object-detection

问题 I've trained a model with a custom dataset (Garfield images) with Tensorflow Object Detection API (ssd_mobilenet_v1 model) and referring it in the android sample application available on Tensorflow repository. The application can only detected the images in distances less or equal 20cm approximately. Do you have any clue about I can improve the model to perform recognitions in longer distances (about 30cm or more) ? I don't know with this limitation is related with input size I'm using

问题 The code which is available at http://www.cs.berkeley.edu/~rbg/latent/voc-release5.tgz is widely used in object detection. There is a imgdetect function which returns ds , bs and trees . It seems ds contains detection boxes and bs contains which of the filters used for detection responded in the image, while trees contains some information about the part scores. I was not able to find out how to get the part scores using trees and bs . Basically, given a detection box, I would like to find

问题 I have an application where I want to track 2 objects at a time that are rather small in the picture. This application should be running on Android and iPhone, so the algorithm should be efficient. For my customer it is perfectly fine if we deliver some patterns along with the software that are attached to the objects to be tracked to have a well-recognizable target. This means that I can make up a pattern on my own. As I am not that much into image processing yet, I don't know which objects

问题 I'm trying to detect the white dots in the following image using OpenCV and Python. I tried using the function cv2.HoughCircles but without any success. Do I need to use a different method? This is my code: import cv2, cv import numpy as np import sys if len(sys.argv)>1: filename = sys.argv[1] else: filename = 'p.png' img_gray = cv2.imread(filename,cv2.CV_LOAD_IMAGE_GRAYSCALE) if img_gray==None: print "cannot open ",filename else: img = cv2.GaussianBlur(img_gray, (0,0), 2) cimg = cv2.cvtColor

问题 I'm training a CNN with this [.config file][1]: rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } As you can see there is a rms_prop as optimizer. What if I would like to use Adam? How am I supposed to edit this file? 回答1: if I'm right, you're trying to use the object_detection model with a pre-trained network offered by Tensorflow, am I

问题 I'm running the typical code found in the github repository tensorflow/object_deteciton: https://github.com/tensorflow/models/tree/master/research/object_detection Specifically the 'object_detection_tutorial.ipynb' file. The main loop is this section here: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Each box represents a part of

问题 I'm trying to train the SVHN (street view house numbers) dataset for object-detection in tensorflow (to do some basic OCR on numbers). So far I have successfully followed the pet-training example from the object-detection-tensorflow guides. When I train the network based on the sample faster_rcnn_resnet101.config, after a few dozen steps I get: INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, Reduction axis 1 is empty in

问题 I'm using Tensorflow's Object Detection API, but get the following error when training: InvalidArgumentError (see above for traceback): assertion failed: [maximum box coordinate value is larger than 1.01: ] [1.47] I get the error when I use any of the following: faster_rcnn_inception_resnet_v2_atrous_coco rfcn_resnet101_coco But NOT when I use: ssd_inception_v2_coco ssd_mobilenet_v1_coco My training images are a mixture of 300x300 and 450x450 pixels. I don't believe any of my bounding boxes

问题 I would like to train a Mobilenet SSD Model on a custom dataset. I have looked into the workflow of retraining a model and noticed the image_resizer{} block in the config file: https://github.com/tensorflow/models/blob/d6d0868209833e014074d6cb4f32558e7acf2a6d/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config#L43 Does the aspect ratio here have to be 1:1 like 300x300 or can I specify a custom ratio? All my dataset images are 960x256 - so could I just input this size for

问题 I have to write a program that detect 3 types of road signs (speed limit, no parking and warnings). I know how to detect a circle using HoughCircles but I have several images and the parameters for HoughCircles are different for each image. There's a general way to detect circles without changing parameters for each image? Moreover I need to detect triangle (warning signs) so I'm searching for a general shape detector. Have you any suggestions/code that can help me in this task? Finally for