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Merge pull request #26486 from gursimarsingh:object_detection_engine_update
Code Fixes and changed post processing based on models.yml in Object Detection Sample #26486 ## Major Changes 1. Changes to add findModel support for config file in models like yolov4, yolov4-tiny, yolov3, ssd_caffe, tiny-yolo-voc, ssd_tf and faster_rcnn_tf. 2. Added new model and config download links for ssd_caffe, as previous links were not working. 3. Switched to DNN ENGINE_CLASSIC for non-cpu convig as new engine does not support it. 4. Fixes in python sample related to yolov5 usage. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Gursimar Singh
GitHub
parent
b476ed6d06
commit
efbe580ff3
@ -62,7 +62,12 @@ std::string genArgument(const std::string& argName, const std::string& help,
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FileNode value = node[argName];
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if (argName.find("sha1") != std::string::npos) {
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std::string prefix = argName.substr(0, argName.find("sha1"));
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value = node[prefix+"load_info"][argName];
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if (prefix == "config_"){
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value = node[prefix+"load_info"]["sha1"];
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}
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else{
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value = node[prefix+"load_info"][argName];
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}
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}
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if (argName.find("download_sha") != std::string::npos) {
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std::string prefix = argName.substr(0, argName.find("download_sha"));
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@ -183,6 +188,8 @@ std::string genPreprocArguments(const std::string& modelName, const std::string&
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modelName, zooFile)+
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genArgument(prefix + "sha1", "Optional path to hashsum of downloaded model to be loaded from models.yml",
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modelName, zooFile)+
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genArgument(prefix + "config_sha1", "Optional path to hashsum of downloaded config to be loaded from models.yml",
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modelName, zooFile)+
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genArgument(prefix + "download_sha", "Optional path to hashsum of downloaded model to be loaded from models.yml",
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modelName, zooFile);
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}
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@ -19,7 +19,10 @@ def add_argument(zoo, parser, name, help, required=False, default=None, type=Non
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if "sha1" in name:
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prefix = name.replace("sha1", "")
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value = node.getNode(prefix + "load_info")
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value = value.getNode(name)
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if prefix == "config_":
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value = value.getNode("sha1")
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else:
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value = value.getNode(name)
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if "download_sha" in name:
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prefix = name.replace("download_sha", "")
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value = node.getNode(prefix + "load_info")
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@ -97,6 +100,8 @@ def add_preproc_args(zoo, parser, sample, alias=None, prefix=""):
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help='An index of background class in predictions. If not negative, exclude such class from list of classes.', alias=alias)
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add_argument(zoo, parser, prefix+'sha1', type=str,
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help='Optional path to hashsum of downloaded model to be loaded from models.yml', alias=alias)
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add_argument(zoo, parser, prefix+'config_sha1', type=str,
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help='Optional path to hashsum of downloaded config to be loaded from models.yml', alias=alias)
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add_argument(zoo, parser, prefix+'download_sha', type=str,
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help='Optional path to hashsum of downloaded model to be loaded from models.yml', alias=alias)
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@ -89,8 +89,8 @@ yolov5l:
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width: 640
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height: 640
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rgb: true
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classes: "object_detection_classes_yolo.txt"
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background_label_id: 0
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labels: "object_detection_classes_yolo.txt"
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postprocessing: "yolov5"
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sample: "object_detection"
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# YOLO4 object detection family from Darknet (https://github.com/AlexeyAB/darknet)
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@ -112,6 +112,7 @@ yolov4:
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rgb: true
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labels: "object_detection_classes_yolo.txt"
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background_label_id: 0
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postprocessing: "darknet"
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sample: "object_detection"
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yolov4-tiny:
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@ -130,6 +131,7 @@ yolov4-tiny:
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rgb: true
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labels: "object_detection_classes_yolo.txt"
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background_label_id: 0
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postprocessing: "darknet"
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sample: "object_detection"
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yolov3:
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@ -148,6 +150,7 @@ yolov3:
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rgb: true
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labels: "object_detection_classes_yolo.txt"
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background_label_id: 0
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postprocessing: "darknet"
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sample: "object_detection"
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tiny-yolo-voc:
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@ -166,14 +169,18 @@ tiny-yolo-voc:
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rgb: true
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labels: "object_detection_classes_pascal_voc.txt"
