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package gocv
/*
#include <stdlib.h>
#include "objdetect.h"
*/
import "C"
import (
"image"
"unsafe"
)
// CascadeClassifier is a cascade classifier class for object detection.
//
// For further details, please see:
// http://docs.opencv.org/master/d1/de5/classcv_1_1CascadeClassifier.html
//
type CascadeClassifier struct {
p C.CascadeClassifier
}
// NewCascadeClassifier returns a new CascadeClassifier.
func NewCascadeClassifier() CascadeClassifier {
return CascadeClassifier{p: C.CascadeClassifier_New()}
}
// Close deletes the CascadeClassifier's pointer.
func (c *CascadeClassifier) Close() error {
C.CascadeClassifier_Close(c.p)
c.p = nil
return nil
}
// Load cascade classifier from a file.
//
// For further details, please see:
// http://docs.opencv.org/master/d1/de5/classcv_1_1CascadeClassifier.html#a1a5884c8cc749422f9eb77c2471958bc
//
func (c *CascadeClassifier) Load(name string) bool {
cName := C.CString(name)
defer C.free(unsafe.Pointer(cName))
return C.CascadeClassifier_Load(c.p, cName) != 0
}
// DetectMultiScale detects objects of different sizes in the input Mat image.
// The detected objects are returned as a slice of image.Rectangle structs.
//
// For further details, please see:
// http://docs.opencv.org/master/d1/de5/classcv_1_1CascadeClassifier.html#aaf8181cb63968136476ec4204ffca498
//
func (c *CascadeClassifier) DetectMultiScale(img Mat) []image.Rectangle {
ret := C.CascadeClassifier_DetectMultiScale(c.p, img.p)
defer C.Rects_Close(ret)
return toRectangles(ret)
}
// DetectMultiScaleWithParams calls DetectMultiScale but allows setting parameters
// to values other than just the defaults.
//
// For further details, please see:
// http://docs.opencv.org/master/d1/de5/classcv_1_1CascadeClassifier.html#aaf8181cb63968136476ec4204ffca498
//
func (c *CascadeClassifier) DetectMultiScaleWithParams(img Mat, scale float64,
minNeighbors, flags int, minSize, maxSize image.Point) []image.Rectangle {
minSz := C.struct_Size{
width: C.int(minSize.X),
height: C.int(minSize.Y),
}
maxSz := C.struct_Size{
width: C.int(maxSize.X),
height: C.int(maxSize.Y),
}
ret := C.CascadeClassifier_DetectMultiScaleWithParams(c.p, img.p, C.double(scale),
C.int(minNeighbors), C.int(flags), minSz, maxSz)
defer C.Rects_Close(ret)
return toRectangles(ret)
}
// HOGDescriptor is a Histogram Of Gradiants (HOG) for object detection.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html#a723b95b709cfd3f95cf9e616de988fc8
//
type HOGDescriptor struct {
p C.HOGDescriptor
}
// NewHOGDescriptor returns a new HOGDescriptor.
func NewHOGDescriptor() HOGDescriptor {
return HOGDescriptor{p: C.HOGDescriptor_New()}
}
// Close deletes the HOGDescriptor's pointer.
func (h *HOGDescriptor) Close() error {
C.HOGDescriptor_Close(h.p)
h.p = nil
return nil
}
// DetectMultiScale detects objects in the input Mat image.
// The detected objects are returned as a slice of image.Rectangle structs.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html#a660e5cd036fd5ddf0f5767b352acd948
//
func (h *HOGDescriptor) DetectMultiScale(img Mat) []image.Rectangle {
ret := C.HOGDescriptor_DetectMultiScale(h.p, img.p)
defer C.Rects_Close(ret)
return toRectangles(ret)
}
// DetectMultiScaleWithParams calls DetectMultiScale but allows setting parameters
// to values other than just the defaults.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html#a660e5cd036fd5ddf0f5767b352acd948
//
func (h *HOGDescriptor) DetectMultiScaleWithParams(img Mat, hitThresh float64,
winStride, padding image.Point, scale, finalThreshold float64, useMeanshiftGrouping bool) []image.Rectangle {
wSz := C.struct_Size{
width: C.int(winStride.X),
height: C.int(winStride.Y),
}
pSz := C.struct_Size{
width: C.int(padding.X),
height: C.int(padding.Y),
}
ret := C.HOGDescriptor_DetectMultiScaleWithParams(h.p, img.p, C.double(hitThresh),
wSz, pSz, C.double(scale), C.double(finalThreshold), C.bool(useMeanshiftGrouping))
defer C.Rects_Close(ret)
return toRectangles(ret)
}
// HOGDefaultPeopleDetector returns a new Mat with the HOG DefaultPeopleDetector.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html#a660e5cd036fd5ddf0f5767b352acd948
//
func HOGDefaultPeopleDetector() Mat {
return newMat(C.HOG_GetDefaultPeopleDetector())
}
// SetSVMDetector sets the data for the HOGDescriptor.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html#a09e354ad701f56f9c550dc0385dc36f1
//
func (h *HOGDescriptor) SetSVMDetector(det Mat) error {
C.HOGDescriptor_SetSVMDetector(h.p, det.p)
return nil
}
// GroupRectangles groups the object candidate rectangles.
//
// For further details, please see:
// https://docs.opencv.org/master/d5/d54/group__objdetect.html#ga3dba897ade8aa8227edda66508e16ab9
//
func GroupRectangles(rects []image.Rectangle, groupThreshold int, eps float64) []image.Rectangle {
cRectArray := make([]C.struct_Rect, len(rects))
for i, r := range rects {
cRect := C.struct_Rect{
x: C.int(r.Min.X),
y: C.int(r.Min.Y),
width: C.int(r.Size().X),
height: C.int(r.Size().Y),
}
cRectArray[i] = cRect
}
cRects := C.struct_Rects{
rects: (*C.Rect)(&cRectArray[0]),
length: C.int(len(rects)),
}
ret := C.GroupRectangles(cRects, C.int(groupThreshold), C.double(eps))
return toRectangles(ret)
}
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