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273 changes: 273 additions & 0 deletions
273
photon-core/src/main/java/org/photonvision/vision/pipeline/DnnPipeline.java
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| /* | ||
| * Copyright (C) Photon Vision. | ||
| * | ||
| * This program is free software: you can redistribute it and/or modify | ||
| * it under the terms of the GNU General Public License as published by | ||
| * the Free Software Foundation, either version 3 of the License, or | ||
| * (at your option) any later version. | ||
| * | ||
| * This program is distributed in the hope that it will be useful, | ||
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| * GNU General Public License for more details. | ||
| * | ||
| * You should have received a copy of the GNU General Public License | ||
| * along with this program. If not, see <https://www.gnu.org/licenses/>. | ||
| */ | ||
|
|
||
| package org.photonvision.vision.pipeline; | ||
|
|
||
| import java.util.ArrayList; | ||
| import java.util.List; | ||
| import org.opencv.core.Core; | ||
| import org.opencv.core.Mat; | ||
| import org.opencv.core.MatOfFloat; | ||
| import org.opencv.core.MatOfInt; | ||
| import org.opencv.core.MatOfRect2d; | ||
| import org.opencv.core.Point; | ||
| import org.opencv.core.Rect2d; | ||
| import org.opencv.core.Scalar; | ||
| import org.opencv.core.Size; | ||
| import org.opencv.dnn.Dnn; | ||
| import org.opencv.dnn.Net; | ||
| import org.opencv.imgproc.Imgproc; | ||
| import org.opencv.utils.Converters; | ||
| import org.photonvision.common.util.ColorHelper; | ||
| import org.photonvision.vision.frame.Frame; | ||
| import org.photonvision.vision.frame.FrameThresholdType; | ||
| import org.photonvision.vision.pipe.impl.*; | ||
| import org.photonvision.vision.pipeline.result.CVPipelineResult; | ||
| import org.photonvision.vision.target.TrackedTarget; | ||
| import org.photonvision.vision.target.TrackedTarget.TargetCalculationParameters; | ||
|
|
||
| public class DnnPipeline extends CVPipeline<CVPipelineResult, DnnPipelineSettings> { | ||
| private final CalculateFPSPipe calculateFPSPipe = new CalculateFPSPipe(); | ||
|
|
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| private static final FrameThresholdType PROCESSING_TYPE = FrameThresholdType.NONE; | ||
|
|
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| public DnnPipeline() { | ||
| super(PROCESSING_TYPE); | ||
| settings = new DnnPipelineSettings(); | ||
| } | ||
|
|
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| Net net = null; | ||
| private List<String> outBlobNames = List.of(); | ||
|
|
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| private List<String> coco_names; | ||
|
|
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| public DnnPipeline(DnnPipelineSettings settings) { | ||
| super(PROCESSING_TYPE); | ||
| this.settings = settings; | ||
|
|
||
| // Downloaded from https://dev.to/kojix2/yolov7-object-detection-in-ruby-in-10-minutes-5cjh | ||
| // https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/307_YOLOv7/with-postprocess/resources_post.tar.gz | ||
| try { | ||
| // this.net = Dnn.readNetFromONNX("/home/matt/Downloads/best_1.onnx"); | ||
| // this.net = Dnn.readNet("/home/matt/Downloads/yolov7_post_640x640.onnx"); | ||
| this.net = | ||
| Dnn.readNetFromDarknet( | ||
| "/home/matt/Downloads/yolov4-csp-swish.cfg", | ||
| "/home/matt/Downloads/yolov4-csp-swish.weights"); | ||
| Core.setNumThreads(8); | ||
| } catch (Exception e) { | ||
| System.out.println(e); | ||
| } | ||
| this.outBlobNames = getOutputNames(net); | ||
|
|
||
| this.coco_names = | ||
