When I try to encode a video the encoder crashes after finishing first GOP.
This is the configuration I'm using:
MaxCUWidth : 16 # Maximum coding unit width in pixel
MaxCUHeight : 16 # Maximum coding unit height in pixel
MaxPartitionDepth : 2 # Maximum coding unit depth
QuadtreeTULog2MaxSize : 3 # Log2 of maximum transform size for
# quadtree-based TU coding (2...5) = MaxPartitionDepth + 2 - 1
QuadtreeTULog2MinSize : 2 # Log2 of minimum transform size for
# quadtree-based TU coding (2...5)
QuadtreeTUMaxDepthInter : 1
QuadtreeTUMaxDepthIntra : 1
#======== Coding Structure =============
IntraPeriod : 8 # Period of I-Frame ( -1 = only first)
DecodingRefreshType : 1 # Random Accesss 0:none, 1:CDR, 2:IDR
GOPSize : 4 # GOP Size (number of B slice = GOPSize-1)
# Type POC QPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 temporal_id #ref_pics_active #ref_pics reference pictures predict deltaRPS #ref_idcs reference idcs
Frame1: P 4 1 0.5 0 0 0 1 1 -4 0
Frame2: B 2 2 0.5 1 0 1 1 2 -2 2 1 2 2 1 1
Frame3: B 1 3 0.5 2 0 2 1 3 -1 1 3 1 1 3 1 1 1
Frame4: B 3 3 0.5 2 0 2 1 2 -1 1 1 -2 4 0 1 1 0
This also happens with CU=16x16 with depth=1
Note: I encoded CU=64x64 with depth=4 with the same GOP configuration and every thing went fine.
This is most probably due to the fact that you have compiled the binary for a 32-bit system?
Please rebuild it for a 64-bit system and the problem will go away.
Related
for example, stitch
first image
1 1 1
1 1 1
1 1 1
second image
2 2 2 2
2 2 2 2
2 2 2 2
and What I want
0 0 0 2 2 2 2
1 1 1 2 2 2 2
1 1 1 2 2 2 2
1 1 1 0 0 0 0
or
1 1 1 0 0 0 0
1 1 1 2 2 2 2
1 1 1 2 2 2 2
0 0 0 2 2 2 2
In python, that is easy to make like..
temp_panorama = np.zeros((1's height+abs(2's upper part length), 1's width+2's width))
temp_panorama[(2's upper part length) : 1's height, 0 : 1's width] = img1[:]
temp_panorama[0 : 2's height, 1's width +1 :] = img2[:, :]
but how can I implement the same function in C++'s opencv?
use subimages:
// ROI where first image will be placed
cv::Rect firstROI = cv::Rect(x1,y2, first.cols, first.height);
cv::Rect secondROI = cv::Rect(x2,y2, second.cols, second.height);
// create an image big enought to hold the result
cv::Mat canvas = cv::Mat::zeros(cv::Size(std::max(x1+first.cols, x2+second.cols), std::max(y1+first.rows, y2+second.rows)), first.type());
// use subimages:
first.copyTo(canvas(firstROI));
second.copyTo(canvas(secondROI));
in your example:
x1 = 0,
y1 = 1,
x2 = 3,
y2 = 0
first.cols == 3
first.rows == 3
second.cols == 4
second.rows == 3
I am using below code to predict anomaly detection. It is a binary classification so the confusion matrix should be 2x2 instead it is 3x3. There are extra zeros appended in T-shape. Similar thing happened using OneClassSVM few weeks back as well but I thought I was doing something wrong. Could you please help me fix this?
