Category: Python Answers

Sometimes, we want to convert an RGB image into grayscale in Python.

In this article, we’ll look at how to convert an RGB image into grayscale in Python.

How to convert an RGB image into grayscale in Python?

To convert an RGB image into grayscale in Python, we can use the convert method.

For instance, we write

from PIL import Image

img = Image.open('image.png').convert('L')
img.save('greyscale.png')

to call Image.open with the image path to open the image.

Then we call convert with 'L' to convert the image to grayscale.

Finally, we call save to save the image to a file.

Conclusion

To convert an RGB image into grayscale in Python, we can use the convert method.

Sometimes, we want to generate a PNG with matplotlib when the DISPLAY environment variable is undefined.

In this article, we’ll look at how to generate a PNG with matplotlib when the DISPLAY environment variable is undefined.

How to generate a PNG with matplotlib when the DISPLAY environment variable is undefined?

To generate a PNG with matplotlib when the DISPLAY environment variable is undefined, we can call the matpolotlib method.

For instance, we write

import matplotlib

matplotlib.use('Agg')

to call matplotlib.use to make matplotlib use a different display back end than the default X-Window back end.

Conclusion

To generate a PNG with matplotlib when the DISPLAY environment variable is undefined, we can call the matpolotlib method.

Sometimes, we want to prevent tensorflow from allocating all of a GPU’s memory with Python.

In this article, we’ll look at how to prevent tensorflow from allocating all of a GPU’s memory with Python.

How to prevent tensorflow from allocating all of a GPU’s memory with Python?

To prevent tensorflow from allocating all of a GPU’s memory with Python, we can create a GPUOptions object.

For instance, we write

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

to create a GPUOptions object with the per_process_gpu_memory_fraction argument set to 0.333 to make tensorflow use 33.3% of the GPU’s memory.

Then we start a session with gpu_options with

sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

Conclusion

To prevent tensorflow from allocating all of a GPU’s memory with Python, we can create a GPUOptions object.

Sometimes, we want to get indices of N maximum values in a Python NumPy array.

In this article, we’ll look at how to get indices of N maximum values in a Python NumPy array.

How to get indices of N maximum values in a Python NumPy array?

To get indices of N maximum values in a Python NumPy array, we can use the np.argpartition method.

For instance, we write

a = np.array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0])
ind = np.argpartition(a, -4)[-4:]

to create the a numpy array with

a = np.array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0])

Then we call argpartition with the array a and -4 to get the indices of the top 4 elements from sorted by value at the given index ascending with

np.argpartition(a, -4)[-4:]

Conclusion

To get indices of N maximum values in a Python NumPy array, we can use the np.argpartition method.

Sometimes, we want to terminate a script with Python

in this article, we’ll look at how to terminate a script with Python.

How to terminate a script with Python?

To terminate a script with Python, we can call sys.exit.

For instance, we write

import sys

sys.exit()

to call sys.exit with an optional exit code argument.

If we call it with anything other than 0, we exit the script with an abnormal termination.

Conclusion

To terminate a script with Python, we can call sys.exit.