| List.of( | ||
| "person", | ||
| "bicycle", | ||
| "car", | ||
| "motorcycle", | ||
| "airplane", | ||
| "bus", | ||
| "train", | ||
| "truck", | ||
| "boat", | ||
| "traffic light", | ||
| "fire hydrant", | ||
| "stop sign", | ||
| "parking meter", | ||
| "bench", | ||
| "bird", | ||
| "cat", | ||
| "dog", | ||
| "horse", | ||
| "sheep", | ||
| "cow", | ||
| "elephant", | ||
| "bear", | ||
| "zebra", | ||
| "giraffe", | ||
| "backpack", | ||
| "umbrella", | ||
| "handbag", | ||
| "tie", | ||
| "suitcase", | ||
| "frisbee", | ||
| "skis", | ||
| "snowboard", | ||
| "sports ball", | ||
| "kite", | ||
| "baseball bat", | ||
| "baseball glove", | ||
| "skateboard", | ||
| "surfboard", | ||
| "tennis racket", | ||
| "bottle", | ||
| "wine glass", | ||
| "cup", | ||
| "fork", | ||
| "knife", | ||
| "spoon", | ||
| "bowl", | ||
| "banana", | ||
| "apple", | ||
| "sandwich", | ||
| "orange", | ||
| "broccoli", | ||
| "carrot", | ||
| "hot dog", | ||
| "pizza", | ||
| "donut", | ||
| "cake", | ||
| "chair", | ||
| "couch", | ||
| "potted plant", | ||
| "bed", | ||
| "dining table", | ||
| "toilet", | ||
| "tv", | ||
| "laptop", | ||
| "mouse", | ||
| "remote", | ||
| "keyboard", | ||
| "cell phone", | ||
| "microwave", | ||
| "oven", | ||
| "toaster", | ||
| "sink", | ||
| "refrigerator", | ||
| "book", | ||
| "clock", | ||
| "vase", | ||
| "scissors", | ||
| "teddy bear", | ||
| "hair drier", | ||
| "toothbrush"); | ||
| } | ||
|
|
||
| @Override | ||
| protected void setPipeParamsImpl() {} | ||
|
|
||
| private static List<String> getOutputNames(Net net) { | ||
| List<String> names = new ArrayList<>(); | ||
|
|
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| List<Integer> outLayers = net.getUnconnectedOutLayers().toList(); | ||
| List<String> layersNames = net.getLayerNames(); | ||
|
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| outLayers.forEach( | ||
| (item) -> names.add(layersNames.get(item - 1))); // unfold and create R-CNN layers from the | ||
| // loaded YOLO model// | ||
| return names; | ||
| } | ||
|
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||
| @Override | ||
| protected CVPipelineResult process(Frame input_frame, DnnPipelineSettings settings) { | ||
| long sumPipeNanosElapsed = 0L; | ||
|
|
||
| // ====================== | ||
|
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| var frame = input_frame.colorImage.getMat(); | ||
|
|
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| if (frame.empty()) { | ||
| return new CVPipelineResult(sumPipeNanosElapsed, 0, List.of(), input_frame); | ||
| } | ||
|
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| var blob = Dnn.blobFromImage(frame, 1.0 / 255.0, new Size(640, 640)); | ||
| net.setInput(blob); | ||
|
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| List<Mat> result = new ArrayList<>(); | ||
| net.forward(result, outBlobNames); // outputlayer : output1 and output2 | ||
|
|
||
| // From https://github.com/suddh123/YOLO-object-detection-in-java/blob/code/yolo.java | ||
|
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| float confThreshold = 0.3f; // Insert thresholding beyond which the model will detect objects// | ||
| List<Integer> clsIds = new ArrayList<>(); | ||
| List<Float> confs = new ArrayList<>(); | ||
| List<Rect2d> rects = new ArrayList<>(); | ||
| for (int i = 0; i < result.size(); ++i) { | ||
| // each row is a candidate detection, the 1st 4 numbers are | ||
| // [center_x, center_y, width, height], followed by (N-4) class probabilities | ||
| Mat level = result.get(i); | ||
| for (int j = 0; j < level.rows(); ++j) { | ||
| Mat row = level.row(j); | ||
