I have the need to read this low quality ECC code using halcon 20.
I have tried all possible parameters but cannot find any combination that is able to read this code.
What seems strange to me is that if I have a look at the example codes, there are images that are even worse than this and those are read without problems.
here is my current code that is NOT able to read this:
create_data_code_2d_model ('Data Matrix ECC 200', ['default_parameters','small_modules_robustness','module_size_min'], ['maximum_recognition','high','1'], DataCodeHandleMaximum)
set_data_code_2d_param (DataCodeHandleMaximum,['symbol_cols','symbol_rows','slant_max','timeout'],[26,12,0.52,10000])
find_data_code_2d (ImageReduced, SymbolXLDs, DataCodeHandleMaximum, ['stop_after_result_num'], [200], ResultHandles, DecodedDataStrings)
any help appreciated
Image seems to be a bit out of focus, so it needs a bit of sharpening filter:
create_data_code_2d_model ('Data Matrix ECC 200', 'default_parameters', 'maximum_recognition', DataCodeHandle)
emphasize (Image, ImageEmphasize, 7, 7, 1)
find_data_code_2d (ImageEmphasize, SymbolXLDs, DataCodeHandle, [], [], ResultHandles, DecodedDataStrings)
Related
I am currently writing an (simple) analytisis code to sum time connected powerreadings. With the data being assumingly raw (e.g. disturbances from the measuring device have not been calculated out) I have to account for disturbances by calculation the mean of the first one thousand samples. The calculation of the mean itself is not a problem. I only am unsure of how to generate the appropriate DataSet.
For now it looks about like this:
DataSet<Tupel2<long,double>>Gyrotron_1=ECRH.includeFields('11000000000'); // obviously the line to declare the first gyrotron, continues for the next ten lines, assuming separattion of not occupied space
DataSet<Tupel2<long,double>>Gyrotron_2=ECRH.includeFields('10100000000');
DataSet<Tupel2<long,double>>Gyrotron_3=ECRH.includeFields('10010000000');
DataSet<Tupel2<long,double>>Gyrotron_4=ECRH.includeFields('10001000000');
DataSet<Tupel2<long,double>>Gyrotron_5=ECRH.includeFields('10000100000');
DataSet<Tupel2<long,double>>Gyrotron_6=ECRH.includeFields('10000010000');
DataSet<Tupel2<long,double>>Gyrotron_7=ECRH.includeFields('10000001000');
DataSet<Tupel2<long,double>>Gyrotron_8=ECRH.includeFields('10000000100');
DataSet<Tupel2<long,double>>Gyrotron_9=ECRH.includeFields('10000000010');
DataSet<Tupel2<long,double>>Gyrotron_10=ECRH.includeFields('10000000001');
for (int=1,i<=10;i++) {
DataSet<double> offset=Gyroton_'+i+'.groupBy(1).first(1000).sum()/1000;
}
It's the part in the for-loop I'm unsure of. Does anybody know if it is possible to append values to DataSets and if so how?
In case of doubt, I could always put the values into an array but I do not know if that is the wise thing to do.
This code will not work for many reasons. I'd recommend looking into the fundamentals of Java and the basic data structures and also in Flink.
It's really hard to understand what you actually try to achieve but this is the closest that I came up with
String[] codes = { "11000000000", ..., "10000000001" };
DataSet<Tuple2<Long, Double>> result = env.fromElements();
for (final String code : codes) {
DataSet<Tuple2<Long, Double>> codeResult = ECRH.includeFields(code)
.groupBy(1)
.first(1000)
.sum(0)
.map(sum -> new Tuple2<>(sum.f0, sum.f1 / 1000d));
result = codeResult.union(result);
}
result.print();
But please take the time and understand the basics before delving deeper. I also recommend to use an IDE like IntelliJ that would point to at least 6 issues in your code.
I am learning machine learning using TensorFlow. I have been through a couple of tutorials but I still have a hard time trying to find what are the good ways of training a model. Recently I implemented a CNN model I found in the litterature. The model must take a crop of a certain size centered on a given pixel and predict the label of this pixel. It does that for each pixel of the image. I used:
classifier = tf.learn.Estimator(model_fn=cnn_model_fn, model_dir="./cnn")
with cnn_model_fn beeing a function I implemented.
For each training image, we take 3000 crops randomly, so I can't load all theses images and their crops to memory. The way I found is by loading one image at a time, extract the 3000 crops and then call classifier.fit() to train on the 3000 crops. Then loop for each image in my dataset.
for i in range(len(filenames)):
...
image = misc.imread(filenames[i])
labels = misc.imread(groundTruth[i]) #labels for each pixels
input_classifier = preprocess(image,...) #crops 3000 images in image and do other things
input_labels = preprocess_labels(labels, ...) #take the corresponding 3000 labels
classifier.fit(x = input_classifier,
y = input_labels,
batch_size = 30
steps = 100)
It worked fine for 100 images, but if I try on the whole dataset (2000 images), it always stops and give an error of ResourceExhausted.
...
[everything goes well]
...
iteration :227/2000
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
INFO:tensorflow:Create CheckpointSaverHook.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
Traceback (most recent call last):
File "train-cnn.py", line 78, in <module>
classifier.fit(x= input_classifier, y=input_labels,batch_size=30, steps=100)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 280, in new_func
...
...
