
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function dumpToFile(cobj::dnn_Net, path::String) |
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function getLayerId(cobj::dnn_Net, layer::String) |
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function getLayerNames(cobj::dnn_Net) |
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function getLayer(cobj::dnn_Net, layerId::dnn_LayerId) |
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function connect(cobj::dnn_Net, outPin::String, inpPin::String) |
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function setInputsNames(cobj::dnn_Net, inputBlobNames::Array{String, 1}) |
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function setInputShape(cobj::dnn_Net, inputName::String, shape::Array{Int32, 1}) |
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function forward(cobj::dnn_Net, outputName::String) |
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forward(cobj::dnn_Net; outputName::String = (String())) = forward(cobj, outputName) |
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function forward(cobj::dnn_Net, outputBlobs::Array{InputArray, 1}, outputName::String) |
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function forward(cobj::dnn_Net, outBlobNames::Array{String, 1}, outputBlobs::Array{InputArray, 1}) |
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forward(cobj::dnn_Net, outBlobNames::Array{String, 1}; outputBlobs::Array{InputArray, 1} = (Array{InputArray, 1}())) = forward(cobj, outBlobNames, outputBlobs) |
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function forwardAsync(cobj::dnn_Net, outputName::String) |
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forwardAsync(cobj::dnn_Net; outputName::String = (String())) = forwardAsync(cobj, outputName) |
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function setHalideScheduler(cobj::dnn_Net, scheduler::String) |
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function setPreferableBack(cobj::dnn_Net, backId::Int32) |
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function setPreferableTarget(cobj::dnn_Net, targetId::Int32) |
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function setInput(cobj::dnn_Net, blob::InputArray, name::String, scalefactor::Float64, mean::Scalar) |
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setInput(cobj::dnn_Net, blob::InputArray; name::String = (""), scalefactor::Float64 = Float64(1.0), mean::Scalar = ()) = setInput(cobj, blob, name, scalefactor, mean) |
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function setParam(cobj::dnn_Net, layer::dnn_LayerId, numParam::Int32, blob::InputArray) |
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function getParam(cobj::dnn_Net, layer::dnn_LayerId, numParam::Int32) |
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getParam(cobj::dnn_Net, layer::dnn_LayerId; numParam::Int32 = Int32(0)) = getParam(cobj, layer, numParam) |
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function getUnconnectedOutLayers(cobj::dnn_Net) |
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function getUnconnectedOutLayersNames(cobj::dnn_Net) |
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function getLayersShapes(cobj::dnn_Net, netInputShapes::Array{Array{Int32, 1}, 1}) |
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function getLayersShapes(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
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function getFLOPS(cobj::dnn_Net, netInputShapes::Array{Array{Int32, 1}, 1}) |
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function getFLOPS(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
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function getFLOPS(cobj::dnn_Net, layerId::Int32, netInputShapes::Array{Array{Int32, 1}, 1}) |
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function getFLOPS(cobj::dnn_Net, layerId::Int32, netInputShape::Array{Int32, 1}) |
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function getLayerTypes(cobj::dnn_Net) |
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function getLayersCount(cobj::dnn_Net, layerType::String) |
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function getMemoryConsumption(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
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function getMemoryConsumption(cobj::dnn_Net, layerId::Int32, netInputShapes::Array{Array{Int32, 1}, 1}) |
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function getMemoryConsumption(cobj::dnn_Net, layerId::Int32, netInputShape::Array{Int32, 1}) |
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function enableFusion(cobj::dnn_Net, fusion::Bool) |
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function getPerfProfile(cobj::dnn_Net) |
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function setInputSize(cobj::dnn_Model, size::Size) |
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function setInputSize(cobj::dnn_Model, width::Int32, height::Int32) |
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function setInputMean(cobj::dnn_Model, mean::Scalar) |
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function setInputScale(cobj::dnn_Model, scale::Float64) |
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function setInputCrop(cobj::dnn_Model, crop::Bool) |
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function setInputSwapRB(cobj::dnn_Model, swapRB::Bool) |
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function setInputParams(cobj::dnn_Model, scale::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool) |
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setInputParams(cobj::dnn_Model; scale::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false)) = setInputParams(cobj, scale, size, mean, swapRB, crop) |
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function predict(cobj::dnn_Model, frame::InputArray, outs::Array{InputArray, 1}) |
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predict(cobj::dnn_Model, frame::InputArray; outs::Array{InputArray, 1} = (Array{InputArray, 1}())) = predict(cobj, frame, outs) |
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function dnn_Model(model::String, config::String) |
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dnn_Model(model::String; config::String = ("")) = dnn_Model(model, config) |
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function dnn_Model(network::dnn_Net) |
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function classify(cobj::dnn_ClassificationModel, frame::InputArray) |
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function dnn_ClassificationModel(model::String, config::String) |
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dnn_ClassificationModel(model::String; config::String = ("")) = dnn_ClassificationModel(model, config) |
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function dnn_ClassificationModel(network::dnn_Net) |
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function estimate(cobj::dnn_KeypointsModel, frame::InputArray, thresh::Float32) |
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estimate(cobj::dnn_KeypointsModel, frame::InputArray; thresh::Float32 = Float32(0.5)) = estimate(cobj, frame, thresh) |
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function dnn_KeypointsModel(model::String, config::String) |
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dnn_KeypointsModel(model::String; config::String = ("")) = dnn_KeypointsModel(model, config) |
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function dnn_KeypointsModel(network::dnn_Net) |
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function segment(cobj::dnn_SegmentationModel, frame::InputArray, mask::InputArray) |
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segment(cobj::dnn_SegmentationModel, frame::InputArray; mask::InputArray = (CxxMat())) = segment(cobj, frame, mask) |
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function dnn_SegmentationModel(model::String, config::String) |
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dnn_SegmentationModel(model::String; config::String = ("")) = dnn_SegmentationModel(model, config) |
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function dnn_SegmentationModel(network::dnn_Net) |
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function detect(cobj::dnn_DetectionModel, frame::InputArray, confThreshold::Float32, nmsThreshold::Float32) |
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detect(cobj::dnn_DetectionModel, frame::InputArray; confThreshold::Float32 = Float32(0.5), nmsThreshold::Float32 = Float32(0.0)) = detect(cobj, frame, confThreshold, nmsThreshold) |
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function dnn_DetectionModel(model::String, config::String) |
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dnn_DetectionModel(model::String; config::String = ("")) = dnn_DetectionModel(model, config) |
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function dnn_DetectionModel(network::dnn_Net) |
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function Net_readFromModelOptimizer(xml::String, bin::String) |
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function Net_readFromModelOptimizer(bufferModelConfig::Array{UInt8, 1}, bufferWeights::Array{UInt8, 1}) |
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function readNetFromDarknet(cfgFile::String, darknetModel::String) |
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readNetFromDarknet(cfgFile::String; darknetModel::String = (String())) = readNetFromDarknet(cfgFile, darknetModel) |
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function readNetFromDarknet(bufferCfg::Array{UInt8, 1}, bufferModel::Array{UInt8, 1}) |
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readNetFromDarknet(bufferCfg::Array{UInt8, 1}; bufferModel::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromDarknet(bufferCfg, bufferModel) |
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function readNetFromCaffe(prototxt::String, caffeModel::String) |
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readNetFromCaffe(prototxt::String; caffeModel::String = (String())) = readNetFromCaffe(prototxt, caffeModel) |
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function readNetFromCaffe(bufferProto::Array{UInt8, 1}, bufferModel::Array{UInt8, 1}) |
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readNetFromCaffe(bufferProto::Array{UInt8, 1}; bufferModel::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromCaffe(bufferProto, bufferModel) |
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function readNetFromTensorflow(model::String, config::String) |
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readNetFromTensorflow(model::String; config::String = (String())) = readNetFromTensorflow(model, config) |
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function readNetFromTensorflow(bufferModel::Array{UInt8, 1}, bufferConfig::Array{UInt8, 1}) |
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readNetFromTensorflow(bufferModel::Array{UInt8, 1}; bufferConfig::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromTensorflow(bufferModel, bufferConfig) |
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function readNetFromTorch(model::String, isBinary::Bool, evaluate::Bool) |
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readNetFromTorch(model::String; isBinary::Bool = (true), evaluate::Bool = (true)) = readNetFromTorch(model, isBinary, evaluate) |
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function readNet(model::String, config::String, framework::String) |
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readNet(model::String; config::String = (""), framework::String = ("")) = readNet(model, config, framework) |
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function readNet(framework::String, bufferModel::Array{UInt8, 1}, bufferConfig::Array{UInt8, 1}) |
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readNet(framework::String, bufferModel::Array{UInt8, 1}; bufferConfig::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNet(framework, bufferModel, bufferConfig) |
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function readTorchBlob(filename::String, isBinary::Bool) |
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readTorchBlob(filename::String; isBinary::Bool = (true)) = readTorchBlob(filename, isBinary) |
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function readNetFromModelOptimizer(xml::String, bin::String) |
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function readNetFromModelOptimizer(bufferModelConfig::Array{UInt8, 1}, bufferWeights::Array{UInt8, 1}) |
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function readNetFromONNX(onnxFile::String) |
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function readNetFromONNX(buffer::Array{UInt8, 1}) |
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function readTensorFromONNX(path::String) |
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function blobFromImage(image::InputArray, scalefactor::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool, ddepth::Int32) |
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blobFromImage(image::InputArray; scalefactor::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false), ddepth::Int32 = Int32(CV_32F)) = blobFromImage(image, scalefactor, size, mean, swapRB, crop, ddepth) |
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function blobFromImages(images::Array{InputArray, 1}, scalefactor::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool, ddepth::Int32) |
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blobFromImages(images::Array{InputArray, 1}; scalefactor::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false), ddepth::Int32 = Int32(CV_32F)) = blobFromImages(images, scalefactor, size, mean, swapRB, crop, ddepth) |
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function imagesFromBlob(blob_::InputArray, images_::Array{InputArray, 1}) |
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imagesFromBlob(blob_::InputArray; images_::Array{InputArray, 1} = (Array{InputArray, 1}())) = imagesFromBlob(blob_, images_) |
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function shrinkCaffeModel(src::String, dst::String, layersTypes::Array{String, 1}) |
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shrinkCaffeModel(src::String, dst::String; layersTypes::Array{String, 1} = (stdggvectoriStringkOP())) = shrinkCaffeModel(src, dst, layersTypes) |
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function writeTextGraph(model::String, output::String) |
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function NMSBoxes(bboxes::Array{Rect{Float64}, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32, eta::Float32, top_k::Int32) |
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NMSBoxes(bboxes::Array{Rect{Float64}, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32; eta::Float32 = Float32(1.0), top_k::Int32 = Int32(0)) = NMSBoxes(bboxes, scores, score_threshold, nms_threshold, eta, top_k) |
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function NMSBoxesRotated(bboxes::Array{RotatedRect, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32, eta::Float32, top_k::Int32) |
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NMSBoxesRotated(bboxes::Array{RotatedRect, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32; eta::Float32 = Float32(1.0), top_k::Int32 = Int32(0)) = NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, eta, top_k) |
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``` |
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@ -0,0 +1,435 @@ |
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function Base.getproperty(m::dnn_Layer, s::Symbol) |
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if s==:blobs |
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return cpp_to_julia(jlopencv_Layer_get_blobs(m)) |
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end |
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if s==:name |
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return cpp_to_julia(jlopencv_Layer_get_name(m)) |
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end |
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if s==:type |
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return cpp_to_julia(jlopencv_Layer_get_type(m)) |
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end |
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if s==:preferableTarget |
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return cpp_to_julia(jlopencv_Layer_get_preferableTarget(m)) |
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end |
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return Base.getfield(m, s) |
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end |
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function Base.setproperty!(m::dnn_Layer, s::Symbol, v) |
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if s==:name |
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jlopencv_Layer_set_name(m, julia_to_cpp(v)) |
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end |
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if s==:type |
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jlopencv_Layer_set_type(m, julia_to_cpp(v)) |
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end |
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if s==:preferableTarget |
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jlopencv_Layer_set_preferableTarget(m, julia_to_cpp(v)) |
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end |
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return Base.setfield!(m, s, v) |
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end |
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function finalize(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}, outputs::Array{InputArray, 1}) where {T <: dnn_Layer} |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Layer_cv_dnn_Layer_finalize(julia_to_cpp(cobj),julia_to_cpp(inputs),julia_to_cpp(outputs))) |
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end |
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finalize(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}; outputs::Array{InputArray, 1} = (Array{InputArray, 1}())) where {T <: dnn_Layer} = finalize(cobj, inputs, outputs) |
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# function run(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}, internals::Array{InputArray, 1}, outputs::Array{InputArray, 1}) where {T <: dnn_Layer} |
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# return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Layer_cv_dnn_Layer_run(julia_to_cpp(cobj),julia_to_cpp(inputs),julia_to_cpp(internals),julia_to_cpp(outputs))) |
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# end |
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# run(cobj::cv_Ptr{T}, inputs::Array{InputArray, 1}, internals::Array{InputArray, 1}; outputs::Array{InputArray, 1} = (Array{InputArray, 1}())) where {T <: dnn_Layer} = run(cobj, inputs, internals, outputs) |
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function outputNameToIndex(cobj::cv_Ptr{T}, outputName::String) where {T <: dnn_Layer} |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Layer_cv_dnn_Layer_outputNameToIndex(julia_to_cpp(cobj),julia_to_cpp(outputName))) |
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end |
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function Base.getproperty(m::dnn_Net, s::Symbol) |
