OpenCV Python : No drawMatchesknn function - opencv

When I tried to use drawMatchesKnn function as mentioned in this tutorial for FLANN feature matching, I get the following error
AttributeError: 'module' object has no attribute 'drawMatchesKnn'
I checked with other resources that drawMatchesKnn method is present in opencv.
Why am I getting this error?
Thanks in advance


The functions cv2.drawMatches and cv2.drawMatchesKnn are not available in newer versions of OpenCV 2.4. #rayryeng provided a lightweight alternative which works as is for the output of DescriptorMatcher.match. The difference with DescriptorMatcher.knnMatch is that the matches are returned as a list of lists. To use the #rayryeng alternative, the matches must be extracted into a 1-D list.
For example, the Brute-Force Matching with SIFT Descriptors and Ratio Test tutorial could be amended as such:
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
# Removed the brackets around m
good.append(m)
# Invoke #rayryeng's drawMatches alternative, note it requires grayscale images
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
drawMatches(gray1,kp1,gray2,kp2,good)


You need to use OpenCV version 3. drawMatchesKnn() is present in 3.0.0-alpha but not in 2.4.11
That error is there, because you are using an old version of OpenCV.


Instead of doing good.append(m) try good.append([m])

Related

Can SpatialAcceleration be used with Eigen?

I am trying to compute product of \dot{J}\dot{q} in drake, where J is augmented form of angular and linear jacobian. For linear part only, I know that it can be done by CalcBiasTranslationalAcceleration function. The return type is Eigen::Vector3d
Eigen::Vector3d mdJv_dq = mplant.CalcBiasTranslationalAcceleration(*mContext,
multibody::JacobianWrtVariable::kV,
*mFrame_EE,
Eigen::Vector3d::Zero(),
mplant.world_frame(),
mplant.world_frame()
);
However, if I need to calculate both the linear and angular jacobain bias, I have to use SpatialAcceleration MultibodyPlant::CalcBiasSpatialAcceleration() and the function is as follow
multibody::SpatialAcceleration<double> mdJ_Aug_dq_robot1 = mplant.CalcBiasSpatialAcceleration(*mContext,
multibody::JacobianWrtVariable::kQDot,
*mFrame_EE,
Eigen::Vector3d::Zero(),
mplant.world_frame(),
mplant.world_frame()
);
Now the return type is spatialacceleration. If we need to use it along with Eigen::Matrix<double, 6,1> like subtracting the two quantities, we get an error as follow
no match for 'operator-' (operand types are 'Eigen::Matrix<double, 6, 1>' and 'drake::multibody::SpatialAcceleration<double>')
I could not find a method to utilize both of them togethor or converting spatialAcceleration quantity to Eigen. Any help and guidance will be appreciated.

You should be able to use get_coeffs() method of SpatialAcceleration to get the Eigen vector out then use normal Eigen operations. doxygen link
Or you can call SpatialAcceleration(my_other_eigen_vector) to lift your Eigen vector up into a SpatialAcceleration.

Plot vtk file using Python

I cannot display a vtk file using python. Spyder Code Analysis prompts me that vtkDataSetMapper is an undefined name.
I know the vtk file is in order because I have already displayed it using Paraview.
My vtk file looks like this:
# vtk DataFile Version 2.0
velocity field
ASCII
DATASET STRUCTURED_POINTS
DIMENSIONS 108 103 31
ORIGIN 0.0000000000000000 0.0000000000000000 -297.50000000000000
SPACING 15.465818554775332 12.565027060488859 10.000000000000000
POINT_DATA 344844
SCALARS scalars float
LOOKUP_TABLE default
8.4405251
8.4405251
...
...
...
After the last shown line, the vtk file contains the rest of information, which are merely numbers (~ 300000 values)
My code looks like this:
import vtk
# Read vtk file data
reader = vtk.vtkDataSetReader()
reader.SetFileName("seaust.vtk")
reader.ReadAllScalarsOn() # Activate the reading of all scalars
reader.ReadAllVectorsOn() # Activate the reading of all vectors
reader.ReadAllTensorsOn() # Activate the reading of all tensors
reader.Update()
data = reader.GetOutput()
scalar_range = data.GetScalarRange()
# Create the mapper that corresponds the objects of the vtk file
# into graphics elements
mapper = vtkDataSetMapper()
mapper.SetInput(data)
When trying to compile the code, python prompts me this error:
AttributeError: 'vtkRenderingCorePython.vtkDataSetMapper' object has no attribute 'SetInput'
I'm expecting a 3D visualisation of my data.
Can you please help me to get it?

