Let's say I have a color image that I've loaded into a numpy array of dimensions (200 x 300 x 3). In total, there are 60,000 pixels in the image. I'm trying to extract the width,height (x,y) coordinates of each pixel starting from the upper left top corner representing pixel 1 such that:
pixel# x y
1 0 0
2 1 0
.
.
301 0 1
302 1 1
.
.
60,000 299 199
I'm tempted to use a for loop to do this in a more manual-nature but are there libraries or more effective ways to get those coordinate values for each pixel as such?
Since the format you're showing seems to be pandas, I'll present the output with pandas, but you could just use a mere print. :)
I even included a n-dimension solution to your problem as comments.
import numpy as np
from itertools import product
arr = np.array([
list(range(300))
for _ in range(200)
])
print(arr.shape)
# (200, 300)
pixels = arr.reshape(-1)
""" n-dimension solution
coords = map(range, arr.shape)
indices = np.array(list( product(*coords) ))
"""
xs = range(arr.shape[0])
ys = range(arr.shape[1])
indices = np.array(list(product(xs, ys)))
import pandas as pd
pd.options.display.max_rows = 20
index = pd.Series(pixels, name="pixels")
df = pd.DataFrame({
"x" : indices[:, 0],
"y" : indices[:, 1]
}, index=index)
print(df)
# x y
# pixels
# 0 0 0
# 1 0 1
# 2 0 2
# 3 0 3
# 4 0 4
# 5 0 5
# 6 0 6
# 7 0 7
# 8 0 8
# 9 0 9
# ... ... ...
# 290 199 290
# 291 199 291
# 292 199 292
# 293 199 293
# 294 199 294
# 295 199 295
# 296 199 296
# 297 199 297
# 298 199 298
# 299 199 299
# [60000 rows x 2 columns]