y2022/vision/blob_detector.cc - RealtimeRoboticsGroup/test - Gitiles

 #include "y2022/vision/blob_detector.h"

 #include <cmath>
 #include <optional>
 #include <string>

 #include "aos/network/team_number.h"
 #include "aos/time/time.h"
 #include "opencv2/features2d.hpp"
 #include "opencv2/imgproc.hpp"

 DEFINE_uint64(green_delta, 50,
               "Required difference between green pixels vs. red and blue");
 DEFINE_bool(use_outdoors, false,
             "If true, change thresholds to handle outdoor illumination");

 namespace y2022 {
 namespace vision {

 cv::Mat BlobDetector::ThresholdImage(cv::Mat bgr_image) {
   cv::Mat binarized_image(cv::Size(bgr_image.cols, bgr_image.rows), CV_8UC1);
   for (int row = 0; row < bgr_image.rows; row++) {
     for (int col = 0; col < bgr_image.cols; col++) {
       cv::Vec3b pixel = bgr_image.at<cv::Vec3b>(row, col);
       uint8_t blue = pixel.val[0];
       uint8_t green = pixel.val[1];
       uint8_t red = pixel.val[2];
       // Simple filter that looks for green pixels sufficiently brigher than
       // red and blue
       if ((green > blue + FLAGS_green_delta) &&
           (green > red + FLAGS_green_delta)) {
         binarized_image.at<uint8_t>(row, col) = 255;
       } else {
         binarized_image.at<uint8_t>(row, col) = 0;
       }
     }
   }

   return binarized_image;
 }

 std::vector<std::vector<cv::Point>> BlobDetector::FindBlobs(
     cv::Mat binarized_image) {
   // find the contours (blob outlines)
   std::vector<std::vector<cv::Point>> contours;
   std::vector<cv::Vec4i> hierarchy;
   cv::findContours(binarized_image, contours, hierarchy, cv::RETR_CCOMP,
                    cv::CHAIN_APPROX_SIMPLE);

   return contours;
 }

 std::vector<BlobDetector::BlobStats> BlobDetector::ComputeStats(
     std::vector<std::vector<cv::Point>> blobs) {
   std::vector<BlobDetector::BlobStats> blob_stats;
   for (auto blob : blobs) {
     // Make the blob convex before finding bounding box
     std::vector<cv::Point> convex_blob;
     cv::convexHull(blob, convex_blob);
     auto blob_size = cv::boundingRect(convex_blob).size();
     cv::Moments moments = cv::moments(convex_blob);

     const auto centroid =
         cv::Point(moments.m10 / moments.m00, moments.m01 / moments.m00);
     const double aspect_ratio =
         static_cast<double>(blob_size.width) / blob_size.height;
     const double area = moments.m00;
     const size_t num_points = blob.size();

     blob_stats.emplace_back(
         BlobStats{centroid, aspect_ratio, area, num_points});
   }
   return blob_stats;
 }

 namespace {

 // Linear equation in the form ax + by = c
 struct Line {
  public:
   double a, b, c;

   std::optional<cv::Point2d> Intersection(const Line &l) const {
     // Use Cramer's rule to solve for the intersection
     const double denominator = Determinant(a, b, l.a, l.b);
     const double numerator_x = Determinant(c, b, l.c, l.b);
     const double numerator_y = Determinant(a, c, l.a, l.c);

     std::optional<cv::Point2d> intersection = std::nullopt;
     // Return nullopt if the denominator is 0, meaning the same slopes
     if (denominator != 0) {
       intersection =
           cv::Point2d(numerator_x / denominator, numerator_y / denominator);
     }

     return intersection;
   }

  private:  // Determinant of [[a, b], [c, d]]
   static double Determinant(double a, double b, double c, double d) {
     return (a * d) - (b * c);
   }
 };

 struct Circle {
  public:
   cv::Point2d center;
   double radius;