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background_label_id: 0
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postprocessing: "darknet"
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sample: "object_detection"
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# Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD
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# Caffe implementation of SSD model from https://github.com/PINTO0309/MobileNet-SSD-RealSense
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ssd_caffe:
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load_info:
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url: "https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc"
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url: "https://github.com/PINTO0309/MobileNet-SSD-RealSense/raw/refs/heads/master/caffemodel/MobileNetSSD/MobileNetSSD_deploy.caffemodel"
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sha1: "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a"
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model: "MobileNetSSD_deploy.caffemodel"
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config_load_info:
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url: "https://github.com/PINTO0309/MobileNet-SSD-RealSense/raw/refs/heads/master/caffemodel/MobileNetSSD/MobileNetSSD_deploy.prototxt"
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sha1: "25c8404cecdef638c2bd9ac7f3b46a8b96897deb"
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config: "MobileNetSSD_deploy.prototxt"
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mean: [127.5, 127.5, 127.5]
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scale: 0.007843
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@ -181,6 +188,7 @@ ssd_caffe:
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height: 300
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rgb: false
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labels: "object_detection_classes_pascal_voc.txt"
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postprocessing: "ssd"
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sample: "object_detection"
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# TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection
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@ -202,6 +210,7 @@ ssd_tf:
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height: 300
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rgb: true
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labels: "object_detection_classes_coco.txt"
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postprocessing: "ssd"
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sample: "object_detection"
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# TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection
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@ -222,6 +231,7 @@ faster_rcnn_tf:
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width: 800
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height: 600
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rgb: true
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postprocessing: "ssd"
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sample: "object_detection"
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################################################################################
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@ -76,7 +76,7 @@ string modelName, framework;
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static void preprocess(const Mat& frame, Net& net, Size inpSize);
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static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name);
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static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing);
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static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
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@ -91,7 +91,7 @@ static void yoloPostProcessing(
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vector<Rect2d>& keep_boxes,
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float conf_threshold,
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float iou_threshold,
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const string& yolo_name);
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const string& postprocessing);
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static void printAliases(string& zooFile){
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vector<string> aliases = findAliases(zooFile, "object_detection");
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@ -195,12 +195,13 @@ int main(int argc, char** argv)
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inpHeight = parser.get<int>("height");
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int async = parser.get<int>("async");
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paddingValue = parser.get<float>("padvalue");
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const string yolo_name = parser.get<String>("postprocessing");
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const string postprocessing = parser.get<String>("postprocessing");
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paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
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//![preprocess_params]
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String sha1 = parser.get<String>("sha1");
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String config_sha1 = parser.get<String>("config_sha1");
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const string modelPath = findModel(parser.get<String>("model"), sha1);
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const string configPath = findFile(parser.get<String>("config"));
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const string configPath = findModel(parser.get<String>("config"), config_sha1);
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framework = modelPath.substr(modelPath.rfind('.') + 1);
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if (parser.has("labels"))
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@ -216,7 +217,11 @@ int main(int argc, char** argv)
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}
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}
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//![read_net]
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Net net = readNet(modelPath, configPath);
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EngineType engine = ENGINE_AUTO;
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if ((parser.get<String>("backend") != "default") || (parser.get<String>("target") != "cpu")){
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engine = ENGINE_CLASSIC;
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}
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Net net = readNet(modelPath, configPath, "", engine);
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int backend = getBackendID(parser.get<String>("backend"));
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net.setPreferableBackend(backend);
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net.setPreferableTarget(getTargetID(parser.get<String>("target")));
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@ -230,7 +235,7 @@ int main(int argc, char** argv)
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// Open a video file or an image file or a camera stream.