import numpy as np
import pandas as pd
import os
from sklearn.ensemble import IsolationForest
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn import metrics
from sklearn.metrics import roc_auc_score
data = pd.read_csv('opensky_train.csv')
#to make sure that normal data contains no anomaly
sortedData = data.sort_values(by=['class'])
target = pd.DataFrame(sortedData['class'])
Y = target.replace(['surveill', 'other'], [1,0])
X = sortedData.drop(['class'], axis = 1)
x_normal = X.iloc[:200,:]
y_normal = Y.iloc[:200,:]
x_anomaly = X.iloc[200:,:]
y_anomaly = Y.iloc[200:,:]
Edited:
column_values = y_anomaly.values.ravel()
unique_values = pd.unique(column_values)
print(unique_values)
Output : [0 1]
clf = IsolationForest(random_state=0).fit(x_normal)
pred = clf.predict(x_anomaly)
print(pred)
Output : [ 1 1 1 1 1 1 -1 1 -1 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 -1 1 1 1 1 1 1 -1 1 1 -1 1 1 -1 1 1 -1 1 -1 1
-1 1 1 -1 -1 1 -1 -1 1 1 1 1 -1 1 1 -1 -1 1 1 1 1 1 1 1
-1 1 1 1 1 1 1 1 1 1 -1]
#printing the results
print(confusion_matrix(y_anomaly, pred))
print (classification_report(y_anomaly, pred))
Result:
Confusion Matrix :
[[ 0 0 0]
[ 7 0 60]
[12 0 28]]
precision recall f1-score support
-1 0.00 0.00 0.00 0
0 0.00 0.00 0.00 67
1 0.32 0.70 0.44 40
accuracy 0.26 107
macro avg 0.11 0.23 0.15 107
weighted avg 0.12 0.26 0.16 107
Inliers are labeled 1, while outliers are labeled -1
Source: scikit-learn Anomaly and Outlier detection.
Your example has transformed the classes to 0,1 - so the three possible options are -1,0,1
You need to change from
Y = target.replace(['surveill', 'other'], [1,0])
to
Y = target.replace(['surveill', 'other'], [1,-1])
As a very begginer in SPSS I would ask you for help with some transformation from table A into table B. I have to recode values of "brand" variable into columns and make 0-1 variables.
#table A#
nr brand
1 GREEN CARE PROFESSIONAL
1 GREEN CARE PROFESSIONAL
1 GREEN CARE PROFESSIONAL
2 HENKEL
3 HENKEL
3 HENKEL
3 HENKEL
3 VIZIR
4 BIEDRONKA
4 BOBINI
4 BOBINI
4 BOBINI
4 BOBINI
4 BOBINI
4 HENKEL
5 VIZIR
6 HENKEL
#table B#
nr GREEN HENKEL VIZIR BIEDR BOBINI
1 1 0 0 0 0
1 1 0 0 0 0
1 1 1 0 0 0
2 0 1 0 0 0
3 0 1 0 0 0
3 0 1 0 0 0
3 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 1 0 0 0
5 0 0 1 0 0
6 0 1 0 0 0
I can do it in this particular case in this simple way:
compute HENKEL=0.
...
do if BRAND='GREEN_CARE' .
compute GREEN_CARE=1.
else if ....
but the loop has to be usable with another variable and different number of values ect. I was trying to make it all day and gave up.
Do you have any idea to make it in a easy way?
Thanks!
The following syntax does the job on the sample data you provided.
First, let's recreate the sample data to demonstrate on:
Data list list/nr (f1) brand (a30).
begin data
1 "GREEN CARE PROFESSIONAL"
1 "GREEN CARE PROFESSIONAL"
1 "GREEN CARE PROFESSIONAL"
2 "HENKEL"
3 "HENKEL"
3 "HENKEL"
3 "HENKEL"
3 "VIZIR"
4 "BIEDRONKA"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "HENKEL"
5 "VIZIR"
6 "HENKEL"
end data.
dataset name originalDataset.
Now for the restructure.
sort cases by nr brand.
* creating an index to enumerate cases for each combination of `nr` and `brand`.
* This is necessary for the `casestovars` command to work later.
compute ind=1.
if $casenum>1 and lag(nr)=nr and lag(brand)=brand ind=lag(ind)+1.
exe.
* variable names can't have spaces in them, so changing the category names accordingly.
compute brand=replace(rtrim(brand)," ","_").
sort cases by nr ind brand.
compute exist=1.
casestovars /id=nr ind /index= brand/autofix=no.
