object_detector_capture.cc

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Example usage:
// bazel run -c opt \
//  tensorflow_lite_support/examples/task/vision/desktop:object_detector_capture \
//  -- \
//  --model_path=/path/to/model.tflite

#include <iostream>
#include <limits>
#include <chrono>

#include <opencv2/opencv.hpp>

#include "absl/flags/flag.h"
#include "absl/flags/parse.h"
#include "absl/status/status.h"
#include "absl/strings/match.h"
#include "absl/strings/str_format.h"
#include "tensorflow_lite_support/cc/port/statusor.h"
#include "tensorflow_lite_support/cc/task/core/external_file_handler.h"
#include "tensorflow_lite_support/cc/task/core/proto/external_file_proto_inc.h"
#include "tensorflow_lite_support/cc/task/vision/object_detector.h"
#include "tensorflow_lite_support/cc/task/vision/proto/bounding_box_proto_inc.h"
#include "tensorflow_lite_support/cc/task/vision/proto/class_proto_inc.h"
#include "tensorflow_lite_support/cc/task/vision/proto/detections_proto_inc.h"
#include "tensorflow_lite_support/cc/task/vision/proto/object_detector_options_proto_inc.h"
#include "tensorflow_lite_support/cc/task/vision/utils/frame_buffer_common_utils.h"
#include "tensorflow_lite_support/examples/task/vision/desktop/utils/image_utils.h"


ABSL_FLAG(std::string, model_path, "",
          "Absolute path to the '.tflite' object detector model.");
ABSL_FLAG(
    float, score_threshold, std::numeric_limits<float>::lowest(),
    "Detection results with a confidence score below this value are "
    "rejected. If specified, overrides the score threshold(s) provided in the "
    "TFLite Model Metadata. Ignored otherwise.");
ABSL_FLAG(int32, max_results, 5,
          "Maximum number of detection results to display.");
ABSL_FLAG(
    std::vector<std::string>, class_name_whitelist, {},
    "Comma-separated list of class names that acts as a whitelist. If "
    "non-empty, detections results whose 'class_name' is not in this list "
    "are filtered out. Mutually exclusive with 'class_name_blacklist'.");
ABSL_FLAG(std::vector<std::string>, class_name_blacklist, {},
          "Comma-separated list of class names that acts as a blacklist. If "
          "non-empty, detections results whose 'class_name' is in this list "
          "are filtered out. Mutually exclusive with 'class_name_whitelist'.");
ABSL_FLAG(int32, num_thread, -1,
          "The number of threads to be used for TFLite ops that support "
          "multi-threading when running inference with CPU."
          "num_threads should be greater than 0 or equal to -1. Setting num_threads to "
          "-1 has the effect to let TFLite runtime set the value.");

namespace tflite {
namespace task {
namespace vision {

namespace {
// The line thickness (in pixels) for drawing the detection results.
constexpr int kLineThickness = 2;

// Window name. 
const cv::String kWindowName = "TensorFlow Lite Support Object detection example.";

// The color   used for drawing the detection results.
const cv::Scalar kBuleColor = cv::Scalar(255, 209, 0);
}  // namespace

ObjectDetectorOptions BuildOptions() {
  ObjectDetectorOptions options;
  options.mutable_model_file_with_metadata()->set_file_name(
      absl::GetFlag(FLAGS_model_path));
  options.set_max_results(absl::GetFlag(FLAGS_max_results));
  options.set_num_threads(absl::GetFlag(FLAGS_num_thread));
  if (absl::GetFlag(FLAGS_score_threshold) >
      std::numeric_limits<float>::lowest()) {
    options.set_score_threshold(absl::GetFlag(FLAGS_score_threshold));
  }
  for (const std::string& class_name :
       absl::GetFlag(FLAGS_class_name_whitelist)) {
    options.add_class_name_whitelist(class_name);
  }
  for (const std::string& class_name :
       absl::GetFlag(FLAGS_class_name_blacklist)) {
    options.add_class_name_blacklist(class_name);
  }
  return options;
}

void DrawCaption(cv::Mat& im,
                 const cv::Point& point,
                 const std::string& caption) {
    cv::putText(im, caption, point, cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(0, 0, 0), 2);
    cv::putText(im, caption, point, cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(255, 255, 255), 1);
}

absl::Status EncodeResultToMat(const DetectionResult& result,
                               cv::Mat& image) {
  for (int index = 0; index < result.detections_size(); ++index) {
    // Get bounding box as left, top, right, bottom.
    const BoundingBox& box = result.detections(index).bounding_box();
    const Detection& detection = result.detections(index);
    const int x = box.origin_x();
    const int y = box.origin_y();
    const int width = box.width();
    const int height = box.height();