...
tensorflow.python.framework.errors_impl.ResourceExhaustedError: cnn/graph.pbtxt.tmp32bcc6311c164c29b91177d17d05d669
I don't see why it gets OOM... I have suspicions that it is because of the way I call fit() in loop. After each fit(), a ckpt is saved and it must be restored right after to train on the next image. So is it a bad way to train a model?
running estimator.fit in a loop with smaller steps is not a good idea. I would put all input logic into an input_fn. then run estimator.fit only once with more steps.
An example of reading data from different files can be found here: tf.contrib.learn.read_batch_examples
Using SceneKit to build some large custom geometries, in order to keep array sizes as small as possible, I fed SCNGeometrySource with vectors with (x,y,z) as short (Int16) integers, as follows:
let vertexSource = SCNGeometrySource(data: dataContent!,
semantic: SCNGeometrySourceSemanticVertex, vectorCount: 4960,
floatComponents: false, componentsPerVector: 3, bytesPerComponent: 2,
dataOffset: 0, dataStride: 6)
.. however, this data was not acceptable, generating the error:
SceneKit: error, C3DMeshSourceGetValueAtIndexAsVector3 - Type not supported
Question: What forms of vector components does SceneKit support?
After writing this question I'm going to try trial & error to see what happens with triples and quads of Float32, Int32, etc. I know Float64 does work but I don't need better than ±2^15 precision, so why waste memory? Perhaps someone can provide a definitive answer .. Apple's documentation appears to not do so.
does anyone use arima.rob() function described by Eric Zivot and Jiahui Wang in { Modelling Financial Time Series with S-PLUS } ?
I have a question about it:
I used a dataset of network traffic flows that has anomaly, and I tried to predict the last part of dataset by robust ARIMA method (Arima.rob() function) .I compare this model with arima.mle of S-PLUS. But Unexpectedly, arima.rob’s prediction did not better than that.
I’m not sure my codes are correct and may be the reason of fault is my codes.
Please, help me if I used Arima.rob inappropriately?
tmp.rr<-arima.rob((tmh75)~1,p=2,d=1,q=2,freq=24,maxiter=4,max.fcal=80000)
tmp.for<-predict(tmp.rr,n.predict=10,newdata=df1,se=T)
plot(tmp.for,tmh75)
summary(tmp.for)
my code for classic arima:
`model <- list(list(order=c(2,1,2)),list(order=c(3,1,2),period=24))
fith <- arima.mle(tmh75-mean(tmh75),model=model)
foreh <- arima.forecast(tmh75,n=25,model=fith$model)
tsplot(tmh75,foreh$mean,foreh$mean+foreh$std.err,foreh$mean-foreh$std.err)
`
I am using Maya to do some procedural work, and I have a lot of textures that I need to load into Maya, and they all have transparencies (alpha channels). I would very much like to be able to automate this process. Using PyMEL, I can create my textures and hook them up to a shader, but the alpha doesn't set properly by default. There is an attribute in the psdFileTex node called "Alpha to Use", and it must be set to "Transparency" in order for my alpha channel to work. My question is this - how do I use PyMEL scripting to set the "Alpha to Use" attribute properly?
Here is the code I am using to set up my textures:
import pymel.core as pm
pm.shadingNode('lambert', asShader=True, name='myShader1')
pm.sets(renderable=True, noSurfaceShader=True, empty=True, name='myShader1SG')
pm.connectAttr('myShader1.outColor', 'myShader1SG.surfaceShader', f=True)
pm.shadingNode('psdFileTex', asTexture=True, name='myShader1PSD')
pm.connectAttr('myShader1PSD.outColor', 'myShader1.color')
pm.connectAttr('myShader1PSD.outTransparency', 'myShader1.transparency')
pm.setAttr('myShader1ColorPSD.fileTextureName', '<pathway>/myShader1_texture.psd', type='string')
If anyone can help me, I would really appreciate it.
Thanks
With any node, you can use listAttr() to get the available editable attributes. Run listAttr('myShaderPSD'), note in it's output, there will be two attributes called 'alpha' and 'alphaList'. Alpha, will return you the current selected alpha channel. AlphaList will return you however many alpha channels you have in your psd.
Example
pm.PyNode('myShader1PSD').alphaList.get()
# Result: [u'Alpha 1', u'Alpha 2'] #
If you know you'll only ever be using just the one alpha, or the first alpha channel, you can simply do this.
psdShader = pm.PyNode('myShader1PSD')
alphaList = psdShader.alphaList.get()
if (len(alphaList) > 0):
psdShader.alpha.set(alphaList[0])
else:
// No alpha channel
pass
Remember that lists start iterating from 0, so our first alpha channel will be located at position 0.
Additionally and unrelated, while you're still using derivative commands of the maya.core converted for Pymel, there's still some commands you can use to help make your code read nicer.
pm.setAttr('myShader1ColorPSD.fileTextureName', '<pathway>/myShader1_texture.psd', type='string')
We can convert this to pymel like so:
pm.PyNode('myShader1ColorPSD').fileTextureName.set('<pathway>/myShader1_texture.psd')
And:
pm.connectAttr('myShader1PSD.outColor', 'myShader1.color')
Can be converted to:
pm.connect('myShader1PSD.outColor', 'myShader1.color')
While they may only be small changes, it reads just the little bit nicer, and it's native PyMel.
Anyway, I hope I have helped you!