   static std::optional<Circle> Fit(std::vector<cv::Point2d> centroids) {
     CHECK_EQ(centroids.size(), 3ul);
     // For the 3 points, we have 3 equations in the form
     // (x - h)^2 + (y - k)^2 = r^2
     // Manipulate them to solve for the center and radius
     // (x1 - h)^2 + (y1 - k)^2 = r^2 ->
     // x1^2 + h^2 - 2x1h + y1^2 + k^2 - 2y1k = r^2
     // Also, (x2 - h)^2 + (y2 - k)^2 = r^2
     // Subtracting these two, we get
     // x1^2 - x2^2 - 2h(x1 - x2) + y1^2 - y2^2 - 2k(y1 - y2) = 0 ->
     // h(x1 - x2) + k(y1 - y2) = (-x1^2 + x2^2 - y1^2 + y2^2) / -2
     // Doing the same with equations 1 and 3, we get the second linear equation
     // h(x1 - x3) + k(y1 - y3) = (-x1^2 + x3^2 - y1^2 + y3^2) / -2
     // Now, we can solve for their intersection and find the center
     const auto l =
         Line{centroids[0].x - centroids[1].x, centroids[0].y - centroids[1].y,
              (-std::pow(centroids[0].x, 2) + std::pow(centroids[1].x, 2) -
               std::pow(centroids[0].y, 2) + std::pow(centroids[1].y, 2)) /
                  -2.0};
     const auto m =
         Line{centroids[0].x - centroids[2].x, centroids[0].y - centroids[2].y,
              (-std::pow(centroids[0].x, 2) + std::pow(centroids[2].x, 2) -
               std::pow(centroids[0].y, 2) + std::pow(centroids[2].y, 2)) /
                  -2.0};
     const auto center = l.Intersection(m);

     std::optional<Circle> circle = std::nullopt;
     if (center) {
       // Now find the radius
       const double radius = cv::norm(centroids[0] - *center);
       circle = Circle{*center, radius};
     }
     return circle;
   }

   double DistanceTo(cv::Point2d p) const {
     const auto p_prime = TranslateToOrigin(p);
     // Now, the distance is simply the difference between distance from the
     // origin to p' and the radius.
     return std::abs(cv::norm(p_prime) - radius);
   }

   bool InAngleRange(cv::Point2d p, double theta_min, double theta_max) const {
     auto p_prime = TranslateToOrigin(p);
     // Flip the y because y values go downwards.
     p_prime.y *= -1;
     const double theta = std::atan2(p_prime.y, p_prime.x);
     return (theta >= theta_min && theta <= theta_max);
   }

  private:
   // Translate the point on the circle
   // as if the circle's center is the origin (0,0)
   cv::Point2d TranslateToOrigin(cv::Point2d p) const {
     return cv::Point2d(p.x - center.x, p.y - center.y);
   }
 };

 }  // namespace

 std::pair<std::vector<std::vector<cv::Point>>, cv::Point>
 BlobDetector::FilterBlobs(std::vector<std::vector<cv::Point>> blobs,
                           std::vector<BlobDetector::BlobStats> blob_stats) {
   std::vector<std::vector<cv::Point>> filtered_blobs;
   std::vector<BlobStats> filtered_stats;

   auto blob_it = blobs.begin();
   auto stats_it = blob_stats.begin();
   while (blob_it < blobs.end() && stats_it < blob_stats.end()) {
     // To estimate the maximum y, we can figure out the y value of the blobs
     // when the camera is the farthest from the target, at the field corner.
     // We can solve for the pitch of the blob:
     // blob_pitch = atan((height_tape - height_camera) / depth) + camera_pitch
     // The triangle with the height of the tape above the camera and the camera
     // depth is similar to the one with the focal length in y pixels and the y
     // coordinate offset from the center of the image.
     // Therefore y_offset = focal_length_y * tan(blob_pitch), and
     // y = -(y_offset - offset_y)
     constexpr int kMaxY = 400;
     constexpr double kTapeAspectRatio = 5.0 / 2.0;
     constexpr double kAspectRatioThreshold = 1.6;
     constexpr double kMinArea = 10;
     constexpr size_t kMinNumPoints = 6;

     // Remove all blobs that are at the bottom of the image, have a different
     // aspect ratio than the tape, or have too little area or points.
     if ((stats_it->centroid.y <= kMaxY) &&
         (std::abs(kTapeAspectRatio - stats_it->aspect_ratio) <
          kAspectRatioThreshold) &&
         (stats_it->area >= kMinArea) &&
         (stats_it->num_points >= kMinNumPoints)) {
       filtered_blobs.push_back(*blob_it);
       filtered_stats.push_back(*stats_it);
     }
     blob_it++;
     stats_it++;
   }

   // Threshold for mean distance from a blob centroid to a circle.
   constexpr double kCircleDistanceThreshold = 5.0;
   // We should only expect to see blobs between these angles on a circle.
   constexpr double kMinBlobAngle = M_PI / 3;
   constexpr double kMaxBlobAngle = M_PI - kMinBlobAngle;
   std::vector<std::vector<cv::Point>> blob_circle;
   std::vector<cv::Point2d> centroids;

   // If we see more than this number of blobs after filtering based on
   // color/size, the circle fit may detect noise so just return no blobs.
   constexpr size_t kMinFilteredBlobs = 3;
   constexpr size_t kMaxFilteredBlobs = 50;
   if (filtered_blobs.size() >= kMinFilteredBlobs &&
       filtered_blobs.size() <= kMaxFilteredBlobs) {
     constexpr size_t kRansacIterations = 15;
     for (size_t i = 0; i < kRansacIterations; i++) {
       // Pick 3 random blobs and see how many fit on their circle
       const size_t j = std::rand() % filtered_blobs.size();
       const size_t k = std::rand() % filtered_blobs.size();
       const size_t l = std::rand() % filtered_blobs.size();

       // Restart if the random indices clash
       if ((j == k) || (j == l) || (k == l)) {
         i--;
         continue;
       }

       std::vector<std::vector<cv::Point>> current_blobs{
           filtered_blobs[j], filtered_blobs[k], filtered_blobs[l]};
       std::vector<cv::Point2d> current_centroids{filtered_stats[j].centroid,
                                                  filtered_stats[k].centroid,
                                                  filtered_stats[l].centroid};
       const std::optional<Circle> circle = Circle::Fit(current_centroids);

       // Make sure that a circle could be created from the points
       if (!circle) {
         continue;
       }

       // Only try to fit points to this circle if all of these are between
       // certain angles.
       if (circle->InAngleRange(current_centroids[0], kMinBlobAngle,
                                kMaxBlobAngle) &&
           circle->InAngleRange(current_centroids[1], kMinBlobAngle,
                                kMaxBlobAngle) &&
           circle->InAngleRange(current_centroids[2], kMinBlobAngle,
                                kMaxBlobAngle)) {
         for (size_t m = 0; m < filtered_blobs.size(); m++) {
           // Add this blob to the list if it is close to the circle, is on the
           // top half,  and isn't one of the other blobs
           if ((m != i) && (m != j) && (m != k) &&
               circle->InAngleRange(filtered_stats[m].centroid, kMinBlobAngle,
                                    kMaxBlobAngle) &&
               (circle->DistanceTo(filtered_stats[m].centroid) <
                kCircleDistanceThreshold)) {
             current_blobs.emplace_back(filtered_blobs[m]);
             current_centroids.emplace_back(filtered_stats[m].centroid);
           }
         }

         if (current_blobs.size() > blob_circle.size()) {
           blob_circle = current_blobs;
           centroids = current_centroids;
         }
       }
     }
   }

   cv::Point avg_centroid(-1, -1);
   if (centroids.size() > 0) {
     for (auto centroid : centroids) {
       avg_centroid.x += centroid.x;
       avg_centroid.y += centroid.y;
     }
     avg_centroid.x /= centroids.size();
     avg_centroid.y /= centroids.size();
   }

   return {blob_circle, avg_centroid};
 }

 void BlobDetector::DrawBlobs(
     cv::Mat view_image,
     const std::vector<std::vector<cv::Point>> &unfiltered_blobs,
     const std::vector<std::vector<cv::Point>> &filtered_blobs,
     const std::vector<BlobStats> &blob_stats, cv::Point centroid) {
   CHECK_GT(view_image.cols, 0);
   if (unfiltered_blobs.size() > 0) {
     // Draw blobs unfilled, with red color border
     cv::drawContours(view_image, unfiltered_blobs, -1, cv::Scalar(0, 0, 255),
                      0);
   }

   cv::drawContours(view_image, filtered_blobs, -1, cv::Scalar(0, 255, 0),
                    cv::FILLED);

   // Draw blob centroids
   for (auto stats : blob_stats) {
     cv::circle(view_image, stats.centroid, 2, cv::Scalar(255, 0, 0),
                cv::FILLED);
   }

   // Draw average centroid
   cv::circle(view_image, centroid, 3, cv::Scalar(255, 255, 0), cv::FILLED);
 }

 void BlobDetector::ExtractBlobs(cv::Mat bgr_image,
                                 BlobDetector::BlobResult *blob_result) {
   auto start = aos::monotonic_clock::now();
   blob_result->binarized_image = ThresholdImage(bgr_image);
   blob_result->unfiltered_blobs = FindBlobs(blob_result->binarized_image);
   blob_result->blob_stats = ComputeStats(blob_result->unfiltered_blobs);
   auto filtered_pair =
       FilterBlobs(blob_result->unfiltered_blobs, blob_result->blob_stats);
   blob_result->filtered_blobs = filtered_pair.first;
   blob_result->centroid = filtered_pair.second;
   auto end = aos::monotonic_clock::now();
   LOG(INFO) << "Blob detection elapsed time: "
             << std::chrono::duration<double, std::milli>(end - start).count()
             << " ms";
 }

 }  // namespace vision
 }  // namespace y2022
	#include "y2022/vision/blob_detector.h"

	#include <cmath>
	#include <optional>
	#include <string>

	#include "aos/network/team_number.h"
	#include "aos/time/time.h"
	#include "opencv2/features2d.hpp"
	#include "opencv2/imgproc.hpp"

	DEFINE_uint64(green_delta, 50,
	"Required difference between green pixels vs. red and blue");
	DEFINE_bool(use_outdoors, false,
	"If true, change thresholds to handle outdoor illumination");

	namespace y2022 {
	namespace vision {

	cv::Mat BlobDetector::ThresholdImage(cv::Mat bgr_image) {
	cv::Mat binarized_image(cv::Size(bgr_image.cols, bgr_image.rows), CV_8UC1);
	for (int row = 0; row < bgr_image.rows; row++) {
	for (int col = 0; col < bgr_image.cols; col++) {
	cv::Vec3b pixel = bgr_image.at<cv::Vec3b>(row, col);
	uint8_t blue = pixel.val[0];
	uint8_t green = pixel.val[1];
	uint8_t red = pixel.val[2];
	// Simple filter that looks for green pixels sufficiently brigher than
	// red and blue
	if ((green > blue + FLAGS_green_delta) &&
	(green > red + FLAGS_green_delta)) {
	binarized_image.at<uint8_t>(row, col) = 255;
	} else {
	binarized_image.at<uint8_t>(row, col) = 0;
	}
	}
	}

	return binarized_image;
	}

	std::vector<std::vector<cv::Point>> BlobDetector::FindBlobs(
	cv::Mat binarized_image) {
	// find the contours (blob outlines)
	std::vector<std::vector<cv::Point>> contours;
	std::vector<cv::Vec4i> hierarchy;
	cv::findContours(binarized_image, contours, hierarchy, cv::RETR_CCOMP,
	cv::CHAIN_APPROX_SIMPLE);