| Mat scores = row.colRange(5, level.cols()); | ||
| Core.MinMaxLocResult mm = Core.minMaxLoc(scores); | ||
| float confidence = (float) mm.maxVal; | ||
| Point classIdPoint = mm.maxLoc; | ||
| if (confidence > confThreshold) { | ||
| // scaling for drawing the bounding boxes// | ||
| int centerX = (int) (row.get(0, 0)[0] * frame.cols()); | ||
| int centerY = (int) (row.get(0, 1)[0] * frame.rows()); | ||
| int width = (int) (row.get(0, 2)[0] * frame.cols()); | ||
| int height = (int) (row.get(0, 3)[0] * frame.rows()); | ||
| int left = centerX - width / 2; | ||
| int top = centerY - height / 2; | ||
|
|
||
| clsIds.add((int) classIdPoint.x); | ||
| confs.add((float) confidence); | ||
| rects.add(new Rect2d(left, top, width, height)); | ||
| } | ||
| } | ||
| } | ||
| float nmsThresh = 0.5f; | ||
| MatOfFloat confidences = new MatOfFloat(Converters.vector_float_to_Mat(confs)); | ||
| Rect2d[] boxesArray = rects.toArray(new Rect2d[0]); | ||
| MatOfRect2d boxes = new MatOfRect2d(boxesArray); | ||
| MatOfInt indices = new MatOfInt(); | ||
| Dnn.NMSBoxes( | ||
| boxes, | ||
| confidences, | ||
| confThreshold, | ||
| nmsThresh, | ||
| indices); // We draw the bounding boxes for objects | ||
| // here// | ||
|
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| List<TrackedTarget> targetList = new ArrayList<>(); | ||
|
|
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| int[] ind = indices.toArray(); | ||
| for (int i = 0; i < ind.length; ++i) { | ||
| int idx = ind[i]; | ||
| var box = boxesArray[idx]; | ||
| Imgproc.rectangle(frame, box.tl(), box.br(), new Scalar(0, 0, 255), 2); | ||
|
|
||
| var name = String.format("%s (%f)", coco_names.get(clsIds.get(idx)), confs.get(idx)); | ||
|
|
||
| Imgproc.putText( | ||
| frame, | ||
| name, | ||
| new Point(box.x + box.width / 2.0, box.y + box.height / 2.0), | ||
| 0, | ||
| 0.6, | ||
| ColorHelper.colorToScalar(java.awt.Color.white), | ||
| 2); | ||
|
|
||
| targetList.add( | ||
| new TrackedTarget( | ||
| box, | ||
| clsIds.get(idx), | ||
| confs.get(idx), | ||
| new TargetCalculationParameters( | ||
| false, null, null, null, null, frameStaticProperties))); | ||
| } | ||
|
|
||
| // ====================== | ||
|
|
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| var fpsResult = calculateFPSPipe.run(null); | ||
| var fps = fpsResult.output; | ||
|
|
||
| return new CVPipelineResult(sumPipeNanosElapsed, fps, targetList, input_frame); | ||
| } | ||
| } |
24 changes: 24 additions & 0 deletions
24
photon-core/src/main/java/org/photonvision/vision/pipeline/DnnPipelineSettings.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,24 @@ | ||
| /* | ||
| * Copyright (C) Photon Vision. | ||
| * | ||
| * This program is free software: you can redistribute it and/or modify | ||
| * it under the terms of the GNU General Public License as published by | ||
| * the Free Software Foundation, either version 3 of the License, or | ||
| * (at your option) any later version. | ||
| * | ||
| * This program is distributed in the hope that it will be useful, | ||
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| * GNU General Public License for more details. | ||
| * | ||
| * You should have received a copy of the GNU General Public License | ||
| * along with this program. If not, see <https://www.gnu.org/licenses/>. | ||
| */ | ||
|
|
||
| package org.photonvision.vision.pipeline; | ||
|
|
||
| public class DnnPipelineSettings extends CVPipelineSettings { | ||
| public DnnPipelineSettings() { | ||
| this.pipelineType = PipelineType.Dnn; | ||
| } | ||
| } |
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