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VideoCapture cap;
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bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(parser.get<int>("device"));
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bool openSuccess = parser.has("input") ? cap.open(findFile(parser.get<String>("input"))) : cap.open(parser.get<int>("device"));
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if (!openSuccess){
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cout << "Could not open input file or camera device" << endl;
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return 0;
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@ -324,7 +329,7 @@ int main(int argc, char** argv)
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classIds.clear();
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confidences.clear();
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boxes.clear();
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
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drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
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@ -354,7 +359,7 @@ int main(int argc, char** argv)
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CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
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// Threading is disabled, run synchronously
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Mat frame, blob;
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while (waitKey(100) < 0) {
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while (waitKey(1) < 0) {
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cap >> frame;
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if (frame.empty()) {
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waitKey();
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@ -369,7 +374,7 @@ int main(int argc, char** argv)
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confidences.clear();
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boxes.clear();
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
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drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
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@ -379,7 +384,7 @@ int main(int argc, char** argv)
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int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
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double freq = getTickFrequency() / 1000;
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double t = net.getPerfProfile(layersTimes) / freq;
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string label = format("Inference time: %.2f ms", t);
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string label = format("FPS: %.2f", 1000/t);
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putText(frame, label, Point(0, size), Scalar(0, 255, 0), sans, size, weight);
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imshow(kWinName, frame);
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}
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@ -414,15 +419,6 @@ void preprocess(const Mat& frame, Net& net, Size inpSize)
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// Set the blob as the network input
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net.setInput(inp);
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// Check if the model is Faster-RCNN or R-FCN
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if (net.getLayer(0)->outputNameToIndex("im_info") != -1)
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{
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// Resize the frame and prepare imInfo
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resize(frame, frame, size);
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Mat imInfo = (Mat_<float>(1, 3) << size.height, size.width, 1.6f);
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net.setInput(imInfo, "im_info");
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}
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}
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void yoloPostProcessing(
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@ -432,7 +428,7 @@ void yoloPostProcessing(
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vector<Rect2d>& keep_boxes,
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float conf_threshold,
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float iou_threshold,
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const string& yolo_name)
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const string& postprocessing)
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{
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// Retrieve
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vector<int> classIds;
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@ -441,12 +437,12 @@ void yoloPostProcessing(
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vector<Mat> outs_copy = outs;
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if (yolo_name == "yolov8")
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if (postprocessing == "yolov8")
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{
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transposeND(outs_copy[0], {0, 2, 1}, outs_copy[0]);
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}
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if (yolo_name == "yolonas")
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if (postprocessing == "yolonas")
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{
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// outs contains 2 elements of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
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Mat concat_out;
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@ -467,16 +463,16 @@ void yoloPostProcessing(
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for (int i = 0; i < preds.rows; ++i)
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{
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// filter out non-object
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float obj_conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? 1.0f : preds.at<float>(i, 4);
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float obj_conf = (postprocessing == "yolov8" || postprocessing == "yolonas") ? 1.0f : preds.at<float>(i, 4);
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if (obj_conf < conf_threshold)
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continue;
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Mat scores = preds.row(i).colRange((yolo_name == "yolov8" || yolo_name == "yolonas") ? 4 : 5, preds.cols);
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Mat scores = preds.row(i).colRange((postprocessing == "yolov8" || postprocessing == "yolonas") ? 4 : 5, preds.cols);
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double conf;
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Point maxLoc;
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minMaxLoc(scores, 0, &conf, 0, &maxLoc);
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conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? conf : conf * obj_conf;
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conf = (postprocessing == "yolov8" || postprocessing == "yolonas") ? conf : conf * obj_conf;
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if (conf < conf_threshold)
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continue;
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@ -488,7 +484,7 @@ void yoloPostProcessing(
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double h = det[3];
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// [x1, y1, x2, y2]
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if (yolo_name == "yolonas") {
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if (postprocessing == "yolonas") {
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boxes.push_back(Rect2d(cx, cy, w, h));
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} else {
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boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
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@ -511,12 +507,10 @@ void yoloPostProcessing(
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}
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}
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void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name)
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void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing)
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{
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static vector<int> outLayers = net.getUnconnectedOutLayers();
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static string outLayerType = net.getLayer(outLayers[0])->type;
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if (outLayerType == "DetectionOutput")
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if (postprocessing == "ssd")
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{
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// Network produces output blob with a shape 1x1xNx7 where N is a number of
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// detections and an every detection is a vector of values
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@ -552,7 +546,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
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}
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}
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}
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else if (outLayerType == "Region")
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else if (postprocessing == "darknet")
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{
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for (size_t i = 0; i < outs.size(); ++i)
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{
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@ -582,7 +576,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
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}
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}
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}
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else if (outLayerType == "Identity")
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else if (postprocessing == "yolov8" || postprocessing == "yolov5")
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{
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//![forward_buffers]
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vector<int> keep_classIds;
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@ -591,7 +585,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
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//![forward_buffers]
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//![postprocess]
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yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, yolo_name);
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yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, postprocessing);
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//![postprocess]
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for (size_t i = 0; i < keep_classIds.size(); ++i)
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@ -614,12 +608,13 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
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}
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else
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{
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CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
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cout<< ("Unknown postprocessing method: " + postprocessing)<<endl;
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exit(-1);
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}
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// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for other backends we need NMS in sample
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// or NMS is required if the number of outputs > 1
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if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
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if (outLayers.size() > 1 || (postprocessing == "darknet" && backend != DNN_BACKEND_OPENCV))
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{
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map<int, vector<size_t> > class2indices;
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for (size_t i = 0; i < classIds.size(); i++)
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@ -37,9 +37,6 @@ parser.add_argument('--out_tf_graph', default='graph.pbtxt',
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help='For models from TensorFlow Object Detection API, you may '
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'pass a .config file which was used for training through --config '
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'argument. This way an additional .pbtxt file with TensorFlow graph will be created.')