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return Base.getfield(m, s) |
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end |
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function Base.setproperty!(m::dnn_Net, s::Symbol, v) |
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return Base.setfield!(m, s, v) |
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end |
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function empty(cobj::dnn_Net) |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_empty(julia_to_cpp(cobj))) |
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end |
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function dump(cobj::dnn_Net) |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_dump(julia_to_cpp(cobj))) |
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end |
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function dumpToFile(cobj::dnn_Net, path::String) |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_dumpToFile(julia_to_cpp(cobj),julia_to_cpp(path))) |
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end |
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function getLayerId(cobj::dnn_Net, layer::String) |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayerId(julia_to_cpp(cobj),julia_to_cpp(layer))) |
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end |
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function getLayerNames(cobj::dnn_Net) |
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return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayerNames(julia_to_cpp(cobj))) |
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end |
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|
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function getLayer(cobj::dnn_Net, layerId::dnn_LayerId) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayer(julia_to_cpp(cobj),julia_to_cpp(layerId))) |
||||
end |
||||
|
||||
function connect(cobj::dnn_Net, outPin::String, inpPin::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_connect(julia_to_cpp(cobj),julia_to_cpp(outPin),julia_to_cpp(inpPin))) |
||||
end |
||||
|
||||
function setInputsNames(cobj::dnn_Net, inputBlobNames::Array{String, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setInputsNames(julia_to_cpp(cobj),julia_to_cpp(inputBlobNames))) |
||||
end |
||||
|
||||
function setInputShape(cobj::dnn_Net, inputName::String, shape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setInputShape(julia_to_cpp(cobj),julia_to_cpp(inputName),julia_to_cpp(shape))) |
||||
end |
||||
|
||||
function forward(cobj::dnn_Net, outputName::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_forward(julia_to_cpp(cobj),julia_to_cpp(outputName))) |
||||
end |
||||
forward(cobj::dnn_Net; outputName::String = (String())) = forward(cobj, outputName) |
||||
|
||||
function forward(cobj::dnn_Net, outputBlobs::Array{InputArray, 1}, outputName::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_forward(julia_to_cpp(cobj),julia_to_cpp(outputBlobs),julia_to_cpp(outputName))) |
||||
end |
||||
|
||||
function forward(cobj::dnn_Net, outBlobNames::Array{String, 1}, outputBlobs::Array{InputArray, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_forward(julia_to_cpp(cobj),julia_to_cpp(outBlobNames),julia_to_cpp(outputBlobs))) |
||||
end |
||||
forward(cobj::dnn_Net, outBlobNames::Array{String, 1}; outputBlobs::Array{InputArray, 1} = (Array{InputArray, 1}())) = forward(cobj, outBlobNames, outputBlobs) |
||||
|
||||
function forwardAsync(cobj::dnn_Net, outputName::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_forwardAsync(julia_to_cpp(cobj),julia_to_cpp(outputName))) |
||||
end |
||||
forwardAsync(cobj::dnn_Net; outputName::String = (String())) = forwardAsync(cobj, outputName) |
||||
|
||||
function setHalideScheduler(cobj::dnn_Net, scheduler::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setHalideScheduler(julia_to_cpp(cobj),julia_to_cpp(scheduler))) |
||||
end |
||||
|
||||
function setPreferableBackend(cobj::dnn_Net, backendId::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setPreferableBackend(julia_to_cpp(cobj),julia_to_cpp(backendId))) |
||||
end |
||||
|
||||
function setPreferableTarget(cobj::dnn_Net, targetId::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setPreferableTarget(julia_to_cpp(cobj),julia_to_cpp(targetId))) |
||||
end |
||||
|
||||
function setInput(cobj::dnn_Net, blob::InputArray, name::String, scalefactor::Float64, mean::Scalar) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setInput(julia_to_cpp(cobj),julia_to_cpp(blob),julia_to_cpp(name),julia_to_cpp(scalefactor),julia_to_cpp(mean))) |
||||
end |
||||
setInput(cobj::dnn_Net, blob::InputArray; name::String = (""), scalefactor::Float64 = Float64(1.0), mean::Scalar = ()) = setInput(cobj, blob, name, scalefactor, mean) |
||||
|
||||
function setParam(cobj::dnn_Net, layer::dnn_LayerId, numParam::Int32, blob::InputArray) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_setParam(julia_to_cpp(cobj),julia_to_cpp(layer),julia_to_cpp(numParam),julia_to_cpp(blob))) |
||||
end |
||||
|
||||
function getParam(cobj::dnn_Net, layer::dnn_LayerId, numParam::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getParam(julia_to_cpp(cobj),julia_to_cpp(layer),julia_to_cpp(numParam))) |
||||
end |
||||
getParam(cobj::dnn_Net, layer::dnn_LayerId; numParam::Int32 = Int32(0)) = getParam(cobj, layer, numParam) |
||||
|
||||
function getUnconnectedOutLayers(cobj::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getUnconnectedOutLayers(julia_to_cpp(cobj))) |
||||
end |
||||
|
||||
function getUnconnectedOutLayersNames(cobj::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getUnconnectedOutLayersNames(julia_to_cpp(cobj))) |
||||
end |
||||
|
||||
function getLayersShapes(cobj::dnn_Net, netInputShapes::Array{Array{Int32, 1}, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayersShapes(julia_to_cpp(cobj),julia_to_cpp(netInputShapes))) |
||||
end |
||||
|
||||
function getLayersShapes(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayersShapes(julia_to_cpp(cobj),julia_to_cpp(netInputShape))) |
||||
end |
||||
|
||||
function getFLOPS(cobj::dnn_Net, netInputShapes::Array{Array{Int32, 1}, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getFLOPS(julia_to_cpp(cobj),julia_to_cpp(netInputShapes))) |
||||
end |
||||
|
||||
function getFLOPS(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getFLOPS(julia_to_cpp(cobj),julia_to_cpp(netInputShape))) |
||||
end |
||||
|
||||
function getFLOPS(cobj::dnn_Net, layerId::Int32, netInputShapes::Array{Array{Int32, 1}, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getFLOPS(julia_to_cpp(cobj),julia_to_cpp(layerId),julia_to_cpp(netInputShapes))) |
