labeling

问题 I've annotated some images using the coco-annotator. I'd like to combine these annotations with the existing one ("person_keypoints_train2017.json" and "person_keypoints_val2017.json"). Has anyone ever managed to do so? If yes, how? (preferrably using python) 回答1: json vlaues can be accessed using the json module try reading both the files and hence decide what values to combine: import json from collections import OrderedDict filename1 = "person_keypoints_train2017.json" with open(filename1)

问题 Hi I have to find out how many objects I have in an image. http://en.wikipedia.org/wiki/Connected-component_labeling I need help with storing the equivalence between neighbours and also the second pass. This pass gives me 173 or so objects and is the first pass. I would like to store equivalences (when they occur) and then in the second pass to be able to just replace the corresponding equivalences with the lowest equivalent value. 回答1: The equivalence table can be implemented using a HashMap

问题 I'm working with a gambling website that historically has labeled itself using the ICRA Vocabulary expressed with RDF and PICS labeling. Its my understanding that these labels were interpreted by systems such as Internet Explorer 6 Content Advisor to block website access. Unfortunately, the ICRA labeling scheme seems to be defunct as evidenced by the ICRA Homepage. the ICRA labeling engine has been discontinued. While all current labels will continue to work with Internet content filters, the

问题 We have a script that labels files recursively. Here is the command that executes the label command. cleartool ls -recurse -vob_only -visible -short | \ xargs cleartool mklabel -replace -follow VPCEUM_9.0.0.99 > label.txt 2>&1 When is encounter files with spaces the name such as /directory/d1/my file is here.doc , the command errors off. How do I get ClearCase/unix to accept the space? 回答1: You have tow approaches with cleartool mklabel: you can use mklabel directly, with the -recurse option.

问题 I'm looking for a way that, given an input image and a neural network, it will output a labeled class for each pixel in the image (sky, grass, mountain, person, car etc). I've set up Caffe (the future-branch) and successfully run the FCN-32s Fully Convolutional Semantic Segmentation on PASCAL-Context model. However, I'm unable to produce clear labeled images with it. Images that visualizes my problem: Input image ground truth And my result: This might be some resolution issue. Any idea of

问题 I am currently writing a solver for a floor planning problem in Prolog and have some issues with the labeling part. The current problem is my constraints are posted but when I launch the labeling, it takes forever to find a solution. I would like to bring in some heuristics. My question is, how do I manually label my variables ? I am afraid that after defining a clpfd variable like this : X in Xinf..Xsup and constraining it, If I do something like : fd_sup(X, Xmax), X = Xmax, ... in my custom

问题 I am using opengl (fixed-function pipeline) and I'm drawing potentially hundreds of thousands of points, and labeling each with a text label. This question is about whether I'm doing this in a reasonable way, and what I can expect in terms of speed. The text labels are drawn by creating a texture coordinate rectangle for each character, and texturing the rectangles using a small font bitmap (each character is about 5x13 pixels in the texture). In a test file, I have about 158,000 points,

问题 I'm currently trying to display a circle graph in R with the ability to place the labels of each node right next to, but outside of, the node itself. I looked at a few answers, and tried one which suggested that I specify locations given in radians for each node itself via: radian.rescale <- function(x, start=0, direction=1) { c.rotate <- function(x) (x + start) %% (2 * pi) * direction c.rotate(scales::rescale(x, c(0, 2 * pi), range(x))) } lab.locs <- radian.rescale(x=1:n, direction=-1, start

问题 I spent more than a week trying to have custom axis label for Core Plot, without success. I read many tutorials and examples on SO, and tried to replicate it in my project, but no success. Please help. This is the code I have: // Create an CPXYGraph and host it inside the view CPTTheme *theme = [CPTTheme themeNamed:kCPTPlainWhiteTheme]; CPTXYGraph *graph = (CPTXYGraph *)[theme newGraph]; hostView.hostedGraph = graph; graph.paddingTop = 10.0; graph.paddingLeft = 10.0; graph.paddingRight = 10.0

问题 I am labeling images using the watershed algorithm in OpenCV (similar to this tutorial: https://docs.opencv.org/3.4/d3/db4/tutorial_py_watershed.html) such that at the end I obtain an array of labels where each region has an integer value corresponding to its label. Now, I want to obtain the coordinates of the bounding boxes and areas of each region. I know this is easily done with skimage.measure.regionprops() but for considerations of speed of execution I would like to achieve this without