	return contours;
	}

	std::vector<BlobDetector::BlobStats> BlobDetector::ComputeStats(
	std::vector<std::vector<cv::Point>> blobs) {
	std::vector<BlobDetector::BlobStats> blob_stats;
	for (auto blob : blobs) {
	// Make the blob convex before finding bounding box
	std::vector<cv::Point> convex_blob;
	cv::convexHull(blob, convex_blob);
	auto blob_size = cv::boundingRect(convex_blob).size();
	cv::Moments moments = cv::moments(convex_blob);

	const auto centroid =
	cv::Point(moments.m10 / moments.m00, moments.m01 / moments.m00);
	const double aspect_ratio =
	static_cast<double>(blob_size.width) / blob_size.height;
	const double area = moments.m00;
	const size_t num_points = blob.size();

	blob_stats.emplace_back(
	BlobStats{centroid, aspect_ratio, area, num_points});
	}
	return blob_stats;
	}

	namespace {

	// Linear equation in the form ax + by = c
	struct Line {
	public:
	double a, b, c;

	std::optional<cv::Point2d> Intersection(const Line &l) const {
	// Use Cramer's rule to solve for the intersection
	const double denominator = Determinant(a, b, l.a, l.b);
	const double numerator_x = Determinant(c, b, l.c, l.b);
	const double numerator_y = Determinant(a, c, l.a, l.c);

	std::optional<cv::Point2d> intersection = std::nullopt;
	// Return nullopt if the denominator is 0, meaning the same slopes
	if (denominator != 0) {
	intersection =
	cv::Point2d(numerator_x / denominator, numerator_y / denominator);
	}

	return intersection;
	}

	private: // Determinant of [[a, b], [c, d]]
	static double Determinant(double a, double b, double c, double d) {
	return (a * d) - (b * c);
	}
	};

	struct Circle {
	public:
	cv::Point2d center;
	double radius;

	static std::optional<Circle> Fit(std::vector<cv::Point2d> centroids) {
	CHECK_EQ(centroids.size(), 3ul);
	// For the 3 points, we have 3 equations in the form
	// (x - h)^2 + (y - k)^2 = r^2
	// Manipulate them to solve for the center and radius
	// (x1 - h)^2 + (y1 - k)^2 = r^2 ->
	// x1^2 + h^2 - 2x1h + y1^2 + k^2 - 2y1k = r^2
	// Also, (x2 - h)^2 + (y2 - k)^2 = r^2
	// Subtracting these two, we get
	// x1^2 - x2^2 - 2h(x1 - x2) + y1^2 - y2^2 - 2k(y1 - y2) = 0 ->
	// h(x1 - x2) + k(y1 - y2) = (-x1^2 + x2^2 - y1^2 + y2^2) / -2
	// Doing the same with equations 1 and 3, we get the second linear equation
	// h(x1 - x3) + k(y1 - y3) = (-x1^2 + x3^2 - y1^2 + y3^2) / -2
	// Now, we can solve for their intersection and find the center
	const auto l =
	Line{centroids[0].x - centroids[1].x, centroids[0].y - centroids[1].y,
	(-std::pow(centroids[0].x, 2) + std::pow(centroids[1].x, 2) -
	std::pow(centroids[0].y, 2) + std::pow(centroids[1].y, 2)) /
	-2.0};
	const auto m =
	Line{centroids[0].x - centroids[2].x, centroids[0].y - centroids[2].y,
	(-std::pow(centroids[0].x, 2) + std::pow(centroids[2].x, 2) -
	std::pow(centroids[0].y, 2) + std::pow(centroids[2].y, 2)) /
	-2.0};
	const auto center = l.Intersection(m);

	std::optional<Circle> circle = std::nullopt;
	if (center) {
	// Now find the radius
	const double radius = cv::norm(centroids[0] - *center);
	circle = Circle{*center, radius};
	}
	return circle;
	}