I guess you are missing the vtk. in
mapper = vtk.vtkDataSetMapper()
and you probably need to use
mapper.SetInputData(data)

OpenCV Threshold Type

I have a question about OpenCV's example on Basic Thresholding as provided in the link below:
http://docs.opencv.org/2.4/doc/tutorials/imgproc/threshold/threshold.html#goal
I am slowly beginning to understand the code and have tried out an example too. However I am confused about a part of the code regarding thresholding operations. How does the thresholding function know which threshold operation to use?
This is where it is called:
threshold( src_gray, dst, threshold_value, max_BINARY_value,threshold_type);
I get that the last parameter "threshold_type is how it knows which threshold operation to use(eg. binary, binary inverted, truncated etc.) However in the code, this is all that is assigned to threshold_type:
int threshold_type = 3
As it is only assigned an int value of 3. How does the Threshold function know what operation to give it? Could someone explain it to me?

You should avoid using numeric literals to call the method of OpenCV instead use the constant variable defined in the opencv namespace, However it won't create any difference in output, but it makes the code more readable, So deciphered set of inputs to the cv::threshold() method are:
THRESH_BINARY = 0,
THRESH_BINARY_INV = 1,
THRESH_TRUNC = 2,
THRESH_TOZERO = 3,
THRESH_TOZERO_INV = 4,
THRESH_MASK = 7,
THRESH_OTSU = 8,
THRESH_TRIANGLE = 16
According to this table you are using thresholdType == THRESH_TOZERO

How to fetch vectors for a word list with Word2Vec?

I want to create a text file that is essentially a dictionary, with each word being paired with its vector representation through word2vec. I'm assuming the process would be to first train word2vec and then look-up each word from my list and find its representation (and then save it in a new text file)?
I'm new to word2vec and I don't know how to go about doing this. I've read from several of the main sites, and several of the questions on Stack, and haven't found a good tutorial yet.

The direct access model[word] is deprecated and will be removed in Gensim 4.0.0 in order to separate the training and the embedding. The command should be replaced with, simply, model.wv[word].
Using Gensim in Python, after vocabs are built and the model trained, you can find the word count and sampling information already mapped in model.wv.vocab, where model is the variable name of your Word2Vec object.
Thus, to create a dictionary object, you may:
my_dict = dict({})
for idx, key in enumerate(model.wv.vocab):
my_dict[key] = model.wv[key]
# Or my_dict[key] = model.wv.get_vector(key)
# Or my_dict[key] = model.wv.word_vec(key, use_norm=False)
Now that you have your dictionary, you can write it to a file with whatever means you like. For example, you can use the pickle library. Alternatively, if you are using Jupyter Notebook, they have a convenient 'magic command' %store my_dict > filename.txt. Your filename.txt will look like:
{'one': array([-0.06590105, 0.01573388, 0.00682817, 0.53970253, -0.20303348,
-0.24792041, 0.08682659, -0.45504045, 0.89248925, 0.0655603 ,
......
-0.8175681 , 0.27659689, 0.22305458, 0.39095637, 0.43375066,
0.36215973, 0.4040089 , -0.72396156, 0.3385369 , -0.600869 ],
dtype=float32),
'two': array([ 0.04694849, 0.13303463, -0.12208422, 0.02010536, 0.05969441,
-0.04734801, -0.08465996, 0.10344813, 0.03990637, 0.07126121,
......
0.31673026, 0.22282903, -0.18084198, -0.07555179, 0.22873943,
-0.72985399, -0.05103955, -0.10911274, -0.27275378, 0.01439812],
dtype=float32),
'three': array([-0.21048863, 0.4945509 , -0.15050395, -0.29089224, -0.29454648,
0.3420335 , -0.3419629 , 0.87303966, 0.21656844, -0.07530259,
......
-0.80034876, 0.02006451, 0.5299498 , -0.6286509 , -0.6182588 ,
-1.0569025 , 0.4557548 , 0.4697938 , 0.8928275 , -0.7877308 ],
dtype=float32),
'four': ......
}
You may also wish to look into the native save / load methods of Gensim's word2vec.