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parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'darknet', 'dldt', 'onnx'],
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help='Optional name of an origin framework of the model. '
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'Detect it automatically if it does not set.')
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parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
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parser.add_argument('--nms', type=float, default=0.4, help='Non-maximum suppression threshold')
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parser.add_argument('--backend', default="default", type=str, choices=backends,
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@ -76,8 +73,9 @@ if args.alias is None or hasattr(args, 'help'):
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args.model = findModel(args.model, args.sha1)
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if args.config is not None:
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args.config = findFile(args.config)
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args.labels = findFile(args.labels)
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args.config = findModel(args.config, args.config_sha1)
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if args.labels is not None:
|
||||
args.labels = findFile(args.labels)
|
||||
|
||||
# If config specified, try to load it as TensorFlow Object Detection API's pipeline.
|
||||
config = readTextMessage(args.config)
|
||||
@ -100,7 +98,10 @@ if args.labels:
|
||||
labels = f.read().rstrip('\n').split('\n')
|
||||
|
||||
# Load a network
|
||||
net = cv.dnn.readNet(args.model, args.config, args.framework)
|
||||
engine = cv.dnn.ENGINE_AUTO
|
||||
if args.backend != "default" or args.target != "cpu":
|
||||
engine = cv.dnn.ENGINE_CLASSIC
|
||||
net = cv.dnn.readNet(args.model, args.config, "", engine)
|
||||
net.setPreferableBackend(get_backend_id(args.backend))
|
||||
net.setPreferableTarget(get_target_id(args.target))
|
||||
outNames = net.getUnconnectedOutLayersNames()
|
||||
@ -126,14 +127,10 @@ def postprocess(frame, outs):
|
||||
frameHeight = frame.shape[0]
|
||||
frameWidth = frame.shape[1]
|
||||
|
||||
layerNames = net.getLayerNames()
|
||||
lastLayerId = net.getLayerId(layerNames[-1])
|
||||
lastLayer = net.getLayer(lastLayerId)
|
||||
|
||||
classIds = []
|
||||
confidences = []
|
||||
boxes = []
|
||||
if lastLayer.type == 'DetectionOutput':
|
||||
if args.postprocessing == 'ssd':
|
||||
# Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||
# detections and an every detection is a vector of values
|
||||
# [batchId, classId, confidence, left, top, right, bottom]
|
||||
@ -157,21 +154,12 @@ def postprocess(frame, outs):
|
||||
classIds.append(int(detection[1]) - 1) # Skip background label
|
||||
confidences.append(float(confidence))
|
||||
boxes.append([left, top, width, height])
|
||||
elif lastLayer.type == 'Region' or args.postprocessing == 'yolov8':
|
||||
# Network produces output blob with a shape NxC where N is a number of
|
||||
# detected objects and C is a number of classes + 4 where the first 4
|
||||
# numbers are [center_x, center_y, width, height]
|
||||
if args.postprocessing == 'yolov8':
|
||||
box_scale_w = frameWidth / args.width
|
||||
box_scale_h = frameHeight / args.height
|
||||
else:
|
||||
box_scale_w = frameWidth
|
||||
box_scale_h = frameHeight
|
||||
|
||||
elif args.postprocessing == 'darknet':
|
||||
box_scale_w = frameWidth
|
||||
box_scale_h = frameHeight
|
||||
|
||||
for out in outs:
|
||||
if args.postprocessing == 'yolov8':
|
||||
out = out[0].transpose(1, 0)
|
||||
|
||||
for detection in out:
|
||||
scores = detection[4:]
|
||||
if args.background_label_id >= 0:
|
||||
@ -188,13 +176,47 @@ def postprocess(frame, outs):
|
||||
classIds.append(classId)
|
||||
confidences.append(float(confidence))
|
||||
boxes.append([left, top, width, height])
|
||||
|
||||
elif args.postprocessing == 'yolov8' or args.postprocessing == 'yolov5':
|
||||
# Network produces output blob with a shape NxC where N is a number of
|
||||
# detected objects and C is a number of classes + 4 where the first 4
|
||||
# numbers are [center_x, center_y, width, height]
|
||||
box_scale_w = frameWidth / args.width
|
||||
box_scale_h = frameHeight / args.height
|
||||
|
||||
for out in outs:
|
||||
if args.postprocessing == 'yolov8':
|
||||
out = out[0].transpose(1, 0)
|
||||
else: # YOLOv5, no transposition needed
|
||||
out = out[0]
|
||||
|
||||
for detection in out:
|
||||
if args.postprocessing == 'yolov8':
|
||||
scores = detection[4:]
|
||||
obj_conf = 1
|
||||
else:
|
||||
scores = detection[5:]
|
||||
obj_conf = detection[4]
|
||||
|
||||
classId = np.argmax(scores)
|
||||
confidence = scores[classId]*obj_conf
|
||||
if confidence > confThreshold:
|
||||
center_x = int(detection[0] * box_scale_w)
|
||||
center_y = int(detection[1] * box_scale_h)
|
||||
width = int(detection[2] * box_scale_w)
|
||||
height = int(detection[3] * box_scale_h)
|
||||
left = int(center_x - width / 2)
|
||||
top = int(center_y - height / 2)
|
||||
classIds.append(classId)
|
||||
confidences.append(float(confidence))
|
||||
boxes.append([left, top, width, height])
|
||||
else:
|
||||
print('Unknown output layer type: ' + lastLayer.type)
|
||||
print('Unknown postprocessing method: ' + args.postprocessing)
|
||||
exit()
|
||||
|
||||
# NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
|
||||
# or NMS is required if number of outputs > 1
|
||||
if len(outNames) > 1 or (lastLayer.type == 'Region' or args.postprocessing == 'yolov8') and args.backend != cv.dnn.DNN_BACKEND_OPENCV:
|
||||
if len(outNames) > 1 or (args.postprocessing == 'darknet' or args.postprocessing == 'yolov8' or args.postprocessing == 'yolov5') and args.backend != cv.dnn.DNN_BACKEND_OPENCV:
|
||||
indices = []
|
||||
classIds = np.array(classIds)
|
||||
boxes = np.array(boxes)
|
||||
@ -308,14 +330,11 @@ def processingThreadBody():
|
||||
# Create a 4D blob from a frame.
|
||||
inpWidth = args.width if args.width else frameWidth
|
||||
inpHeight = args.height if args.height else frameHeight
|
||||
blob = cv.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
|
||||
blob = cv.dnn.blobFromImage(frame, scalefactor=args.scale, mean=args.mean, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
|
||||
processedFramesQueue.put(frame)
|
||||
|
||||
# Run a model
|
||||
net.setInput(blob, scalefactor=args.scale, mean=args.mean)
|
||||
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
|
||||
frame = cv.resize(frame, (inpWidth, inpHeight))
|
||||
net.setInput(np.array([[inpHeight, inpWidth, 1.6]], dtype=np.float32), 'im_info')
|
||||
net.setInput(blob)
|
||||
|
||||
if asyncN:
|
||||
futureOutputs.append(net.forwardAsync())
|
||||
@ -385,9 +404,9 @@ else:
|
||||
|
||||
inpWidth = args.width if args.width else frameWidth
|
||||
inpHeight = args.height if args.height else frameHeight
|
||||
blob = cv.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
|
||||
blob = cv.dnn.blobFromImage(frame, scalefactor=args.scale, mean=args.mean, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
|
||||
|
||||
net.setInput(blob, scalefactor=args.scale, mean=args.mean)
|
||||
net.setInput(blob)
|
||||
outs = net.forward(outNames)
|
||||
|
||||
boxes, classIds, confidences, indices = postprocess(frame, outs)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user