object-detection

问题 I'm attempting to create my own dataset for use with YOLO (You Only Look Once). Initially, I started with a large geotiff file that had pictures of landscape and animals. I was able to write a script to extract the images of the animals into separate files. I'm now at a point where I'd like to use those animal images as part of a YOLO dataset. However, all of the examples I've seen online utilize annotation files, which denote the location of an object-to-be-detected within a larger image. In

问题 I have the following code where the user can press p to pause the video, draw a bounding box around the object to be tracked, and then press Enter (carriage return) to track that object in the video feed: import cv2 import sys major_ver, minor_ver, subminor_ver = cv2.__version__.split('.') if __name__ == '__main__' : # Set up tracker. tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT'] tracker_type = tracker_types[1] if int(minor_ver) < 3: tracker = cv2

问题 We're struggling to get our Yolov3 working for a 2 class detection problem (the size of the objects of both classes are varying and similar, generally small, and the size itself does not help differentiating the object type). We think that the training is not working due to some problem with the anchor boxes, since we can clearly see that depending on the assigned anchor values the yolo_output_0, yolo_output_1 or yolo_output_2 fail to return a loss value different to 0 (for xy, hw and class

问题 This is probably going to be a stupid question but I am new to machine learning and Tensorflow. I am trying to run object detection API on Raspberry Pi using Tensorflow Lite . I am trying to modify my code with the help of this example https://github.com/freedomtan/tensorflow/blob/deeplab_tflite_python/tensorflow/contrib/lite/examples/python/object_detection.py This piece of code will detect object from a image. But instead of a image I want to detect object on real time through Pi camera. I

问题 I'm testing some function in Hololens. Want to know whether it is possible to use any of object detection/text recognition in Hololens? 回答1: Here's a great blog post by one of my fellow Microsoft MVPs... :) "Labeling Toy Aircraft in 3D space using an ONNX model and Windows ML on a HoloLens" Ping me if you hit any snags -- awesome stuff! http://dotnetbyexample.blogspot.com/2019/01/labeling-toy-aircraft-in-3d-space-using.html 回答2: Hololens 1 doesn't natively support object detection, you'll

问题 I'm testing some function in Hololens. Want to know whether it is possible to use any of object detection/text recognition in Hololens? 回答1: Here's a great blog post by one of my fellow Microsoft MVPs... :) "Labeling Toy Aircraft in 3D space using an ONNX model and Windows ML on a HoloLens" Ping me if you hit any snags -- awesome stuff! http://dotnetbyexample.blogspot.com/2019/01/labeling-toy-aircraft-in-3d-space-using.html 回答2: Hololens 1 doesn't natively support object detection, you'll

问题 I'm testing some function in Hololens. Want to know whether it is possible to use any of object detection/text recognition in Hololens? 回答1: Here's a great blog post by one of my fellow Microsoft MVPs... :) "Labeling Toy Aircraft in 3D space using an ONNX model and Windows ML on a HoloLens" Ping me if you hit any snags -- awesome stuff! http://dotnetbyexample.blogspot.com/2019/01/labeling-toy-aircraft-in-3d-space-using.html 回答2: Hololens 1 doesn't natively support object detection, you'll

问题 The script used to do detection. Weight file was yolov4 coco pre-trained model and that can be found over here.(https://drive.google.com/file/d/1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT/view) import time from absl import app, flags, logging from absl.flags import FLAGS import core.utils as utils from core.yolov4 import YOLOv4, YOLOv3, YOLOv3_tiny, decode from PIL import Image from core.config import cfg import cv2 import numpy as np import tensorflow as tf flags.DEFINE_string('framework', 'tf', '(tf,

问题 For my thesis i am trying to evaluate the impact of different parameters on my active learning object detector with tensorflow (v 1.14). Therefore i am using the faster_rcnn_inception_v2_coco standard config from the model zoo and a fixed random.seed(1). To make sure i have a working baseline experiment i tried to run the object detector two times with the same dataset, learning time, poolingsize and so forth. Anyhow the two plotted graphs after 20 active learning cycles are quite different

问题 The coordinate of the system is boundary coordinates (x_min, y_min, x_max, y_max). and I want to find the intersection of two boxes set1 and set2 set1 -> (n1,4) set2 -> (n2,4) example set_1-> tensor([[0.2400, 0.2342, 0.8500, 0.8048], [0.1420, 0.5075, 0.2440, 0.5856], [0.0000, 0.5075, 0.1420, 0.5976]], device='cuda:0') set_2-> tensor([[-0.0368, -0.0368, 0.0632, 0.0632], [-0.0576, -0.0576, 0.0839, 0.0839], [-0.0576, -0.0222, 0.0839, 0.0485], ..., [ 0.0000, 0.0000, 1.0000, 1.0000], [ 0.0000, 0