Compute Library
 20.02.1
NEHOGMultiDetection.cpp
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25 
26 #include "arm_compute/core/Error.h"
32 
33 using namespace arm_compute;
34 
35 NEHOGMultiDetection::NEHOGMultiDetection(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
36  : _memory_group(std::move(memory_manager)),
37  _gradient_kernel(),
38  _orient_bin_kernel(),
39  _block_norm_kernel(),
40  _hog_detect_kernel(),
41  _non_maxima_kernel(),
42  _hog_space(),
43  _hog_norm_space(),
44  _detection_windows(),
45  _mag(),
46  _phase(),
47  _non_maxima_suppression(false),
48  _num_orient_bin_kernel(0),
49  _num_block_norm_kernel(0),
50  _num_hog_detect_kernel(0)
51 {
52 }
53 
54 void NEHOGMultiDetection::configure(ITensor *input, const IMultiHOG *multi_hog, IDetectionWindowArray *detection_windows, const ISize2DArray *detection_window_strides, BorderMode border_mode,
55  uint8_t constant_border_value, float threshold, bool non_maxima_suppression, float min_distance)
56 {
59  ARM_COMPUTE_ERROR_ON(nullptr == detection_windows);
60  ARM_COMPUTE_ERROR_ON(detection_window_strides->num_values() != multi_hog->num_models());
61 
62  const size_t width = input->info()->dimension(Window::DimX);
63  const size_t height = input->info()->dimension(Window::DimY);
64  const TensorShape &shape_img = input->info()->tensor_shape();
65  const size_t num_models = multi_hog->num_models();
66  PhaseType phase_type = multi_hog->model(0)->info()->phase_type();
67 
68  size_t prev_num_bins = multi_hog->model(0)->info()->num_bins();
69  Size2D prev_cell_size = multi_hog->model(0)->info()->cell_size();
70  Size2D prev_block_size = multi_hog->model(0)->info()->block_size();
71  Size2D prev_block_stride = multi_hog->model(0)->info()->block_stride();
72 
73  /* Check if NEHOGOrientationBinningKernel and NEHOGBlockNormalizationKernel kernels can be skipped for a specific HOG data-object
74  *
75  * 1) NEHOGOrientationBinningKernel and NEHOGBlockNormalizationKernel are skipped if the cell size and the number of bins don't change.
76  * Since "multi_hog" is sorted,it is enough to check the HOG descriptors at level "ith" and level "(i-1)th
77  * 2) NEHOGBlockNormalizationKernel is skipped if the cell size, the number of bins and block size do not change.
78  * Since "multi_hog" is sorted,it is enough to check the HOG descriptors at level "ith" and level "(i-1)th
79  *
80  * @note Since the orientation binning and block normalization kernels can be skipped, we need to keep track of the input to process for each kernel
81  * with "input_orient_bin", "input_hog_detect" and "input_block_norm"
82  */
83  std::vector<size_t> input_orient_bin;
84  std::vector<size_t> input_hog_detect;
85  std::vector<std::pair<size_t, size_t>> input_block_norm;
86 
87  input_orient_bin.push_back(0);
88  input_hog_detect.push_back(0);
89  input_block_norm.emplace_back(0, 0);
90 
91  for(size_t i = 1; i < num_models; ++i)
92  {
93  size_t cur_num_bins = multi_hog->model(i)->info()->num_bins();
94  Size2D cur_cell_size = multi_hog->model(i)->info()->cell_size();
95  Size2D cur_block_size = multi_hog->model(i)->info()->block_size();
96  Size2D cur_block_stride = multi_hog->model(i)->info()->block_stride();
97 
98  if((cur_num_bins != prev_num_bins) || (cur_cell_size.width != prev_cell_size.width) || (cur_cell_size.height != prev_cell_size.height))
99  {
100  prev_num_bins = cur_num_bins;
101  prev_cell_size = cur_cell_size;
102  prev_block_size = cur_block_size;
103  prev_block_stride = cur_block_stride;
104 
105  // Compute orientation binning and block normalization kernels. Update input to process
106  input_orient_bin.push_back(i);
107  input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
108  }
109  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)
110  || (cur_block_stride.height != prev_block_stride.height))
111  {
112  prev_block_size = cur_block_size;
113  prev_block_stride = cur_block_stride;
114 
115  // Compute block normalization kernel. Update input to process
116  input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
117  }
118 
119  // Update input to process for hog detector kernel
120  input_hog_detect.push_back(input_block_norm.size() - 1);
121  }
122 
123  _detection_windows = detection_windows;
124  _non_maxima_suppression = non_maxima_suppression;
125  _num_orient_bin_kernel = input_orient_bin.size(); // Number of NEHOGOrientationBinningKernel kernels to compute
126  _num_block_norm_kernel = input_block_norm.size(); // Number of NEHOGBlockNormalizationKernel kernels to compute
127  _num_hog_detect_kernel = input_hog_detect.size(); // Number of NEHOGDetector functions to compute
128 
129  _orient_bin_kernel.clear();
130  _block_norm_kernel.clear();
131  _hog_detect_kernel.clear();
132  _hog_space.clear();
133  _hog_norm_space.clear();
134 
135  _orient_bin_kernel.resize(_num_orient_bin_kernel);
136  _block_norm_kernel.resize(_num_block_norm_kernel);
137  _hog_detect_kernel.resize(_num_hog_detect_kernel);
138  _hog_space.resize(_num_orient_bin_kernel);
139  _hog_norm_space.resize(_num_block_norm_kernel);
140  _non_maxima_kernel = CPPDetectionWindowNonMaximaSuppressionKernel();
141 
142  // Allocate tensors for magnitude and phase
143  TensorInfo info_mag(shape_img, Format::S16);
144  _mag.allocator()->init(info_mag);
145 
146  TensorInfo info_phase(shape_img, Format::U8);
147  _phase.allocator()->init(info_phase);
148 
149  // Manage intermediate buffers
150  _memory_group.manage(&_mag);
151  _memory_group.manage(&_phase);
152 
153  // Initialise gradient kernel
154  _gradient_kernel.configure(input, &_mag, &_phase, phase_type, border_mode, constant_border_value);
155 
156  // Configure NETensor for the HOG space and orientation binning kernel
157  for(size_t i = 0; i < _num_orient_bin_kernel; ++i)
158  {
159  const size_t idx_multi_hog = input_orient_bin[i];
160 
161  // Get the corresponding cell size and number of bins
162  const Size2D &cell = multi_hog->model(idx_multi_hog)->info()->cell_size();
163  const size_t num_bins = multi_hog->model(idx_multi_hog)->info()->num_bins();
164 
165  // Calculate number of cells along the x and y directions for the hog_space
166  const size_t num_cells_x = width / cell.width;
167  const size_t num_cells_y = height / cell.height;
168 
169  // TensorShape of hog space
170  TensorShape shape_hog_space = input->info()->tensor_shape();
171  shape_hog_space.set(Window::DimX, num_cells_x);
172  shape_hog_space.set(Window::DimY, num_cells_y);
173 
174  // Allocate HOG space
175  TensorInfo info_space(shape_hog_space, num_bins, DataType::F32);
176  _hog_space[i].allocator()->init(info_space);
177 
178  // Manage intermediate buffers
179  _memory_group.manage(&_hog_space[i]);
180 
181  // Initialise orientation binning kernel
182  _orient_bin_kernel[i].configure(&_mag, &_phase, &_hog_space[i], multi_hog->model(idx_multi_hog)->info());
183  }
184 
185  // Allocate intermediate tensors
186  _mag.allocator()->allocate();
187  _phase.allocator()->allocate();
188 
189  // Configure NETensor for the normalized HOG space and block normalization kernel
190  for(size_t i = 0; i < _num_block_norm_kernel; ++i)
191  {
192  const size_t idx_multi_hog = input_block_norm[i].first;
193  const size_t idx_orient_bin = input_block_norm[i].second;
194 
195  // Allocate normalized HOG space
196  TensorInfo tensor_info(*(multi_hog->model(idx_multi_hog)->info()), width, height);
197  _hog_norm_space[i].allocator()->init(tensor_info);
198 
199  // Manage intermediate buffers
200  _memory_group.manage(&_hog_norm_space[i]);
201 
202  // Initialize block normalization kernel
203  _block_norm_kernel[i].configure(&_hog_space[idx_orient_bin], &_hog_norm_space[i], multi_hog->model(idx_multi_hog)->info());
204  }
205 
206  // Allocate intermediate tensors
207  for(size_t i = 0; i < _num_orient_bin_kernel; ++i)
208  {
209  _hog_space[i].allocator()->allocate();
210  }
211 
212  // Configure HOG detector kernel
213  for(size_t i = 0; i < _num_hog_detect_kernel; ++i)
214  {
215  const size_t idx_block_norm = input_hog_detect[i];
216 