I'm trying to implement a simple NN in Torch to learn more about it. I created a very simple dataset: binary numbers from 0 to 15 and my goal is to classify the numbers into two classes - class 1 are numbers 0-3 and 12-15, class 2 are the remaining ones. The following code is what i have now (i have removed the data loading routine only):
require 'torch'
require 'nn'
data = torch.Tensor( 16, 4 )
class = torch.Tensor( 16, 1 )
network = nn.Sequential()
network:add( nn.Linear( 4, 8 ) )
network:add( nn.ReLU() )
network:add( nn.Linear( 8, 2 ) )
network:add( nn.LogSoftMax() )
criterion = nn.ClassNLLCriterion()
for i = 1, 300 do
prediction = network:forward( data )
--print( "prediction: " .. tostring( prediction ) )
--print( "class: " .. tostring( class ) )
loss = criterion:forward( prediction, class )
network:zeroGradParameters()
grad = criterion:backward( prediction, class )
network:backward( data, grad )
network:updateParameters( 0.1 )
end
This is how the data and class Tensors look like:
0 0 0 0
0 0 0 1
0 0 1 0
0 0 1 1
0 1 0 0
0 1 0 1
0 1 1 0
0 1 1 1
1 0 0 0
1 0 0 1
1 0 1 0
1 0 1 1
1 1 0 0
1 1 0 1
1 1 1 0
1 1 1 1
[torch.DoubleTensor of size 16x4]
2
2
2
2
1
1
1
1
1
1
1
1
2
2
2
2
[torch.DoubleTensor of size 16x1]
Which is what I expect it to be. However when running this code, i get the following error on line loss = criterion:forward( prediction, class ):
torch/install/share/lua/5.1/nn/ClassNLLCriterion.lua:69: attempt to
perform arithmetic on a nil value
When i modify the training routine like this (processing a single data point at a time instead of all 16 in a batch) it works and the network successfully learns to recognize the two classes:
for k = 1, 300 do
for i = 1, 16 do
prediction = network:forward( data[i] )
--print( "prediction: " .. tostring( prediction ) )
--print( "class: " .. tostring( class ) )
loss = criterion:forward( prediction, class[i] )
network:zeroGradParameters()
grad = criterion:backward( prediction, class[i] )
network:backward( data[i], grad )
network:updateParameters( 0.1 )
end
end
I'm not sure what might be wrong with the "batch processing" i'm trying to do. A brief look at the ClassNLLCriterion didn't help, it seems i'm giving it the expected input (see below), but it still fails. The input it receives (prediction and class Tensors) looks like this:
-0.9008 -0.5213
-0.8591 -0.5508
-0.9107 -0.5146
-0.8002 -0.5965
-0.9244 -0.5055
-0.8581 -0.5516
-0.9174 -0.5101
-0.8040 -0.5934
-0.9509 -0.4884
-0.8409 -0.5644
-0.8922 -0.5272
-0.7737 -0.6186
-0.9422 -0.4939
-0.8405 -0.5648
-0.9012 -0.5210
-0.7820 -0.6116
[torch.DoubleTensor of size 16x2]
2
2
2
2
1
1
1
1
1
1
1
1
2
2
2
2
[torch.DoubleTensor of size 16x1]
Can someone help me out here? Thanks.
Experience has shown that nn.ClassNLLCriterion expects target to be a 1D tensor of size batch_size or a scalar. Your class is a 2D one (batch_size x 1) but class[i] is 1D, that's why your non-batch version works.
So, this will solve your problem:
class = class:view(-1)
Alternatively, you can replace
network:add( nn.LogSoftMax() )
criterion = nn.ClassNLLCriterion()
with the equivalent:
criterion = nn.CrossEntropyCriterion()
The interesting thing is that nn.CrossEntropyCriterion is also able to take a 2D tensor. Why is nn.ClassNLLCriterion not?
I want to parse a file in scala (probably using JavaTokerParsers?). Possibly without using too many vars :-)
The file is the input for a ray tracer.
It is a line based file structure.
Three types of lines exists: empty line, comment line and command line
The comment line starts with # (maybe has some whitespace before the #)
Command line starts with an identifier optionally followed by a number of parameters (float or filename).
