Eliminating Number Of Connected Pixels Smaller Than Some Specified Number Threshold

I have some data and the dimension is 249X250. I have used the following code to plot the data: import numpy as np import pandas as pd import matplotlib.pyplot as pl data = pd.

Solution 1:

Essentially what you are looking to do is identify all objects in your image. This can be done with ndimage.measurements.label from scipy. Essentially it searches through an image for continuous groups of pixels and assigns them a label. You can then loop through those labeled sectors and count the size (in pixels) of the object and filter on that basis.

Even though you are pulling data in from Excel--what you effectively have is a 249x250 pixel "image" that you are plotting. Each cell in Excel is effectively a "pixel" containing a value. To drive this point home you could quite literally use the image-showing functions in matplotlib (e.g. plt.imshow)

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage

xn = 250
yn = 249# fake data to illustrate that images are just matrices of values
X = np.stack([np.arange(xn)] * yn)
Y = np.stack([np.arange(yn)] * xn).transpose()
Z = np.sin(3*np.pi * X/xn) * np.cos(4*np.pi * Y/yn) * np.sin(np.pi * X/xn)
Z[Z <.5] = 0

fig,axes = plt.subplots(1,2)
axes[0].contourf(Z)
axes[0].set_title("Before Removing Features")

# now identify the objects and remove those above a threshold
Zlabeled,Nlabels = ndimage.measurements.label(Z)
label_size = [(Zlabeled == label).sum() for label inrange(Nlabels + 1)]
for label,size inenumerate(label_size): print("label %s is %s pixels in size" % (label,size))

# now remove the labelsfor label,size inenumerate(label_size):
    if size < 1800:
        Z[Zlabeled == label] = 0

axes[1].contourf(Z)
axes[1].set_title("After Removing Features")

Illustrated outcome: