[−][src]Struct opencv::objdetect::HOGDescriptor
Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs Dalal2005 .
useful links:
https://hal.inria.fr/inria-00548512/document/
https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
http://www.learnopencv.com/histogram-of-oriented-gradients
http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
Implementations
impl HOGDescriptor
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pub fn as_raw_HOGDescriptor(&self) -> *const c_void
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pub fn as_raw_mut_HOGDescriptor(&mut self) -> *mut c_void
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impl HOGDescriptor
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pub fn default() -> Result<HOGDescriptor>
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Creates the HOG descriptor and detector with default params.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
pub fn new(
_win_size: Size,
_block_size: Size,
_block_stride: Size,
_cell_size: Size,
_nbins: i32,
_deriv_aperture: i32,
_win_sigma: f64,
_histogram_norm_type: HOGDescriptor_HistogramNormType,
_l2_hys_threshold: f64,
_gamma_correction: bool,
_nlevels: i32,
_signed_gradient: bool
) -> Result<HOGDescriptor>
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_win_size: Size,
_block_size: Size,
_block_stride: Size,
_cell_size: Size,
_nbins: i32,
_deriv_aperture: i32,
_win_sigma: f64,
_histogram_norm_type: HOGDescriptor_HistogramNormType,
_l2_hys_threshold: f64,
_gamma_correction: bool,
_nlevels: i32,
_signed_gradient: bool
) -> Result<HOGDescriptor>
Creates the HOG descriptor and detector with default params.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
Overloaded parameters
Parameters
- _winSize: sets winSize with given value.
- _blockSize: sets blockSize with given value.
- _blockStride: sets blockStride with given value.
- _cellSize: sets cellSize with given value.
- _nbins: sets nbins with given value.
- _derivAperture: sets derivAperture with given value.
- _winSigma: sets winSigma with given value.
- _histogramNormType: sets histogramNormType with given value.
- _L2HysThreshold: sets L2HysThreshold with given value.
- _gammaCorrection: sets gammaCorrection with given value.
- _nlevels: sets nlevels with given value.
- _signedGradient: sets signedGradient with given value.
C++ default parameters
- _deriv_aperture: 1
- _win_sigma: -1
- _histogram_norm_type: HOGDescriptor::L2Hys
- _l2_hys_threshold: 0.2
- _gamma_correction: false
- _nlevels: HOGDescriptor::DEFAULT_NLEVELS
- _signed_gradient: false
pub fn new_from_file(filename: &str) -> Result<HOGDescriptor>
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Creates the HOG descriptor and detector with default params.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
Overloaded parameters
Parameters
- filename: The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
pub fn copy(d: &HOGDescriptor) -> Result<HOGDescriptor>
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Creates the HOG descriptor and detector with default params.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
Overloaded parameters
Parameters
- d: the HOGDescriptor which cloned to create a new one.
pub fn get_default_people_detector() -> Result<Vector<f32>>
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Returns coefficients of the classifier trained for people detection (for 64x128 windows).
pub fn get_daimler_people_detector() -> Result<Vector<f32>>
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@example samples/tapi/hog.cpp / Returns coefficients of the classifier trained for people detection (for 48x96 windows).
Trait Implementations
impl Boxed for HOGDescriptor
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unsafe fn from_raw(ptr: *mut c_void) -> Self
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fn into_raw(self) -> *mut c_void
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fn as_raw(&self) -> *const c_void
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fn as_raw_mut(&mut self) -> *mut c_void
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impl Drop for HOGDescriptor
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impl HOGDescriptorTrait for HOGDescriptor
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fn as_raw_HOGDescriptor(&self) -> *const c_void
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fn as_raw_mut_HOGDescriptor(&mut self) -> *mut c_void
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fn win_size(&self) -> Size
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fn set_win_size(&mut self, val: Size)
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fn block_size(&self) -> Size
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fn set_block_size(&mut self, val: Size)
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fn block_stride(&self) -> Size
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fn set_block_stride(&mut self, val: Size)
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fn cell_size(&self) -> Size
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fn set_cell_size(&mut self, val: Size)
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fn nbins(&self) -> i32
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fn set_nbins(&mut self, val: i32)
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fn deriv_aperture(&self) -> i32
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fn set_deriv_aperture(&mut self, val: i32)
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fn win_sigma(&self) -> f64
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fn set_win_sigma(&mut self, val: f64)
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fn histogram_norm_type(&self) -> HOGDescriptor_HistogramNormType
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fn set_histogram_norm_type(&mut self, val: HOGDescriptor_HistogramNormType)
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fn l2_hys_threshold(&self) -> f64
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fn set_l2_hys_threshold(&mut self, val: f64)
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fn gamma_correction(&self) -> bool
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fn set_gamma_correction(&mut self, val: bool)
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fn svm_detector(&mut self) -> Vector<f32>
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fn set_svm_detector_vec(&mut self, val: Vector<f32>)
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fn ocl_svm_detector(&mut self) -> UMat
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fn set_ocl_svm_detector(&mut self, val: UMat)
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fn free_coef(&self) -> f32
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fn set_free_coef(&mut self, val: f32)
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fn nlevels(&self) -> i32
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fn set_nlevels(&mut self, val: i32)
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fn signed_gradient(&self) -> bool
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fn set_signed_gradient(&mut self, val: bool)
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fn get_descriptor_size(&self) -> Result<size_t>
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fn check_detector_size(&self) -> Result<bool>
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fn get_win_sigma(&self) -> Result<f64>
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fn set_svm_detector(&mut self, svmdetector: &dyn ToInputArray) -> Result<()>
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fn read(&mut self, fn_: &mut FileNode) -> Result<bool>
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fn write(&self, fs: &mut FileStorage, objname: &str) -> Result<()>
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fn load(&mut self, filename: &str, objname: &str) -> Result<bool>
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fn save(&self, filename: &str, objname: &str) -> Result<()>
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fn copy_to(&self, c: &mut HOGDescriptor) -> Result<()>
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fn compute(
&self,
img: &dyn ToInputArray,
descriptors: &mut Vector<f32>,
win_stride: Size,
padding: Size,
locations: &Vector<Point>
) -> Result<()>
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&self,
img: &dyn ToInputArray,
descriptors: &mut Vector<f32>,
win_stride: Size,
padding: Size,
locations: &Vector<Point>
) -> Result<()>
fn detect_weights(
&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Point>,
weights: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
search_locations: &Vector<Point>
) -> Result<()>
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&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Point>,
weights: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
search_locations: &Vector<Point>
) -> Result<()>
fn detect(
&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Point>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
search_locations: &Vector<Point>
) -> Result<()>
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&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Point>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
search_locations: &Vector<Point>
) -> Result<()>
fn detect_multi_scale_weights(
&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
found_weights: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
scale: f64,
final_threshold: f64,
use_meanshift_grouping: bool
) -> Result<()>
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&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
found_weights: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
scale: f64,
final_threshold: f64,
use_meanshift_grouping: bool
) -> Result<()>
fn detect_multi_scale(
&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
scale: f64,
final_threshold: f64,
use_meanshift_grouping: bool
) -> Result<()>
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&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
hit_threshold: f64,
win_stride: Size,
padding: Size,
scale: f64,
final_threshold: f64,
use_meanshift_grouping: bool
) -> Result<()>
fn compute_gradient(
&self,
img: &dyn ToInputArray,
grad: &mut dyn ToInputOutputArray,
angle_ofs: &mut dyn ToInputOutputArray,
padding_tl: Size,
padding_br: Size
) -> Result<()>
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&self,
img: &dyn ToInputArray,
grad: &mut dyn ToInputOutputArray,
angle_ofs: &mut dyn ToInputOutputArray,
padding_tl: Size,
padding_br: Size
) -> Result<()>
fn detect_roi(
&self,
img: &dyn ToInputArray,
locations: &Vector<Point>,
found_locations: &mut Vector<Point>,
confidences: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size
) -> Result<()>
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&self,
img: &dyn ToInputArray,
locations: &Vector<Point>,
found_locations: &mut Vector<Point>,
confidences: &mut Vector<f64>,
hit_threshold: f64,
win_stride: Size,
padding: Size
) -> Result<()>
fn detect_multi_scale_roi(
&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
locations: &mut Vector<DetectionROI>,
hit_threshold: f64,
group_threshold: i32
) -> Result<()>
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&self,
img: &dyn ToInputArray,
found_locations: &mut Vector<Rect>,
locations: &mut Vector<DetectionROI>,
hit_threshold: f64,
group_threshold: i32
) -> Result<()>
fn group_rectangles(
&self,
rect_list: &mut Vector<Rect>,
weights: &mut Vector<f64>,
group_threshold: i32,
eps: f64
) -> Result<()>
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&self,
rect_list: &mut Vector<Rect>,
weights: &mut Vector<f64>,
group_threshold: i32,
eps: f64
) -> Result<()>
impl Send for HOGDescriptor
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Auto Trait Implementations
impl RefUnwindSafe for HOGDescriptor
impl !Sync for HOGDescriptor
impl Unpin for HOGDescriptor
impl UnwindSafe for HOGDescriptor
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,