	double DistanceTo(cv::Point2d p) const {
	const auto p_prime = TranslateToOrigin(p);
	// Now, the distance is simply the difference between distance from the
	// origin to p' and the radius.
	return std::abs(cv::norm(p_prime) - radius);
	}

	bool InAngleRange(cv::Point2d p, double theta_min, double theta_max) const {
	auto p_prime = TranslateToOrigin(p);
	// Flip the y because y values go downwards.
	p_prime.y *= -1;
	const double theta = std::atan2(p_prime.y, p_prime.x);
	return (theta >= theta_min && theta <= theta_max);
	}

	private:
	// Translate the point on the circle
	// as if the circle's center is the origin (0,0)
	cv::Point2d TranslateToOrigin(cv::Point2d p) const {
	return cv::Point2d(p.x - center.x, p.y - center.y);
	}
	};

	} // namespace

	std::pair<std::vector<std::vector<cv::Point>>, cv::Point>
	BlobDetector::FilterBlobs(std::vector<std::vector<cv::Point>> blobs,
	std::vector<BlobDetector::BlobStats> blob_stats) {
	std::vector<std::vector<cv::Point>> filtered_blobs;
	std::vector<BlobStats> filtered_stats;

	auto blob_it = blobs.begin();
	auto stats_it = blob_stats.begin();
	while (blob_it < blobs.end() && stats_it < blob_stats.end()) {
	// To estimate the maximum y, we can figure out the y value of the blobs
	// when the camera is the farthest from the target, at the field corner.
	// We can solve for the pitch of the blob:
	// blob_pitch = atan((height_tape - height_camera) / depth) + camera_pitch
	// The triangle with the height of the tape above the camera and the camera
	// depth is similar to the one with the focal length in y pixels and the y
	// coordinate offset from the center of the image.
	// Therefore y_offset = focal_length_y * tan(blob_pitch), and
	// y = -(y_offset - offset_y)
	constexpr int kMaxY = 400;
	constexpr double kTapeAspectRatio = 5.0 / 2.0;
	constexpr double kAspectRatioThreshold = 1.6;
	constexpr double kMinArea = 10;
	constexpr size_t kMinNumPoints = 6;

	// Remove all blobs that are at the bottom of the image, have a different
	// aspect ratio than the tape, or have too little area or points.
	if ((stats_it->centroid.y <= kMaxY) &&
	(std::abs(kTapeAspectRatio - stats_it->aspect_ratio) <
	kAspectRatioThreshold) &&
	(stats_it->area >= kMinArea) &&
	(stats_it->num_points >= kMinNumPoints)) {
	filtered_blobs.push_back(*blob_it);
	filtered_stats.push_back(*stats_it);
	}
	blob_it++;
	stats_it++;
	}

	// Threshold for mean distance from a blob centroid to a circle.
	constexpr double kCircleDistanceThreshold = 5.0;
	// We should only expect to see blobs between these angles on a circle.
	constexpr double kMinBlobAngle = M_PI / 3;
	constexpr double kMaxBlobAngle = M_PI - kMinBlobAngle;
	std::vector<std::vector<cv::Point>> blob_circle;
	std::vector<cv::Point2d> centroids;

	// If we see more than this number of blobs after filtering based on
	// color/size, the circle fit may detect noise so just return no blobs.
	constexpr size_t kMinFilteredBlobs = 3;
	constexpr size_t kMaxFilteredBlobs = 50;
	if (filtered_blobs.size() >= kMinFilteredBlobs &&
	filtered_blobs.size() <= kMaxFilteredBlobs) {
	constexpr size_t kRansacIterations = 15;
	for (size_t i = 0; i < kRansacIterations; i++) {
	// Pick 3 random blobs and see how many fit on their circle
	const size_t j = std::rand() % filtered_blobs.size();
	const size_t k = std::rand() % filtered_blobs.size();
	const size_t l = std::rand() % filtered_blobs.size();

