| | |
| -: | :- |
| Original Author | Chengrui Wang, Yuantao Feng |
-| Compatibility | OpenCV >= 4.5.1 |
+| Compatibility | OpenCV >= 4.5.4 |
## Introduction
-In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizerSF` are presented in [Usage](#Usage).
+In this section, we introduce cv::FaceDetectorYN class for face detection and cv::FaceRecognizerSF class for face recognition.
## Models
There are two models (ONNX format) pre-trained and required for this module:
-- [Face Detection](https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx):
- - Size: 337KB
+- [Face Detection](https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet):
+ - Size: 338KB
- Results on WIDER Face Val set: 0.830(easy), 0.824(medium), 0.708(hard)
-- [Face Recognition](https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view?usp=sharing)
+- [Face Recognition](https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface)
- Size: 36.9MB
- Results:
| AgeDB-30 | 94.90% | 1.202 | 0.277 |
| CFP-FP | 94.80% | 1.253 | 0.212 |
-## Usage
-
-### DNNFaceDetector
+## Code
@add_toggle_cpp
- **Downloadable code**: Click
/**
@defgroup objdetect Object Detection
-Haar Feature-based Cascade Classifier for Object Detection
-----------------------------------------------------------
+@{
+ @defgroup objdetect_cascade_classifier Cascade Classifier for Object Detection
The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
improved by Rainer Lienhart @cite Lienhart02 .
compensate for the differences in the size of areas. The sums of pixel values over a rectangular
regions are calculated rapidly using integral images (see below and the integral description).
-To see the object detector at work, have a look at the facedetect demo:
-<https://github.com/opencv/opencv/tree/4.x/samples/cpp/dbt_face_detection.cpp>
+Check @ref tutorial_cascade_classifier "the corresponding tutorial" for more details.
The following reference is for the detection part only. There is a separate application called
opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
-<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
+<https://github.com/SvHey/thesis/blob/master/Literature/ObjectDetection/violaJones_CVPR2001.pdf>
-@{
- @defgroup objdetect_c C API
+ @defgroup objdetect_hog HOG (Histogram of Oriented Gradients) descriptor and object detector
+ @defgroup objdetect_qrcode QRCode detection and encoding
+ @defgroup objdetect_dnn_face DNN-based face detection and recognition
+Check @ref tutorial_dnn_face "the corresponding tutorial" for more details.
+ @defgroup objdetect_common Common functions and classes
@}
*/
namespace cv
{
-//! @addtogroup objdetect
+//! @addtogroup objdetect_common
//! @{
///////////////////////////// Object Detection ////////////////////////////
-//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
-//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
+/** @brief This class is used for grouping object candidates detected by Cascade Classifier, HOG etc.
+
+instance of the class is to be passed to cv::partition
+ */
class CV_EXPORTS SimilarRects
{
public:
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
std::vector<double>& foundScales,
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
+//! @}
+
+//! @addtogroup objdetect_cascade_classifier
+//! @{
template<> struct DefaultDeleter<CvHaarClassifierCascade>{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; };
CV_WRAP bool load( const String& filename );
/** @brief Reads a classifier from a FileStorage node.
- @note The file may contain a new cascade classifier (trained traincascade application) only.
+ @note The file may contain a new cascade classifier (trained by the traincascade application) only.
*/
CV_WRAP bool read( const FileNode& node );
cvHaarDetectObjects. It is not used for a new cascade.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
-
- The function is parallelized with the TBB library.
-
- @note
- - (Python) A face detection example using cascade classifiers can be found at
- opencv_source_code/samples/python/facedetect.py
*/
CV_WRAP void detectMultiScale( InputArray image,
CV_OUT std::vector<Rect>& objects,
};
CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
+//! @}
+//! @addtogroup objdetect_hog
+//! @{
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
//! struct for detection region of interest (ROI)
*/
void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
};
+//! @}
+
+//! @addtogroup objdetect_qrcode
+//! @{
class CV_EXPORTS_W QRCodeEncoder {
protected:
Ptr<Impl> p;
};
-//! @} objdetect
+//! @}
}
#include "opencv2/objdetect/detection_based_tracker.hpp"
namespace cv
{
-//! @addtogroup objdetect
+//! @addtogroup objdetect_cascade_classifier
//! @{
class CV_EXPORTS DetectionBasedTracker
void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
};
-//! @} objdetect
+//! @}
} //end of cv namespace
#include <opencv2/core.hpp>
-/** @defgroup dnn_face DNN-based face detection and recognition
- */
-
namespace cv
{
-/** @brief DNN-based face detector, model download link: https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.
