Setting up Blob detector and viewer code in y2022
Includes viewer with SIFT code stripped out, since we're not using it
in y2022.
Change-Id: Id56e9e5c868a1d730dcfb433d5e4868898a6f011
Signed-off-by: Jim Ostrowski <yimmy13@gmail.com>
diff --git a/y2022/vision/blob_detector.cc b/y2022/vision/blob_detector.cc
new file mode 100644
index 0000000..bf5afd7
--- /dev/null
+++ b/y2022/vision/blob_detector.cc
@@ -0,0 +1,98 @@
+#include "y2022/vision/blob_detector.h"
+
+#include <opencv2/imgproc.hpp>
+
+#include "aos/network/team_number.h"
+
+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 rgb_image) {
+ cv::Mat gray_image(cv::Size(rgb_image.cols, rgb_image.rows), CV_8UC1);
+ for (int row = 0; row < rgb_image.rows; row++) {
+ for (int col = 0; col < rgb_image.cols; col++) {
+ cv::Vec3b pixel = rgb_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 + 30) && (green > red + 50)) {
+ gray_image.at<uint8_t>(row, col) = 255;
+ } else {
+ gray_image.at<uint8_t>(row, col) = 0;
+ }
+ }
+ }
+
+ return gray_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;
+}
+
+// Filter blobs to get rid of noise, too large items, etc.
+std::vector<std::vector<cv::Point>> BlobDetector::FilterBlobs(
+ std::vector<std::vector<cv::Point>> blobs) {
+ // TODO: Put in some filters
+
+ std::vector<std::vector<cv::Point>> filtered_blobs;
+ for (auto blob : blobs) {
+ // for now, let's remove all blobs that are at the bottom of the image
+ if (blob[0].y < 400) {
+ filtered_blobs.push_back(blob);
+ } else {
+ // LOG(INFO) << "Found and removed blob";
+ }
+ }
+ return filtered_blobs;
+}
+
+void BlobDetector::DrawBlobs(
+ cv::Mat view_image, std::vector<std::vector<cv::Point>> unfiltered_blobs,
+ std::vector<std::vector<cv::Point>> filtered_blobs) {
+ CHECK_GT(view_image.cols, 0);
+ if (unfiltered_blobs.size() > 0) {
+ // Draw blobs unfilled, with red color border
+ drawContours(view_image, unfiltered_blobs, -1, cv::Scalar(0, 0, 255), 0);
+ }
+
+ drawContours(view_image, filtered_blobs, -1, cv::Scalar(0, 255, 0),
+ cv::FILLED);
+}
+
+std::vector<std::vector<cv::Point>> BlobDetector::ComputeStats(
+ std::vector<std::vector<cv::Point>> blobs) {
+ // Placeholder for now for this
+ // TODO<Jim>: need to compute stats on blobs, like centroid, aspect
+ // ratio, bounding box
+ return blobs;
+}
+
+void BlobDetector::ExtractBlobs(
+ cv::Mat rgb_image, cv::Mat binarized_image, cv::Mat blob_image,
+ std::vector<std::vector<cv::Point>> &filtered_blobs,
+ std::vector<std::vector<cv::Point>> &unfiltered_blobs,
+ std::vector<std::vector<cv::Point>> &blob_stats) {
+ binarized_image = ThresholdImage(rgb_image);
+ unfiltered_blobs = FindBlobs(binarized_image);
+ filtered_blobs = FilterBlobs(unfiltered_blobs);
+ DrawBlobs(blob_image, unfiltered_blobs, filtered_blobs);
+ blob_stats = ComputeStats(filtered_blobs);
+}
+
+} // namespace vision
+} // namespace y2022