217  _hog_detect_kernel[i].configure(&_hog_norm_space[idx_block_norm], multi_hog->model(i), detection_windows, detection_window_strides->at(i), threshold, i);
218  }
219 
220  // Configure non maxima suppression kernel
221  _non_maxima_kernel.configure(_detection_windows, min_distance);
222 
223  // Allocate intermediate tensors
224  for(size_t i = 0; i < _num_block_norm_kernel; ++i)
225  {
226  _hog_norm_space[i].allocator()->allocate();
227  }
228 }
229 
231 {
232  ARM_COMPUTE_ERROR_ON_MSG(_detection_windows == nullptr, "Unconfigured function");
233 
234  MemoryGroupResourceScope scope_mg(_memory_group);
235 
236  // Reset detection window
237  _detection_windows->clear();
238 
239  // Run gradient
240  _gradient_kernel.run();
241 
242  // Run orientation binning kernel
243  for(auto &kernel : _orient_bin_kernel)
244  {
246  }
247 
248  // Run block normalization kernel
249  for(auto &kernel : _block_norm_kernel)
250  {
252  }
253 
254  // Run HOG detector kernel
255  for(auto &kernel : _hog_detect_kernel)
256  {
257  kernel.run();
258  }
259 
260  // Run non-maxima suppression kernel if enabled
261  if(_non_maxima_suppression)
262  {
263  NEScheduler::get().schedule(&_non_maxima_kernel, Window::DimY);
264  }
265 }
BorderMode
Methods available to handle borders.
Definition: Types.h:261
size_t num_bins() const
The number of histogram bins for each cell.
Definition: HOGInfo.cpp:111
void clear()
Clear all the points from the array.
Definition: IArray.h:91
Shape of a tensor.
Definition: TensorShape.h:39
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...
const Size2D & cell_size() const
The cell size in pixels.
Definition: HOGInfo.cpp:91
PhaseType
Phase calculation type.
Definition: Types.h:406
1 channel, 1 U8 per channel
Array of type T.
Definition: IArray.h:40
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.
Definition: Error.h:466
PhaseType phase_type() const
The type of PhaseType.
Definition: HOGInfo.cpp:126
Interface for NEON tensor.
Definition: ITensor.h:36
const Size2D & block_stride() const
The block stride in pixels.
Definition: HOGInfo.cpp:106
Copyright (c) 2017-2020 ARM Limited.
size_t height
Height of the image region or rectangle.
Definition: Size2D.h:93
#define ARM_COMPUTE_ERROR_ON_INVALID_MULTI_HOG(m)
Definition: Validate.h:925
void run() override
Run the kernels contained in the function.
TensorAllocator * allocator()
Return a pointer to the tensor's allocator.
Definition: Tensor.cpp:48
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.
Definition: MemoryGroup.h:79
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
size_t num_values() const
Number of values currently stored in the array.
Definition: IArray.h:68
#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)
Definition: Error.h:456
const Size2D & block_size() const
The block size in pixels.
Definition: HOGInfo.cpp:96
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.
void run() override
Run the kernels contained in the function.
1 channel, 1 S16 per channel
CPP kernel to perform in-place computation of euclidean distance on IDetectionWindowArray.
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:790
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,...
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
virtual IHOG * model(size_t index)=0
Return a pointer to the requested HOG model.
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
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.
Definition: Size2D.h:92
Interface for storing multiple HOG data-objects.
Definition: IMultiHOG.h:34
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.
Definition: Size2D.h:34
TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)
Accessor to set the value of one of the dimensions.
Definition: TensorShape.h:78
Store the tensor's metadata.
Definition: TensorInfo.h:45
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)
Definition: Threshold.cpp:35
virtual T & at(size_t index) const
Reference to the element of the array located at the given index.
Definition: IArray.h:117
static IScheduler & get()
Access the scheduler singleton.
Definition: Scheduler.cpp:95