How to combine dimensions in numpy array?
Without modifying your reading function:
imgs = readImages(...)
print imgs.shape # (100, 718, 686, 3)
# flatten axes -2 and -3, using -1 to autocalculate the size
pixel_lists = imgs.reshape(imgs.shape[:-3] + (-1, 3))
print pixel_lists.shape # (100, 492548, 3)
In case anyone wants it. Here's a general way of doing this
import functools
def combine_dims(a, i=0, n=1):
"""
Combines dimensions of numpy array `a`,
starting at index `i`,
and combining `n` dimensions
"""
s = list(a.shape)
combined = functools.reduce(lambda x,y: x*y, s[i:i+n+1])
return np.reshape(a, s[:i] + [combined] + s[i+n+1:])
With this function you could use it like this:
imgs = combine_dims(imgs, 1) # combines dimension 1 and 2
# imgs.shape = (100, 718*686, 3)
import cv2
import os
import numpy as np
def readImages(path):
imgs = np.empty((0, 492548, 3))
for file in os.listdir(path):
if file.endswith('.png'):
img = cv2.imread(file)
img = img.reshape((1, 492548, 3))
imgs = np.append(imgs, img, axis=0)
return (imgs)
imgs = readImages(...)
print imgs.shape # (100, 492548, 3)
The trick was to reshape and append to a numpy array. It's not good practice to hardcode the length of the vector (492548) so if I were you I'd also add a line that calculates this number and puts it in a variable, for use in the rest of the script.