Plumerai People Detection for Cortex-A C++ API¶

PeopleDetection::PeopleDetection(int height, int width)

template <ImageFormat image_format>
std::vector<BoxPrediction> process_frame(const std::uint8_t *image_data,
                                         float delta_t = 0.f)

template <ImageFormat image_format>
std::vector<BoxPrediction> process_frame(const std::uint8_t *image_y,
                                         const std::uint8_t *image_u,
                                         const std::uint8_t *image_v,
                                         float delta_t = 0.f)

std::vector<BoxPrediction> single_image(const std::uint8_t *image_data,
                                        float confidence_threshold);

struct BoxPrediction {
  float y_min;       // top coordinate between 0 and 1 in height dimension
  float x_min;       // left coordinate between 0 and 1 in width dimension
  float y_max;       // bottom coordinate between 0 and 1 in height dimension
  float x_max;       // right coordinate between 0 and 1 in width dimension
  float confidence;  // between 0 and 1, higher means more confident
  unsigned int id;   // the tracked identifier of this box
  unsigned int class_id;  // the class of the detected object
};

#pragma once

#include <string>
#include <vector>

#include "box_prediction.h"

namespace plumerai {

// Supported input formats
// - Packed/interleaved:
//   - RGB888
//   - YUYV, also known as YUY2, which has 4:2:0 subsampling
// - Planar:
//   - YUV420
enum class ImageFormat { PACKED_RGB888, PACKED_YUYV, PLANAR_YUV420 };

class PeopleDetection {
 public:
  // Initializes a new people detection object. This needs to be called only
  // once at the start of the application.
  //
  // @param height The height of the input image in pixels.
  // @param width The width of the input image in pixels.
  PeopleDetection(int height, int width);

  // Destructor, called automatically when the object goes out of scope
  ~PeopleDetection();

  // Process a single frame from a video sequence.
  // This version supports RGB or YUYV input. See below for the YUV420 version.
  //
  // @param image_format Can be either ImageFormat::PACKED_RGB888 or
  //  ImageFormat::PACKED_YUYV. See below for PLANAR_YUYV420.
  // @param image_data A pointer to RGB image data (1st byte red, 3rd blue)
  //  of size height * width * 3 or YUYV image data of size height * width * 2.
  // @param delta_t The time in seconds it took between this and the previous
  //  video frame (1/fps). If left to the default of 0, then the system clock
  //  will be used to compute this value.
  // @return the resulting bounding-boxes found in the frame.
  template <ImageFormat image_format = ImageFormat::PACKED_RGB888>
  std::vector<BoxPrediction> process_frame(const std::uint8_t *image_data,
                                           float delta_t = 0.f);

  // Process a single frame from a video sequence with planar YUV input and 420
  // chroma subsampling. See above for the RGB and YUYV versions.
  //
  // @param image_format Has to be ImageFormat::PLANAR_YUYV420. See above for
  //  other formats.
  // @param image_data_y A pointer to the Y channel, of size height * width.
  // @param image_data_u A pointer to the U channel, of size height * width / 4.
  // @param image_data_v A pointer to the V channel, of size height * width / 4.
  // @param delta_t The time in seconds it took between this and the previous
  //  video frame (1/fps). If left to the default of 0, then the system clock
  //  will be used to compute this value.
  // @return the resulting bounding-boxes found in the frame.
  template <ImageFormat image_format = ImageFormat::PLANAR_YUV420>
  std::vector<BoxPrediction> process_frame(const std::uint8_t *image_y,
                                           const std::uint8_t *image_u,
                                           const std::uint8_t *image_v,
                                           float delta_t = 0.f);

  // Process a single image not part of a video sequence. This should not be
  // used for video data, but only for single image evaluation and debugging.
  // The returned box id values are not related to those returned by
  // `process_frame` or other calls to `single_frame`.
  //
  // @param image_data A pointer to RGB image data (1st byte red, 3rd blue)
  //  of size height * width * 3.
  // @param confidence_threshold Any box with a confidence value below this
  //  threshold will be filtered out. Range between 0 and 1. A value of 0.63
  //  is recommended for regular evaluation, but for mAP computation this can
  //  be set to 0.
  // @param height The height of the input image in pixels. If `height = 0` the
  //  height set in the constructor will be used.
  // @param width The width of the input image in pixels. If `width = 0` the
  //  width set in the constructor will be used.
  // @return the resulting bounding-boxes found in the image.
  std::vector<BoxPrediction> single_image(const std::uint8_t *image_data,
                                          float confidence_threshold,
                                          int height = 0, int width = 0);
};

}  // namespace plumerai

#include <string>
#include <vector>

#include "plumerai/people_detection.h"

int main() {

  // Settings, to be changed as needed
  constexpr int width = 1600;   // camera image width in pixels
  constexpr int height = 1200;  // camera image height in pixels
  constexpr auto image_format = plumerai::ImageFormat::PACKED_RGB888;

  // Initialize the people detection algorithm
  auto ppd = plumerai::PeopleDetection(height, width);

  // Loop over frames in a video stream
  while (true) {

    // Some example input here, normally this is where camera data is acquired
    auto image = std::vector<std::uint8_t>(height * width * 3);  // 3 for RGB

    // Process the frame
    auto predictions = ppd.process_frame<image_format>(image.data());

    // Display the results to stdout
    for (auto &p : predictions) {
      printf(
          "Box #%d of class %d with confidence %.2f @ (x,y) -> (%.2f,%.2f) "
          "till (%.2f,%.2f)\n",
          p.id, p.class_id, p.confidence, p.x_min, p.y_min, p.x_max, p.y_max);
    }
  }
  return 0;
}