Package org.opencv.dnn

Class DetectionModel

  • public class DetectionModel
extends Model
    This class represents high-level API for object detection networks. DetectionModel allows to set params for preprocessing input image. DetectionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return result detections. For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.

    • Constructor Detail

      • DetectionModel

        protected DetectionModel​(long addr)

      • DetectionModel

        public DetectionModel​(Net network)
        Create model from deep learning network.
        Parameters:
        network - Net object.

      • DetectionModel

        public DetectionModel​(java.lang.String model,
                      java.lang.String config)
        Create detection model from network represented in one of the supported formats. An order of model and config arguments does not matter.
        Parameters:
        model - Binary file contains trained weights.
        config - Text file contains network configuration.

      • DetectionModel

        public DetectionModel​(java.lang.String model)
        Create detection model from network represented in one of the supported formats. An order of model and config arguments does not matter.
        Parameters:
        model - Binary file contains trained weights.

    • Method Detail

      • __fromPtr__

        public static DetectionModel __fromPtr__​(long addr)

      • detect

        public void detect​(Mat frame,
                   MatOfInt classIds,
                   MatOfFloat confidences,
                   MatOfRect boxes,
                   float confThreshold,
                   float nmsThreshold)
        Given the input frame, create input blob, run net and return result detections.
        Parameters:
        classIds - Class indexes in result detection.
        confidences - A set of corresponding confidences.
        boxes - A set of bounding boxes.
        confThreshold - A threshold used to filter boxes by confidences.
        nmsThreshold - A threshold used in non maximum suppression.
        frame - automatically generated

      • detect

        public void detect​(Mat frame,
                   MatOfInt classIds,
                   MatOfFloat confidences,
                   MatOfRect boxes,
                   float confThreshold)
        Given the input frame, create input blob, run net and return result detections.
        Parameters:
        classIds - Class indexes in result detection.
        confidences - A set of corresponding confidences.
        boxes - A set of bounding boxes.
        confThreshold - A threshold used to filter boxes by confidences.
        frame - automatically generated

      • detect

        public void detect​(Mat frame,
                   MatOfInt classIds,
                   MatOfFloat confidences,
                   MatOfRect boxes)
        Given the input frame, create input blob, run net and return result detections.
        Parameters:
        classIds - Class indexes in result detection.
        confidences - A set of corresponding confidences.
        boxes - A set of bounding boxes.
        frame - automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class Model
        Throws:
        java.lang.Throwable