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059 Python: Tensorflow - AttributeError: 'OwnedIterator' object has no attribute 'string_handle'

the code is as follows :
iterator = tf.compat.v1.data.make_one_shot_iterator(batched_dataset)
handle = iterator.string_handle()
i try to run a Captcha crack code which is this github project and despite the fact that i had to modify so many parts of the code for compatibility issues i face this error and the error says :
AttributeError: 'OwnedIterator' object has no attribute 'string_handle'
i have been trying for more than 1 day and couldn't and can't find any solution to this issue hope someone help me run the code with No Error!
i am using Pycharm and python and Tensorflow version = 2.4.1

cv2.cuda_CascadeClassifier in python

I am attempting to use the Haarclassifier from opencv cuda, for this I found the object cv.cuda_CascadeClassifier. However, assigning cv.cuda_CascadeClassifier() to a variable spit the following error:
this object has no ''load'' attribute. I could successfully verify it
by printing their dir() print(dir(cv.cuda_CascadeClassifier)).
Is there any other way to call this object or did anyone effectively exploite the cascadeclassifier with opencv cuda?
thx

The lack of documentation for the python API really is a pain. Speaking about the version 4.5 of OpenCV, you have to call the create method when reading a xml cascade file or it'll yield segmentation fault when trying to detect. In my experience you'll also need to convert to gray scale or it will yield (-215:Assertion failed) src.type() == CV_8UC1.
Here's my working code on OpenCV 4.5.1:
import cv2
img = cv2.imread('./img_sample.jpg')
cascade = cv2.cuda_CascadeClassifier.create('./cascade_file.xml')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cuFrame = cv2.cuda_GpuMat(gray_img)
result = cascade.detectMultiScale(cuFrame).download() # download() gets the result as UMat
if result is not None:
print(result[0])
I hope this answers your question about the cuda version usage.

This is most likely due to the use of a version of openCV between 4.0.0 and 4.3.0, In those versions cuda_CascadeClassifier was disabled. In 4.4.0 This functionallity was brought back. (https://github.com/opencv/opencv_contrib/pull/2554/files)
Even though this seems to work fine in C++ it gives me a segmentation fault using the python wrapper using the following code:
classifier_cuda = cv2.cuda_CascadeClassifier('cascades_file.xml')
while True:
success, frame = vidcap.read()
cuFrame = cv2.cuda_GpuMat(frame)
result = classifier_cuda.detectMultiScale(cuFrame)
print (result)
Any solutions?

As already Breno Alef wrote, the problem of OpenCV for Python is the lack of documentation and also of some code examples, which makes difficult to understand how to write correctly Python code to use OpenCV.
Looking at the documentation of the cuda_CascadeClassifier or using the Python built-in function help() you can see that the cuda_CascadeClassifier is a subclass of cv.Algorithm, which contains the load() method, but the problem is the way cuda_CascadeClassifier works is a bit different from the Cascade class declared in opencv2/objdetect.hpp.

Using TriangulatePoints with SFM with Python

I'm trying to use the built in opencv function TriangulatePoints for multiple views: https://docs.opencv.org/3.4.4/d0/dbd/group__triangulation.html#ga211c855276b3084f3bbd8b2d9161dc74.
Using python on linux, does anyone have experience using this function? The website only has the syntax for cpp and in python I don't know how to code it.
My code now:
import cv2
import numpy as np
point_2D = np.array([[17.4485, 709.7993], [17.4382, 709.8409]])
Proj_Matrices = np.array([ [1037.5, -6.9927, -10.0190, -4780.7], [6.9747, 1043.3, -5.8867, -731.9206], [644.7895, 383.4982, -3231.1], [1036.937, -22.8371, -28.3254, -5607.7], [23.0587, 1043.1, 3.1815, -633.4485], [650.4355, 373.6, -15.3504, -3706.5] ])
OutputArray = np.zeros((3,2))
Points_3D = cv2.sfm.triangulatePoints(point_2D, Proj_Matrices, OutputArray)
However, when I run it from the terminal I get the following error:
AttributeError: module 'cv2' has no attribute 'sfm.
I installed sfm on my computer using the instructions on the opencv site.
When I omit sfm I get the following error:
AttributeError: module 'cv2' has no attribute 'sfm'
I think I get that error since the machine thinks I'm trying to use the previous version of TriangulatePoints.
Examples of opencv code using sfm exist on the opencv website but they are in cpp.
I'm wondering what the syntax for using TriangulatePoints(with sfm) is in python? Even in cpp, I don't understand how the output array is an input to the function? Also, if anyone knows how to fix the sfm error I am receiving that would be appreciated.
Thanks!

NullFunctionError: Attempt to call an undefined alternate function (glGenFramebuffers, glGenFramebuffersEXT)

Currently I'm working with 3D objects rendering. In that while dealing with framebuffer part I'm getting some error.
self.fbo = glGenFramebuffers(1)
whenever interpreter hits this line its showing following error
**File "C:\Python27\lib\site-packages\OpenGL\latebind.py", line 44, in __call__
self._finalCall = self.finalise()
File "C:\Python27\lib\site-packages\OpenGL\extensions.py", line 189, in finalise
self.__name__,
NullFunctionError: Attempt to call an undefined alternate function (glGenFramebuffers, glGenFramebuffersEXT), check for bool(glGenFramebuffers) before calling**
I'm using python 2.7.3 and pyOpenGL 3.0.2.I couldn't find any answer for this error.

If bool(glGenFramebuffers) returns False, the error probably means that your computer does not have access to OpenGL >= 2.1 so Framebufffer objects won't work. Check your OpenGL supported version with GPU Caps Viewer for Windows. For Linux see here: https://askubuntu.com/questions/47062/what-is-terminal-command-that-can-show-opengl-version
If you have at least 2.1, then maybe the library you are using to create the context (pySDL, glut, pySFML, etc.) is not creating a compatible one. Fixing that depends on the library and probably already has an answer.
If bool(glGenFramebuffers) returns True, the problem might be somewhere else early in the code.
Also, remember that the context must be created and current before trying to create or use shaders, framebuffers, etc.

Weird error in sklearn.cluster.KMeans

I want to use mr. yasaichi's implementation of x-means written in Python for my master's thesis (yasaichi's x-means: https://gist.github.com/yasaichi/254a060eff56a3b3b858) . For the last few weeks there have been no problem and I have been running the algorithm several times on various data sets. Today, however, a weird error popped up:
AttributeError: 'KMeans' object has no attribute 'get_params'.
The error comes from line 75 in the yasaichi's implementation:
labels = range(0, k_means.get_params()["n_clusters"])
Originally I thought it was me who had done some weird changes to the code, but when I re-downloaded the original again it came up with the same error.
Any ideas?

It sounds like the KMeans object you are trying to use doesn't have the method get_params.
I just tested the code at https://gist.github.com/yasaichi/254a060eff56a3b3b858 and it worked for me. So, my best guess is that you are somehow overwriting the KMeans object or that your code is using a cached version of the code that defines the KMeans object.
To verify this, try adding print dir(k_means) before line 75 of yasaichi's implementation. You should also see that print k_means.__module__ should show sklearn.cluster.k_means_. If this is the case, the final thing I would recommend would be deleting the compiled Python file implementing the k_means_ module. This can be found by running the following:
import sklearn.cluster.k_means_
print sklearn.cluster.__file__