40 : _memory_group(
std::move(memory_manager)),
51 _non_maxima_suppression(false),
52 _num_orient_bin_kernel(0),
53 _num_block_norm_kernel(0),
54 _num_hog_detect_kernel(0)
69 const size_t num_models = multi_hog->
num_models();
87 std::vector<size_t> input_orient_bin;
88 std::vector<size_t> input_hog_detect;
89 std::vector<std::pair<size_t, size_t>> input_block_norm;
91 input_orient_bin.push_back(0);
92 input_hog_detect.push_back(0);
93 input_block_norm.emplace_back(0, 0);
95 for(
size_t i = 1; i < num_models; ++i)
102 if((cur_num_bins != prev_num_bins) || (cur_cell_size.
width != prev_cell_size.
width) || (cur_cell_size.
height != prev_cell_size.
height))
104 prev_num_bins = cur_num_bins;
105 prev_cell_size = cur_cell_size;
106 prev_block_size = cur_block_size;
107 prev_block_stride = cur_block_stride;
110 input_orient_bin.push_back(i);
111 input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
113 else if((cur_block_size.
width != prev_block_size.
width) || (cur_block_size.
height != prev_block_size.
height) || (cur_block_stride.
width != prev_block_stride.
width)
114 || (cur_block_stride.
height != prev_block_stride.
height))
116 prev_block_size = cur_block_size;
117 prev_block_stride = cur_block_stride;
120 input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
124 input_hog_detect.push_back(input_block_norm.size() - 1);
127 _detection_windows = detection_windows;
129 _num_orient_bin_kernel = input_orient_bin.size();
130 _num_block_norm_kernel = input_block_norm.size();
131 _num_hog_detect_kernel = input_hog_detect.size();
133 _orient_bin_kernel.clear();
134 _block_norm_kernel.clear();
135 _hog_detect_kernel.clear();
137 _hog_norm_space.clear();
139 _orient_bin_kernel.resize(_num_orient_bin_kernel);
140 _block_norm_kernel.resize(_num_block_norm_kernel);
141 _hog_detect_kernel.resize(_num_hog_detect_kernel);
142 _hog_space.resize(_num_orient_bin_kernel);
143 _hog_norm_space.resize(_num_block_norm_kernel);
154 _memory_group.
manage(&_mag);
155 _memory_group.
manage(&_phase);
158 _gradient_kernel.
configure(input, &_mag, &_phase, phase_type, border_mode, constant_border_value);
161 for(
size_t i = 0; i < _num_orient_bin_kernel; ++i)
163 const size_t idx_multi_hog = input_orient_bin[i];
170 const size_t num_cells_x = width / cell.
width;
171 const size_t num_cells_y = height / cell.
height;
180 _hog_space[i].allocator()->init(info_space);
183 _memory_group.
manage(&_hog_space[i]);
186 _orient_bin_kernel[i].configure(&_mag, &_phase, &_hog_space[i], multi_hog->
model(idx_multi_hog)->
info());
194 for(
size_t i = 0; i < _num_block_norm_kernel; ++i)
196 const size_t idx_multi_hog = input_block_norm[i].first;
197 const size_t idx_orient_bin = input_block_norm[i].second;
201 _hog_norm_space[i].allocator()->init(tensor_info);
204 _memory_group.
manage(&_hog_norm_space[i]);
207 _block_norm_kernel[i].configure(&_hog_space[idx_orient_bin], &_hog_norm_space[i], multi_hog->
model(idx_multi_hog)->
info());
211 for(
size_t i = 0; i < _num_orient_bin_kernel; ++i)
213 _hog_space[i].allocator()->allocate();
217 for(
size_t i = 0; i < _num_hog_detect_kernel; ++i)
219 const size_t idx_block_norm = input_hog_detect[i];
221 _hog_detect_kernel[i].configure(&_hog_norm_space[idx_block_norm], multi_hog->
model(i), detection_windows, detection_window_strides->
at(i),
threshold, i);
225 _non_maxima_kernel.
configure(_detection_windows, min_distance);
228 for(
size_t i = 0; i < _num_block_norm_kernel; ++i)
230 _hog_norm_space[i].allocator()->allocate();
241 _detection_windows->
clear();
244 _gradient_kernel.
run();
247 for(
auto &kernel : _orient_bin_kernel)
253 for(
auto &kernel : _block_norm_kernel)
259 for(
auto &kernel : _hog_detect_kernel)
265 if(_non_maxima_suppression)
BorderMode
Methods available to handle borders.
size_t num_bins() const
The number of histogram bins for each cell.
void clear()
Clear all the points from the array.
void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)
Shares the same backing memory with another tensor allocator, while the tensor info might be differen...
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
const Size2D & cell_size() const
The cell size in pixels.
PhaseType
Phase calculation type.
1 channel, 1 U8 per channel
~NEHOGMultiDetection()
Default destructor.
1 channel, 1 F32 per channel
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
PhaseType phase_type() const
The type of PhaseType.
Interface for Neon tensor.
const Size2D & block_stride() const
The block stride in pixels.
Copyright (c) 2017-2021 Arm Limited.
size_t height
Height of the image region or rectangle.
void run() override
Run the kernels contained in the function.
TensorAllocator * allocator()
Return a pointer to the tensor's allocator.
void configure(ITensor *input, ITensor *output_magnitude, ITensor *output_phase, PhaseType phase_type, BorderMode border_mode, uint8_t constant_border_value=0)
Initialise the function's source, destinations, phase type and border mode.
virtual size_t num_models() const =0
The number of HOG models stored.
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
#define ARM_COMPUTE_ERROR_ON_INVALID_MULTI_HOG(m)
size_t num_values() const
Number of values currently stored in the array.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)
const Size2D & block_size() const
The block size in pixels.
void allocate() override
Allocate size specified by TensorInfo of CPU memory.
void configure(IDetectionWindowArray *input_output, float min_distance)
Initialise the kernel's input, output and the euclidean minimum distance.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor's metadata.
void run() override
Run the kernels contained in the function.
1 channel, 1 S16 per channel
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
CPP kernel to perform in-place computation of euclidean distance on IDetectionWindowArray.
void configure(ITensor *input, const IMultiHOG *multi_hog, IDetectionWindowArray *detection_windows, const ISize2DArray *detection_window_strides, BorderMode border_mode, uint8_t constant_border_value=0, float threshold=0.0f, bool non_maxima_suppression=false, float min_distance=1.0f)
Initialise the function's source, destination, detection window strides, border mode, threshold and non-maxima suppression.
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
virtual IHOG * model(size_t index)=0
Return a pointer to the requested HOG model.
Memory group resources scope handling class.
virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0
Runs the kernel in the same thread as the caller synchronously.
size_t width
Width of the image region or rectangle.
Interface for storing multiple HOG data-objects.
SimpleTensor< T > non_maxima_suppression(const SimpleTensor< T > &src, BorderMode border_mode, T constant_border_value)
Class for specifying the size of an image or rectangle.
Store the tensor's metadata.
NEHOGMultiDetection(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
virtual const HOGInfo * info() const =0
Interface to be implemented by the child class to return the HOG's metadata.
SimpleTensor< T > threshold(const SimpleTensor< T > &src, T threshold, T false_value, T true_value, ThresholdType type, T upper)
TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true, bool increase_dim_unit=true)
Accessor to set the value of one of the dimensions.
virtual T & at(size_t index) const
Reference to the element of the array located at the given index.
static IScheduler & get()
Access the scheduler singleton.