||||
end |
||||
|
||||
function getFLOPS(cobj::dnn_Net, layerId::Int32, netInputShape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getFLOPS(julia_to_cpp(cobj),julia_to_cpp(layerId),julia_to_cpp(netInputShape))) |
||||
end |
||||
|
||||
function getLayerTypes(cobj::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayerTypes(julia_to_cpp(cobj))) |
||||
end |
||||
|
||||
function getLayersCount(cobj::dnn_Net, layerType::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getLayersCount(julia_to_cpp(cobj),julia_to_cpp(layerType))) |
||||
end |
||||
|
||||
function getMemoryConsumption(cobj::dnn_Net, netInputShape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getMemoryConsumption(julia_to_cpp(cobj),julia_to_cpp(netInputShape))) |
||||
end |
||||
|
||||
function getMemoryConsumption(cobj::dnn_Net, layerId::Int32, netInputShapes::Array{Array{Int32, 1}, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getMemoryConsumption(julia_to_cpp(cobj),julia_to_cpp(layerId),julia_to_cpp(netInputShapes))) |
||||
end |
||||
|
||||
function getMemoryConsumption(cobj::dnn_Net, layerId::Int32, netInputShape::Array{Int32, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getMemoryConsumption(julia_to_cpp(cobj),julia_to_cpp(layerId),julia_to_cpp(netInputShape))) |
||||
end |
||||
|
||||
function enableFusion(cobj::dnn_Net, fusion::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_enableFusion(julia_to_cpp(cobj),julia_to_cpp(fusion))) |
||||
end |
||||
|
||||
function getPerfProfile(cobj::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_cv_dnn_Net_getPerfProfile(julia_to_cpp(cobj))) |
||||
end |
||||
|
||||
function Base.getproperty(m::dnn_Model, s::Symbol) |
||||
return Base.getfield(m, s) |
||||
end |
||||
function Base.setproperty!(m::dnn_Model, s::Symbol, v) |
||||
return Base.setfield!(m, s, v) |
||||
end |
||||
|
||||
function setInputSize(cobj::dnn_Model, size::Size) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputSize(julia_to_cpp(cobj),julia_to_cpp(size))) |
||||
end |
||||
|
||||
function setInputSize(cobj::dnn_Model, width::Int32, height::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputSize(julia_to_cpp(cobj),julia_to_cpp(width),julia_to_cpp(height))) |
||||
end |
||||
|
||||
function setInputMean(cobj::dnn_Model, mean::Scalar) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputMean(julia_to_cpp(cobj),julia_to_cpp(mean))) |
||||
end |
||||
|
||||
function setInputScale(cobj::dnn_Model, scale::Float64) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputScale(julia_to_cpp(cobj),julia_to_cpp(scale))) |
||||
end |
||||
|
||||
function setInputCrop(cobj::dnn_Model, crop::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputCrop(julia_to_cpp(cobj),julia_to_cpp(crop))) |
||||
end |
||||
|
||||
function setInputSwapRB(cobj::dnn_Model, swapRB::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputSwapRB(julia_to_cpp(cobj),julia_to_cpp(swapRB))) |
||||
end |
||||
|
||||
function setInputParams(cobj::dnn_Model, scale::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_setInputParams(julia_to_cpp(cobj),julia_to_cpp(scale),julia_to_cpp(size),julia_to_cpp(mean),julia_to_cpp(swapRB),julia_to_cpp(crop))) |
||||
end |
||||
setInputParams(cobj::dnn_Model; scale::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false)) = setInputParams(cobj, scale, size, mean, swapRB, crop) |
||||
|
||||
function predict(cobj::dnn_Model, frame::InputArray, outs::Array{InputArray, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_predict(julia_to_cpp(cobj),julia_to_cpp(frame),julia_to_cpp(outs))) |
||||
end |
||||
predict(cobj::dnn_Model, frame::InputArray; outs::Array{InputArray, 1} = (Array{InputArray, 1}())) = predict(cobj, frame, outs) |
||||
|
||||
function dnn_Model(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_Model(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
dnn_Model(model::String; config::String = ("")) = dnn_Model(model, config) |
||||
|
||||
function dnn_Model(network::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Model_cv_dnn_Model_Model(julia_to_cpp(network))) |
||||
end |
||||
function Base.getproperty(m::dnn_ClassificationModel, s::Symbol) |
||||
return Base.getfield(m, s) |
||||
end |
||||
function Base.setproperty!(m::dnn_ClassificationModel, s::Symbol, v) |
||||
return Base.setfield!(m, s, v) |
||||
end |
||||
|
||||
function classify(cobj::dnn_ClassificationModel, frame::InputArray) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_ClassificationModel_cv_dnn_ClassificationModel_classify(julia_to_cpp(cobj),julia_to_cpp(frame))) |
||||
end |
||||
|
||||
function dnn_ClassificationModel(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_ClassificationModel_cv_dnn_ClassificationModel_ClassificationModel(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
dnn_ClassificationModel(model::String; config::String = ("")) = dnn_ClassificationModel(model, config) |
||||
|
||||
function dnn_ClassificationModel(network::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_ClassificationModel_cv_dnn_ClassificationModel_ClassificationModel(julia_to_cpp(network))) |
||||
end |
||||
function Base.getproperty(m::dnn_KeypointsModel, s::Symbol) |
||||
return Base.getfield(m, s) |
||||
end |
||||
function Base.setproperty!(m::dnn_KeypointsModel, s::Symbol, v) |
||||
return Base.setfield!(m, s, v) |
||||
end |
||||
|
||||
function estimate(cobj::dnn_KeypointsModel, frame::InputArray, thresh::Float32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_KeypointsModel_cv_dnn_KeypointsModel_estimate(julia_to_cpp(cobj),julia_to_cpp(frame),julia_to_cpp(thresh))) |
||||
end |
||||
estimate(cobj::dnn_KeypointsModel, frame::InputArray; thresh::Float32 = Float32(0.5)) = estimate(cobj, frame, thresh) |
||||
|
||||
function dnn_KeypointsModel(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_KeypointsModel_cv_dnn_KeypointsModel_KeypointsModel(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
dnn_KeypointsModel(model::String; config::String = ("")) = dnn_KeypointsModel(model, config) |
||||
|
||||
function dnn_KeypointsModel(network::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_KeypointsModel_cv_dnn_KeypointsModel_KeypointsModel(julia_to_cpp(network))) |
||||
end |
||||
function Base.getproperty(m::dnn_SegmentationModel, s::Symbol) |
||||
return Base.getfield(m, s) |
||||
end |
||||
function Base.setproperty!(m::dnn_SegmentationModel, s::Symbol, v) |
||||
return Base.setfield!(m, s, v) |
||||
end |
||||
|
||||