	// Restart if the random indices clash
	if ((j == k) \|\| (j == l) \|\| (k == l)) {
	i--;
	continue;
	}

	std::vector<std::vector<cv::Point>> current_blobs{
	filtered_blobs[j], filtered_blobs[k], filtered_blobs[l]};
	std::vector<cv::Point2d> current_centroids{filtered_stats[j].centroid,
	filtered_stats[k].centroid,
	filtered_stats[l].centroid};
	const std::optional<Circle> circle = Circle::Fit(current_centroids);

	// Make sure that a circle could be created from the points
	if (!circle) {
	continue;
	}

	// Only try to fit points to this circle if all of these are between
	// certain angles.
	if (circle->InAngleRange(current_centroids[0], kMinBlobAngle,
	kMaxBlobAngle) &&
	circle->InAngleRange(current_centroids[1], kMinBlobAngle,
	kMaxBlobAngle) &&
	circle->InAngleRange(current_centroids[2], kMinBlobAngle,
	kMaxBlobAngle)) {
	for (size_t m = 0; m < filtered_blobs.size(); m++) {
	// Add this blob to the list if it is close to the circle, is on the
	// top half, and isn't one of the other blobs
	if ((m != i) && (m != j) && (m != k) &&
	circle->InAngleRange(filtered_stats[m].centroid, kMinBlobAngle,
	kMaxBlobAngle) &&
	(circle->DistanceTo(filtered_stats[m].centroid) <
	kCircleDistanceThreshold)) {
	current_blobs.emplace_back(filtered_blobs[m]);
	current_centroids.emplace_back(filtered_stats[m].centroid);
	}
	}

	if (current_blobs.size() > blob_circle.size()) {
	blob_circle = current_blobs;
	centroids = current_centroids;
	}
	}
	}
	}

	cv::Point avg_centroid(-1, -1);
	if (centroids.size() > 0) {
	for (auto centroid : centroids) {
	avg_centroid.x += centroid.x;
	avg_centroid.y += centroid.y;
	}
	avg_centroid.x /= centroids.size();
	avg_centroid.y /= centroids.size();
	}

	return {blob_circle, avg_centroid};
	}

	void BlobDetector::DrawBlobs(
	cv::Mat view_image,
	const std::vector<std::vector<cv::Point>> &unfiltered_blobs,
	const std::vector<std::vector<cv::Point>> &filtered_blobs,
	const std::vector<BlobStats> &blob_stats, cv::Point centroid) {
	CHECK_GT(view_image.cols, 0);
	if (unfiltered_blobs.size() > 0) {
	// Draw blobs unfilled, with red color border
	cv::drawContours(view_image, unfiltered_blobs, -1, cv::Scalar(0, 0, 255),
	0);
	}

	cv::drawContours(view_image, filtered_blobs, -1, cv::Scalar(0, 255, 0),
	cv::FILLED);

	// Draw blob centroids
	for (auto stats : blob_stats) {
	cv::circle(view_image, stats.centroid, 2, cv::Scalar(255, 0, 0),
	cv::FILLED);
	}

	// Draw average centroid
	cv::circle(view_image, centroid, 3, cv::Scalar(255, 255, 0), cv::FILLED);
	}

	void BlobDetector::ExtractBlobs(cv::Mat bgr_image,
	BlobDetector::BlobResult *blob_result) {
	auto start = aos::monotonic_clock::now();
	blob_result->binarized_image = ThresholdImage(bgr_image);
	blob_result->unfiltered_blobs = FindBlobs(blob_result->binarized_image);
	blob_result->blob_stats = ComputeStats(blob_result->unfiltered_blobs);
	auto filtered_pair =
	FilterBlobs(blob_result->unfiltered_blobs, blob_result->blob_stats);
	blob_result->filtered_blobs = filtered_pair.first;
	blob_result->centroid = filtered_pair.second;
	auto end = aos::monotonic_clock::now();
	LOG(INFO) << "Blob detection elapsed time: "
	<< std::chrono::duration<double, std::milli>(end - start).count()
	<< " ms";
	}

	} // namespace vision
	} // namespace y2022