Gensim tutorial explains it very clearly.
First, you should create word2vec model - either by training it on text, e.g.
model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)
or by loading pre-trained model (you can find them here, for example).
Then iterate over all your words and check for their vectors in the model:
for word in words:
vector = model[word]
Having that, just write word and vector formatted as you want.

You can Directly get the vectors through
model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)
model.wv.vectors
and words through
model.wv.vocab.keys()
Hope it helps !

If you are willing to use python with gensim package, then building upon this answer and Gensim Word2Vec Documentation you could do something like this
from gensim.models import Word2Vec
# Take some sample sentences
tokenized_sentences = [["here","is","one"],["and","here","is","another"]]
# Initialise model, for more information, please check the Gensim Word2vec documentation
model = Word2Vec(tokenized_sentences, size=100, window=2, min_count=0)
# Get the ordered list of words in the vocabulary
words = model.wv.vocab.keys()
# Make a dictionary
we_dict = {word:model.wv[word] for word in words}

Gensim 4.0 updates: vocab method is depreciated and change in how to parse a word's vector
Get the ordered list of words in the vocabulary
words = list(w for w in model.wv.index_to_key)
Get the vector for 'also'
print(model.wv['also'])

Using basic python:
all_vectors = []
for index, vector in enumerate(model.wv.vectors):
vector_object = {}
vector_object[list(model.wv.vocab.keys())[index]] = vector
all_vectors.append(vector_object)

For gensim 4.0:
my_dict = dict({})
for word in word_list:
my_dict[word] = model.wv.get_vector('0', norm = True)

I would suggest this, you may find anything you need including Word2Vec, FastText, Doc2Vec, KeyedVectors and so on...

Invalid indexing operation error when trying to draw epipolar lines

I'm creating Stereo images processing project modeled on Matlab's examples. A copy pasted code from one of them don't works well.
I1 = rgb2gray(imread('viprectification_deskLeft.png'));
I2 = rgb2gray(imread('viprectification_deskRight.png'));
points1 = detectHarrisFeatures(I1);
points2 = detectHarrisFeatures(I2);
[features1, valid_points1] = extractFeatures(I1, points1);
[features2, valid_points2] = extractFeatures(I2, points2);
indexPairs = matchFeatures(features1, features2);
matchedPoints1 = valid_points1(indexPairs(:, 1),:);
matchedPoints2 = valid_points2(indexPairs(:, 2),:);
figure; showMatchedFeatures(I1, I2, matchedPoints1, matchedPoints2);
load stereoPointPairs
[fLMedS, inliers] = estimateFundamentalMatrix(matchedPoints1,matchedPoints2,'NumTrials',4000);
figure;
subplot(121); imshow(I1);
title('Inliers and Epipolar Lines in First Image'); hold on;
plot(matchedPoints1(inliers,1), matchedPoints1(inliers,2), 'go');
An error:
Error using epilineTest (line 24) Invalid indexing operation.
Best regards

Looks like you have an older version of MATLAB. Try doing this:
[fLMedS, inliers] = estimateFundamentalMatrix(...
matchedPoints1.Location, matchedPoints2.Location,'NumTrials',4000);
Generally, look at the example in your own local MATLAB documentation, rather than the one on the website. The website has the doc for the latest release (currently R2014a), and the examples may be using new features that do not exist in the older versions.

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