function segment(cobj::dnn_SegmentationModel, frame::InputArray, mask::InputArray) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_SegmentationModel_cv_dnn_SegmentationModel_segment(julia_to_cpp(cobj),julia_to_cpp(frame),julia_to_cpp(mask))) |
||||
end |
||||
segment(cobj::dnn_SegmentationModel, frame::InputArray; mask::InputArray = (CxxMat())) = segment(cobj, frame, mask) |
||||
|
||||
function dnn_SegmentationModel(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_SegmentationModel_cv_dnn_SegmentationModel_SegmentationModel(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
dnn_SegmentationModel(model::String; config::String = ("")) = dnn_SegmentationModel(model, config) |
||||
|
||||
function dnn_SegmentationModel(network::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_SegmentationModel_cv_dnn_SegmentationModel_SegmentationModel(julia_to_cpp(network))) |
||||
end |
||||
function Base.getproperty(m::dnn_DetectionModel, s::Symbol) |
||||
return Base.getfield(m, s) |
||||
end |
||||
function Base.setproperty!(m::dnn_DetectionModel, s::Symbol, v) |
||||
return Base.setfield!(m, s, v) |
||||
end |
||||
|
||||
function detect(cobj::dnn_DetectionModel, frame::InputArray, confThreshold::Float32, nmsThreshold::Float32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_DetectionModel_cv_dnn_DetectionModel_detect(julia_to_cpp(cobj),julia_to_cpp(frame),julia_to_cpp(confThreshold),julia_to_cpp(nmsThreshold))) |
||||
end |
||||
detect(cobj::dnn_DetectionModel, frame::InputArray; confThreshold::Float32 = Float32(0.5), nmsThreshold::Float32 = Float32(0.0)) = detect(cobj, frame, confThreshold, nmsThreshold) |
||||
|
||||
function dnn_DetectionModel(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_DetectionModel_cv_dnn_DetectionModel_DetectionModel(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
dnn_DetectionModel(model::String; config::String = ("")) = dnn_DetectionModel(model, config) |
||||
|
||||
function dnn_DetectionModel(network::dnn_Net) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_DetectionModel_cv_dnn_DetectionModel_DetectionModel(julia_to_cpp(network))) |
||||
end |
||||
|
||||
# function getAvailableTargets(be::dnn_Backend) |
||||
# return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_getAvailableTargets(julia_to_cpp(be))) |
||||
# end |
||||
|
||||
function Net_readFromModelOptimizer(xml::String, bin::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_readFromModelOptimizer(julia_to_cpp(xml),julia_to_cpp(bin))) |
||||
end |
||||
|
||||
function Net_readFromModelOptimizer(bufferModelConfig::Array{UInt8, 1}, bufferWeights::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_Net_readFromModelOptimizer(julia_to_cpp(bufferModelConfig),julia_to_cpp(bufferWeights))) |
||||
end |
||||
|
||||
function readNetFromDarknet(cfgFile::String, darknetModel::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromDarknet(julia_to_cpp(cfgFile),julia_to_cpp(darknetModel))) |
||||
end |
||||
readNetFromDarknet(cfgFile::String; darknetModel::String = (String())) = readNetFromDarknet(cfgFile, darknetModel) |
||||
|
||||
function readNetFromDarknet(bufferCfg::Array{UInt8, 1}, bufferModel::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromDarknet(julia_to_cpp(bufferCfg),julia_to_cpp(bufferModel))) |
||||
end |
||||
readNetFromDarknet(bufferCfg::Array{UInt8, 1}; bufferModel::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromDarknet(bufferCfg, bufferModel) |
||||
|
||||
function readNetFromCaffe(prototxt::String, caffeModel::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromCaffe(julia_to_cpp(prototxt),julia_to_cpp(caffeModel))) |
||||
end |
||||
readNetFromCaffe(prototxt::String; caffeModel::String = (String())) = readNetFromCaffe(prototxt, caffeModel) |
||||
|
||||
function readNetFromCaffe(bufferProto::Array{UInt8, 1}, bufferModel::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromCaffe(julia_to_cpp(bufferProto),julia_to_cpp(bufferModel))) |
||||
end |
||||
readNetFromCaffe(bufferProto::Array{UInt8, 1}; bufferModel::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromCaffe(bufferProto, bufferModel) |
||||
|
||||
function readNetFromTensorflow(model::String, config::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromTensorflow(julia_to_cpp(model),julia_to_cpp(config))) |
||||
end |
||||
readNetFromTensorflow(model::String; config::String = (String())) = readNetFromTensorflow(model, config) |
||||
|
||||
function readNetFromTensorflow(bufferModel::Array{UInt8, 1}, bufferConfig::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromTensorflow(julia_to_cpp(bufferModel),julia_to_cpp(bufferConfig))) |
||||
end |
||||
readNetFromTensorflow(bufferModel::Array{UInt8, 1}; bufferConfig::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNetFromTensorflow(bufferModel, bufferConfig) |
||||
|
||||
function readNetFromTorch(model::String, isBinary::Bool, evaluate::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromTorch(julia_to_cpp(model),julia_to_cpp(isBinary),julia_to_cpp(evaluate))) |
||||
end |
||||
readNetFromTorch(model::String; isBinary::Bool = (true), evaluate::Bool = (true)) = readNetFromTorch(model, isBinary, evaluate) |
||||
|
||||
function readNet(model::String, config::String, framework::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNet(julia_to_cpp(model),julia_to_cpp(config),julia_to_cpp(framework))) |
||||
end |
||||
readNet(model::String; config::String = (""), framework::String = ("")) = readNet(model, config, framework) |
||||
|
||||
function readNet(framework::String, bufferModel::Array{UInt8, 1}, bufferConfig::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNet(julia_to_cpp(framework),julia_to_cpp(bufferModel),julia_to_cpp(bufferConfig))) |
||||
end |
||||
readNet(framework::String, bufferModel::Array{UInt8, 1}; bufferConfig::Array{UInt8, 1} = (stdggvectoriUInt8kOP())) = readNet(framework, bufferModel, bufferConfig) |
||||
|
||||
function readTorchBlob(filename::String, isBinary::Bool) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readTorchBlob(julia_to_cpp(filename),julia_to_cpp(isBinary))) |
||||
end |
||||
readTorchBlob(filename::String; isBinary::Bool = (true)) = readTorchBlob(filename, isBinary) |
||||
|
||||
function readNetFromModelOptimizer(xml::String, bin::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromModelOptimizer(julia_to_cpp(xml),julia_to_cpp(bin))) |
||||
end |
||||
|
||||
function readNetFromModelOptimizer(bufferModelConfig::Array{UInt8, 1}, bufferWeights::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromModelOptimizer(julia_to_cpp(bufferModelConfig),julia_to_cpp(bufferWeights))) |
||||
end |
||||
|
||||