+//! @addtogroup objdetect_dnn_face
+//! @{
+
+/** @brief DNN-based face detector
+
+model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet
*/
class CV_EXPORTS_W FaceDetectorYN
{
int target_id = 0);
};
-/** @brief DNN-based face recognizer, model download link: https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.
+/** @brief DNN-based face recognizer
+
+model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface
*/
class CV_EXPORTS_W FaceRecognizerSF
{
CV_WRAP virtual void feature(InputArray aligned_img, OutputArray face_feature) = 0;
/** @brief Calculating the distance between two face features
- * @param _face_feature1 the first input feature
- * @param _face_feature2 the second input feature of the same size and the same type as _face_feature1
+ * @param face_feature1 the first input feature
+ * @param face_feature2 the second input feature of the same size and the same type as face_feature1
* @param dis_type defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2"
*/
- CV_WRAP virtual double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type = FaceRecognizerSF::FR_COSINE) const = 0;
+ CV_WRAP virtual double match(InputArray face_feature1, InputArray face_feature2, int dis_type = FaceRecognizerSF::FR_COSINE) const = 0;
/** @brief Creates an instance of this class with given parameters
* @param model the path of the onnx model used for face recognition
CV_WRAP static Ptr<FaceRecognizerSF> create(const String& model, const String& config, int backend_id = 0, int target_id = 0);
};
+//! @}
} // namespace cv
#endif
"{image2 i2 | | Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm}"
"{video v | 0 | Path to the input video}"
"{scale sc | 1.0 | Scale factor used to resize input video frames}"
- "{fd_model fd | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx }"
- "{fr_model fr | face_recognizer_fast.onnx | Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view}"
+ "{fd_model fd | face_detection_yunet_2021dec.onnx| Path to the model. Download yunet.onnx in https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet}"
+ "{fr_model fr | face_recognition_sface_2021dec.onnx | Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface}"
"{score_threshold | 0.9 | Filter out faces of score < score_threshold}"
"{nms_threshold | 0.3 | Suppress bounding boxes of iou >= nms_threshold}"
"{top_k | 5000 | Keep top_k bounding boxes before NMS}"
int topK = parser.get<int>("top_k");
bool save = parser.get<bool>("save");
+ float scale = parser.get<float>("scale");
double cosine_similar_thresh = 0.363;
double l2norm_similar_thresh = 1.128;
return 2;
}
+ int imageWidth = int(image1.cols * scale);
+ int imageHeight = int(image1.rows * scale);
+ resize(image1, image1, Size(imageWidth, imageHeight));
tm.start();
//! [inference]
else
{
int frameWidth, frameHeight;
- float scale = parser.get<float>("scale");
VideoCapture capture;
std::string video = parser.get<string>("video");
if (video.size() == 1 && isdigit(video[0]))
parser.add_argument('--image2', '-i2', type=str, help='Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm.')
parser.add_argument('--video', '-v', type=str, help='Path to the input video.')
parser.add_argument('--scale', '-sc', type=float, default=1.0, help='Scale factor used to resize input video frames.')
-parser.add_argument('--face_detection_model', '-fd', type=str, default='yunet.onnx', help='Path to the face detection model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.')
-parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognizer_fast.onnx', help='Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.')
+parser.add_argument('--face_detection_model', '-fd', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the face detection model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet')
+parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognition_sface_2021dec.onnx', help='Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface')
parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.')
parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
# If input is an image
if args.image1 is not None:
img1 = cv.imread(cv.samples.findFile(args.image1))
+ img1Width = int(img1.shape[1]*args.scale)
+ img1Height = int(img1.shape[0]*args.scale)
+ img1 = cv.resize(img1, (img1Width, img1Height))
tm.start()
+
## [inference]
# Set input size before inference
- detector.setInputSize((img1.shape[1], img1.shape[0]))
+ detector.setInputSize((img1Width, img1Height))
faces1 = detector.detect(img1)
## [inference]
projects / platform / upstream / opencv.git / commitdiff