    // Draw. Boxes might have coordinates outside of [0, w( x [0, h( so clamping
    // is applied.
    cv::rectangle(image, cv::Rect(x, y, width, height), kBuleColor, kLineThickness);

    // Draw. Caption.
    std::ostringstream caption;

    if (detection.classes_size() == 0) {
      caption << "  No top-1 class available";
    } else {
      const Class& classification = detection.classes(0);

      if (classification.has_class_name()) {
        caption << classification.class_name();
      } else {
        caption << classification.index();
      }
      caption << " (" << std::fixed << std::setprecision(2) << classification.score() << ")";
      DrawCaption(image, cv::Point(x-3, y), caption.str());
    }
  }

  return absl::OkStatus();
}

absl::Status Detect() {
  // Build ObjectDetector.
  const ObjectDetectorOptions& options = BuildOptions();
  ASSIGN_OR_RETURN(std::unique_ptr<ObjectDetector> object_detector,
                   ObjectDetector::CreateFromOptions(options));

  // OpenCV window setting.
  cv::namedWindow(kWindowName,
      cv::WINDOW_GUI_NORMAL | cv::WINDOW_AUTOSIZE | cv::WINDOW_KEEPRATIO);
  cv::moveWindow(kWindowName, 100, 100);

  // Opencv videocapture setting.
  cv::VideoCapture cap(0);
  auto cap_width = cap.get(cv::CAP_PROP_FRAME_WIDTH);
  auto cap_height = cap.get(cv::CAP_PROP_FRAME_HEIGHT);

  while(cap.isOpened())
  {
    cv::Mat frame, input_im;
    std::chrono::duration<double, std::milli> time_span;
    std::ostringstream time_caption;

    cap >> frame;
    cv::cvtColor(frame, input_im, cv::COLOR_BGR2RGB);

    const auto& start_time = std::chrono::steady_clock::now();

    // Load image in a FrameBuffer.
    std::unique_ptr<FrameBuffer> frame_buffer;
    frame_buffer = CreateFromRgbRawBuffer(input_im.data, {input_im.cols, input_im.rows});

    // Run object detection and draw results on input image.
    ASSIGN_OR_RETURN(DetectionResult result,
                     object_detector->Detect(*frame_buffer));
    time_span = std::chrono::steady_clock::now() - start_time;
    RETURN_IF_ERROR(EncodeResultToMat(result, frame));

    time_caption << "Inference: " << std::fixed << std::setprecision(2) << time_span.count() << " ms";
    DrawCaption(frame, cv::Point(10, 30), time_caption.str());

    // Show image and handle the keyboard before moving to the next frame
    cv::imshow(kWindowName, frame);
    const int key = cv::waitKey(1);
    if (key == 27 || key == 'q') {  // Esc or q key
      break;  // Escape
    }
  }

  return absl::OkStatus();
}

}  // namespace vision
}  // namespace task
}  // namespace tflite

int main(int argc, char** argv) {
  // Parse command line arguments and perform sanity checks.
  absl::ParseCommandLine(argc, argv);
  if (absl::GetFlag(FLAGS_model_path).empty()) {
    std::cerr << "Missing mandatory 'model_path' argument.\n";
    return 1;
  }
  if (!absl::GetFlag(FLAGS_class_name_whitelist).empty() &&
      !absl::GetFlag(FLAGS_class_name_blacklist).empty()) {
    std::cerr << "'class_name_whitelist' and 'class_name_blacklist' arguments "
                 "are mutually exclusive.\n";
    return 1;
  }

  // Run detection.
  absl::Status status = tflite::task::vision::Detect();
  if (status.ok()) {
    return 0;
  } else {
    std::cerr << "Detection failed: " << status.message() << "\n";
    return 1;
  }
}