function readNetFromONNX(onnxFile::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromONNX(julia_to_cpp(onnxFile))) |
||||
end |
||||
|
||||
function readNetFromONNX(buffer::Array{UInt8, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readNetFromONNX(julia_to_cpp(buffer))) |
||||
end |
||||
|
||||
function readTensorFromONNX(path::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_readTensorFromONNX(julia_to_cpp(path))) |
||||
end |
||||
|
||||
function blobFromImage(image::InputArray, scalefactor::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool, ddepth::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_blobFromImage(julia_to_cpp(image),julia_to_cpp(scalefactor),julia_to_cpp(size),julia_to_cpp(mean),julia_to_cpp(swapRB),julia_to_cpp(crop),julia_to_cpp(ddepth))) |
||||
end |
||||
blobFromImage(image::InputArray; scalefactor::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false), ddepth::Int32 = Int32(CV_32F)) = blobFromImage(image, scalefactor, size, mean, swapRB, crop, ddepth) |
||||
|
||||
function blobFromImages(images::Array{InputArray, 1}, scalefactor::Float64, size::Size, mean::Scalar, swapRB::Bool, crop::Bool, ddepth::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_blobFromImages(julia_to_cpp(images),julia_to_cpp(scalefactor),julia_to_cpp(size),julia_to_cpp(mean),julia_to_cpp(swapRB),julia_to_cpp(crop),julia_to_cpp(ddepth))) |
||||
end |
||||
blobFromImages(images::Array{InputArray, 1}; scalefactor::Float64 = Float64(1.0), size::Size = (SizeOP()), mean::Scalar = (), swapRB::Bool = (false), crop::Bool = (false), ddepth::Int32 = Int32(CV_32F)) = blobFromImages(images, scalefactor, size, mean, swapRB, crop, ddepth) |
||||
|
||||
function imagesFromBlob(blob_::InputArray, images_::Array{InputArray, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_imagesFromBlob(julia_to_cpp(blob_),julia_to_cpp(images_))) |
||||
end |
||||
imagesFromBlob(blob_::InputArray; images_::Array{InputArray, 1} = (Array{InputArray, 1}())) = imagesFromBlob(blob_, images_) |
||||
|
||||
function shrinkCaffeModel(src::String, dst::String, layersTypes::Array{String, 1}) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_shrinkCaffeModel(julia_to_cpp(src),julia_to_cpp(dst),julia_to_cpp(layersTypes))) |
||||
end |
||||
shrinkCaffeModel(src::String, dst::String; layersTypes::Array{String, 1} = (stdggvectoriStringkOP())) = shrinkCaffeModel(src, dst, layersTypes) |
||||
|
||||
function writeTextGraph(model::String, output::String) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_writeTextGraph(julia_to_cpp(model),julia_to_cpp(output))) |
||||
end |
||||
|
||||
function NMSBoxes(bboxes::Array{Rect{Float64}, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32, eta::Float32, top_k::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_NMSBoxes(julia_to_cpp(bboxes),julia_to_cpp(scores),julia_to_cpp(score_threshold),julia_to_cpp(nms_threshold),julia_to_cpp(eta),julia_to_cpp(top_k))) |
||||
end |
||||
NMSBoxes(bboxes::Array{Rect{Float64}, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32; eta::Float32 = Float32(1.0), top_k::Int32 = Int32(0)) = NMSBoxes(bboxes, scores, score_threshold, nms_threshold, eta, top_k) |
||||
|
||||
function NMSBoxesRotated(bboxes::Array{RotatedRect, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32, eta::Float32, top_k::Int32) |
||||
return cpp_to_julia(jlopencv_cv_dnn_cv_dnn_NMSBoxes(julia_to_cpp(bboxes),julia_to_cpp(scores),julia_to_cpp(score_threshold),julia_to_cpp(nms_threshold),julia_to_cpp(eta),julia_to_cpp(top_k))) |
||||
end |
||||
NMSBoxesRotated(bboxes::Array{RotatedRect, 1}, scores::Array{Float32, 1}, score_threshold::Float32, nms_threshold::Float32; eta::Float32 = Float32(1.0), top_k::Int32 = Int32(0)) = NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, eta, top_k) |
||||
@ -0,0 +1,36 @@ |
||||
using OpenCV |
||||
const cv = OpenCV |
||||
size0 = Int32(300) |
||||
# take the model from https://github.com/opencv/opencv_extra/tree/master/testdata/dnn |
||||
net = cv.dnn_DetectionModel("opencv_face_detector.pbtxt", "opencv_face_detector_uint8.pb") |
||||
|
||||
cv.setPreferableTarget(net, cv.DNN_TARGET_CPU) |
||||
cv.setInputMean(net, (104, 177, 123)) |
||||
cv.setInputScale(net, 1.) |
||||
cv.setInputSize(net, size0, size0) |
||||
|
||||
cap = cv.VideoCapture(Int32(0)) |
||||
while true |
||||
ok, frame = cv.read(cap) |
||||
if ok == false |
||||
break |
||||
end |
||||
classIds, confidences, boxes = cv.detect(net, frame, confThreshold=Float32(0.5)) |
||||
for i in 1:size(boxes,1) |
||||
confidence = confidences[i] |
||||
x0 = Int32(boxes[i].x) |
||||
y0 = Int32(boxes[i].y) |
||||
x1 = Int32(boxes[i].x+boxes[i].width) |
||||
y1 = Int32(boxes[i].y+boxes[i].height) |
||||
cv.rectangle(frame, cv.Point{Int32}(x0, y0), cv.Point{Int32}(x1, y1), (100, 255, 100); thickness = Int32(5)) |
||||
label = "face: " * string(confidence) |
||||
lsize, bl = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, Int32(1)) |
||||
cv.rectangle(frame, cv.Point{Int32}(x0,y0), cv.Point{Int32}(x0+lsize.width, y0+lsize.height+bl), (100,255,100); thickness = Int32(-1)) |
||||
cv.putText(frame, label, cv.Point{Int32}(x0, y0 + lsize.height), |
||||
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0); thickness = Int32(1), lineType = cv.LINE_AA) |
||||
end |
||||
cv.imshow("detections", frame) |
||||
if cv.waitKey(Int32(30)) >= 0 |
||||
break |
||||
end |
||||
end |
||||
@ -0,0 +1,33 @@ |
||||
function IOU(boxA, boxB) |
||||
xA = max(boxA[1], boxB[1]) |
||||
yA = max(boxA[2], boxB[2]) |
||||
xB = min(boxA[3], boxB[3]) |
||||
yB = min(boxA[4], boxB[4]) |
||||
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1) |
||||
boxAArea = (boxA[3] - boxA[1] + 1) * (boxA[4] - boxA[2] + 1) |
||||
boxBArea = (boxB[3] - boxB[1] + 1) * (boxB[4] - boxB[2] + 1) |
||||
iou = interArea / float(boxAArea + boxBArea - interArea) |
||||
return iou |
||||
end |
||||
|
||||
const cv = OpenCV |
||||
|
||||
net = cv.dnn_DetectionModel(joinpath(ENV["OPENCV_TEST_DATA_PATH"], "dnn", "opencv_face_detector.pbtxt"),joinpath(ENV["OPENCV_TEST_DATA_PATH"], "dnn", "opencv_face_detector_uint8.pb")) |
||||
size0 = Int32(300) |
||||
|
||||
cv.setPreferableTarget(net, cv.DNN_TARGET_CPU) |
||||
cv.setInputMean(net, (104, 177, 123)) |
||||
cv.setInputScale(net, 1.) |
||||
cv.setInputSize(net, size0, size0) |
||||
|
||||
|
||||
img = OpenCV.imread(joinpath(test_dir, "cascadeandhog", "images", "mona-lisa.png")) |
||||
|
||||
classIds, confidences, boxes = cv.detect(net, img, confThreshold=Float32(0.5)) |
||||
|
||||
box = (boxes[1].x, boxes[1].y, boxes[1].x+boxes[1].width, boxes[1].y+boxes[1].height) |
