This module provides utility functions for creating and manipulating flattened indices and pointers for multi-dimensional arrays in Cython. It is designed to facilitate passing arrays of any dimension to Cython as memory views, enabling the release of the Global Interpreter Lock (GIL) for improved performance in parallel processing.
# Functions:
- get_pointer_array: Get a flat pointer array from the original array. # if you change it, the original data changes too
- get_iterarray: Get an array of flattened indices along with a flat pointer array for an input array.
- get_flat_iter_for_cython: Get an array of flattened indices and a flat pointer array suitable for use in Cython.
- get_iterarray_shape: Get an array of flattened indices with a specified last dimension.
import cv2
import numpy as np
from cythonanyarray import get_flat_iter_for_cython
data = cv2.imread(r"C:\Users\hansc\Pictures\socialmediaiconsmatching.png")
indexarray, flatpointerarray = get_flat_iter_for_cython(data, dtype=np.int64, unordered=True)
print(flatpointerarray)
# [[ 0 0 0 0]
# [ 5745 1 0 0]
# [ 11490 2 0 0]
# ...
# [5543924 964 1914 2]
# [5549669 965 1914 2]
# [5555414 966 1914 2]]
# [51 51 51 ... 43 43 43]
# Iterate through the flattened indices - Blueprint for Cython
# This snippet is crap in Python but in Cython it is pure gold (don't forget to add types!)
flatiter = len(indexarray)
for i in range(flatiter):
if flatpointerarray[indexarray[i][0]] == 255:
print(indexarray[i][1:])
break
# Accessing the original array using the flattened indices
print(data[34, 0, 0]) # Output: [34, 0, 0]
# Another example with a multi-dimensional array
data = np.arange(10 * 4 * 12 * 12 * 32).reshape((10, 4, 12, 12, 32)).astype(np.float64)
indexarray, flatpointerarray = get_flat_iter_for_cython(data, dtype=np.int64, unordered=True)
print(flatpointerarray)
# [[ 0 0 0 0 0 0]
# [ 18432 1 0 0 0 0]
# [ 36864 2 0 0 0 0]
# ...
# [147455 7 3 11 11 31]
# [165887 8 3 11 11 31]
# [184319 9 3 11 11 31]]
# [0.00000e+00 1.00000e+00 2.00000e+00 ... 1.84317e+05 1.84318e+05
# 1.84319e+05]
# Iterate through the flattened indices
flatiter = len(indexarray)
for i in range(flatiter):
if flatpointerarray[indexarray[i][0]] == 255.0:
print(indexarray[i][1:])
break
# Accessing the original array using the flattened indices
# Output: [0, 0, 0, 7, 31]
# Changing values
data = np.arange(100 * 100 * 100).reshape((100, 100, 100)).astype(np.int32)
indexarray, flatpointerarray = get_flat_iter_for_cython(data, dtype=np.int64, unordered=True)
print(flatpointerarray)
flatiter = len(indexarray)
for i in range(flatiter):
if indexarray[i][3] % 2 == 0:
flatpointerarray[indexarray[i][0]] = 10000000
print(data)
# [[[10000000 1 10000000 ... 97 10000000 99]
# [10000000 101 10000000 ... 197 10000000 199]
# [10000000 201 10000000 ... 297 10000000 299]
# ...
# [10000000 9701 10000000 ... 9797 10000000 9799]
# [10000000 9801 10000000 ... 9897 10000000 9899]
# [10000000 9901 10000000 ... 9997 10000000 9999]]