How would I go about this. I would want to parser to be called like this
val scene = parseAll(sceneFile, file);
Sample file:
#Cornell Box
size 640 480
camera 0 0 1 0 0 -1 0 1 0 45
output scene6.png
maxdepth 5
maxverts 12
#planar face
vertex -1 +1 0
vertex -1 -1 0
vertex +1 -1 0
vertex +1 +1 0
#cube
vertex -1 +1 +1
vertex +1 +1 +1
vertex -1 -1 +1
vertex +1 -1 +1
vertex -1 +1 -1
vertex +1 +1 -1
vertex -1 -1 -1
vertex +1 -1 -1
ambient 0 0 0
specular 0 0 0
shininess 1
emission 0 0 0
diffuse 0 0 0
attenuation 1 0.1 0.05
point 0 0.44 -1.5 0.8 0.8 0.8
directional 0 1 -1 0.2 0.2 0.2
diffuse 0 0 1
#sphere 0 0.8 -1.5 0.1
pushTransform
#red
pushTransform
translate 0 0 -3
rotate 0 1 0 60
scale 10 10 1
diffuse 1 0 0
tri 0 1 2
tri 0 2 3
popTransform
#green
pushTransform
translate 0 0 -3
rotate 0 1 0 -60
scale 10 10 1
diffuse 0 1 0
tri 0 1 2
tri 0 2 3
popTransform
#back
pushTransform
scale 10 10 1
translate 0 0 -2
diffuse 1 1 1
tri 0 1 2
tri 0 2 3
popTransform
#sphere
diffuse 0.7 0.5 0.2
specular 0.2 0.2 0.2
pushTransform
translate 0 -0.7 -1.5
scale 0.1 0.1 0.1
sphere 0 0 0 1
popTransform
#cube
diffuse 0.5 0.7 0.2
specular 0.2 0.2 0.2
pushTransform
translate -0.25 -0.4 -1.8
rotate 0 1 0 15
scale 0.25 0.4 0.2
diffuse 1 1 1
tri 4 6 5
tri 6 7 5
tri 4 5 8
tri 5 9 8
tri 7 9 5
tri 7 11 9
tri 4 8 10
tri 4 10 6
tri 6 10 11
tri 6 11 7
tri 10 8 9
tri 10 9 11
popTransform
popTransform
popTransform
Maybe I've pushed it too hard for the one liner but that's my take (although idiomatic it might not be optimal):
First, CommandParams represents a command along with its arguments in a list format. If no arguments then we have None args:
case class CommandParams(command:String, params:Option[List[String]])
Then here's the file parsing and construction one liner along with line-by-line explanation:
val fileToDataStructure = Source.fromFile("file.txt").getLines() //open file and get lines iterator
.filter(!_.isEmpty) //exclude empty lines
.filter(!_.startsWith("#")) //exclude comments
.foldLeft(List[CommandParams]()) //iterate and store in a list of CommandParams
{(listCmds:List[CommandParams], line:String) => //tuple of a list of objs so far and the current line
val arr = line.split("\\s") //split line on any space delim
val command = arr.head //first element of array is the command
val args = if(arr.tail.isEmpty) None else Option(arr.tail.toList) //rest are their params
new CommandParams(command, args)::listCmds //construct the obj and cons it to the list
}
.reverse //due to cons concat we need to reverse to preserve order
A demo output iterating through it:
fileToDataStructure.foreach(println)
yields:
CommandParams(size,Some(List(640, 480)))
CommandParams(camera,Some(List(0, 0, 1, 0, 0, -1, 0, 1, 0, 45)))
CommandParams(output,Some(List(scene6.png)))
CommandParams(maxdepth,Some(List(5)))
CommandParams(maxverts,Some(List(12)))
CommandParams(vertex,Some(List(-1, +1, 0)))
...
CommandParams(pushTransform,None)
CommandParams(pushTransform,None)
CommandParams(translate,Some(List(0, 0, -3)))
...
A demo of how to iterate through it to do actual work once loaded:
fileToDataStructure.foreach{
cmdParms => cmdParms match {
case CommandParams(cmd, None) => println(s"I'm a ${cmd} with no args")
case CommandParams(cmd, Some(args))=> println(s"I'm a ${cmd} with args: ${args.mkString(",")}")
}
}
yields output:
I'm a size with args: 640,480
I'm a camera with args: 0,0,1,0,0,-1,0,1,0,45
I'm a output with args: scene6.png
I'm a maxdepth with args: 5
I'm a maxverts with args: 12
I'm a vertex with args: -1,+1,0
...
I'm a popTransform with no args
I'm a popTransform with no args