||||
expected_rect = (185,101,129+185,169+101) |
||||
|
||||
@test IOU(box, expected_rect) > 0.8 |
||||
|
||||
print("dnn test passed\n") |
||||
@ -0,0 +1,118 @@ |
||||
# This file is adapted from test/abstractarray.jl from Julia. |
||||
|
||||
# This file is a part of Julia. License is MIT: https://julialang.org/license |
||||
|
||||
A_abs = rand(5,4,3) |
||||
A = OpenCV.Mat(A_abs) |
||||
@testset "Bounds checking" begin |
||||
@test checkbounds(Bool, A, 1, 1, 1) == true |
||||
@test checkbounds(Bool, A, 5, 4, 3) == true |
||||
@test checkbounds(Bool, A, 0, 1, 1) == false |
||||
@test checkbounds(Bool, A, 1, 0, 1) == false |
||||
@test checkbounds(Bool, A, 1, 1, 0) == false |
||||
@test checkbounds(Bool, A, 6, 4, 3) == false |
||||
@test checkbounds(Bool, A, 5, 5, 3) == false |
||||
@test checkbounds(Bool, A, 5, 4, 4) == false |
||||
@test checkbounds(Bool, A, 1) == true # linear indexing |
||||
@test checkbounds(Bool, A, 60) == true |
||||
@test checkbounds(Bool, A, 61) == false |
||||
@test checkbounds(Bool, A, 2, 2, 2, 1) == true # extra indices |
||||
@test checkbounds(Bool, A, 2, 2, 2, 2) == false |
||||
@test checkbounds(Bool, A, 1, 1) == false |
||||
@test checkbounds(Bool, A, 1, 12) == false |
||||
@test checkbounds(Bool, A, 5, 12) == false |
||||
@test checkbounds(Bool, A, 1, 13) == false |
||||
@test checkbounds(Bool, A, 6, 12) == false |
||||
end |
||||
|
||||
@testset "single CartesianIndex" begin |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 1, 1))) == true |
||||
@test checkbounds(Bool, A, CartesianIndex((5, 4, 3))) == true |
||||
@test checkbounds(Bool, A, CartesianIndex((0, 1, 1))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 0, 1))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 1, 0))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((6, 4, 3))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((5, 5, 3))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((5, 4, 4))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((60,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((61,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((2, 2, 2, 1,))) == true |
||||
@test checkbounds(Bool, A, CartesianIndex((2, 2, 2, 2,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 1,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 12,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((5, 12,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((1, 13,))) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((6, 12,))) == false |
||||
end |
||||
|
||||
@testset "mix of CartesianIndex and Int" begin |
||||
@test checkbounds(Bool, A, CartesianIndex((1,)), 1, CartesianIndex((1,))) == true |
||||
@test checkbounds(Bool, A, CartesianIndex((5, 4)), 3) == true |
||||
@test checkbounds(Bool, A, CartesianIndex((0, 1)), 1) == false |
||||
@test checkbounds(Bool, A, 1, CartesianIndex((0, 1))) == false |
||||
@test checkbounds(Bool, A, 1, 1, CartesianIndex((0,))) == false |
||||
@test checkbounds(Bool, A, 6, CartesianIndex((4, 3))) == false |
||||
@test checkbounds(Bool, A, 5, CartesianIndex((5,)), 3) == false |
||||
@test checkbounds(Bool, A, CartesianIndex((5,)), CartesianIndex((4,)), CartesianIndex((4,))) == false |
||||
end |
||||
|
||||
@testset "vector indices" begin |
||||
@test checkbounds(Bool, A, 1:5, 1:4, 1:3) == true |
||||
@test checkbounds(Bool, A, 0:5, 1:4, 1:3) == false |
||||
@test checkbounds(Bool, A, 1:5, 0:4, 1:3) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:4, 0:3) == false |
||||
@test checkbounds(Bool, A, 1:6, 1:4, 1:3) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:5, 1:3) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:4, 1:4) == false |
||||
@test checkbounds(Bool, A, 1:60) == true |
||||
@test checkbounds(Bool, A, 1:61) == false |
||||
@test checkbounds(Bool, A, 2, 2, 2, 1:1) == true # extra indices |
||||
@test checkbounds(Bool, A, 2, 2, 2, 1:2) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:4) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:12) == false |
||||
@test checkbounds(Bool, A, 1:5, 1:13) == false |
||||
@test checkbounds(Bool, A, 1:6, 1:12) == false |
||||
end |
||||
|
||||
@testset "logical" begin |
||||
@test checkbounds(Bool, A, trues(5), trues(4), trues(3)) == true |
||||
@test checkbounds(Bool, A, trues(6), trues(4), trues(3)) == false |
||||
@test checkbounds(Bool, A, trues(5), trues(5), trues(3)) == false |
||||
@test checkbounds(Bool, A, trues(5), trues(4), trues(4)) == false |
||||
@test checkbounds(Bool, A, trues(60)) == true |
||||
@test checkbounds(Bool, A, trues(61)) == false |
||||
@test checkbounds(Bool, A, 2, 2, 2, trues(1)) == true # extra indices |
||||
@test checkbounds(Bool, A, 2, 2, 2, trues(2)) == false |
||||
@test checkbounds(Bool, A, trues(5), trues(12)) == false |
||||
@test checkbounds(Bool, A, trues(5), trues(13)) == false |
||||
@test checkbounds(Bool, A, trues(6), trues(12)) == false |
||||
@test checkbounds(Bool, A, trues(5, 4, 3)) == true |
||||
@test checkbounds(Bool, A, trues(5, 4, 2)) == false |
||||
@test checkbounds(Bool, A, trues(5, 12)) == false |
||||
@test checkbounds(Bool, A, trues(1, 5), trues(1, 4, 1), trues(1, 1, 3)) == false |
||||
@test checkbounds(Bool, A, trues(1, 5), trues(1, 4, 1), trues(1, 1, 2)) == false |
||||
@test checkbounds(Bool, A, trues(1, 5), trues(1, 5, 1), trues(1, 1, 3)) == false |
||||
@test checkbounds(Bool, A, trues(1, 5), :, 2) == false |
||||
end |
||||
|
||||
@testset "array of CartesianIndex" begin |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 1, 1))]) == true |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 4, 3))]) == true |
||||
@test checkbounds(Bool, A, [CartesianIndex((0, 1, 1))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 0, 1))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 1, 0))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((6, 4, 3))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 5, 3))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 4, 4))]) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 1))], 1) == true |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 4))], 3) == true |
||||
@test checkbounds(Bool, A, [CartesianIndex((0, 1))], 1) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 0))], 1) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((1, 1))], 0) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((6, 4))], 3) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 5))], 3) == false |
||||
@test checkbounds(Bool, A, [CartesianIndex((5, 4))], 4) == false |
||||
end |
||||
|
||||
println("OpenCV.Mat tests passed") |
||||
Loading…
Reference in new issue