algorithm/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-07-05 17:34:49 +08:00
Code Issues Packages Projects Releases Wiki Activity

Upgrade to Python 3.13 (#11588)

Browse Source
This commit is contained in:
Christian Clauss
2024-09-30 23:01:15 +02:00
committed by GitHub
parent a7bfa22455
commit 0177ae1cd5
35 changed files with 135 additions and 131 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
computer_vision/haralick_descriptors.py
View File

@ -19,7 +19,7 @@ def root_mean_square_error(original: np.ndarray, reference: np.ndarray) -> float
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([6, 4, 2]))
3.1622776601683795
"""
return np.sqrt(((original - reference) ** 2).mean())
return float(np.sqrt(((original - reference) ** 2).mean()))
def normalize_image(
@ -273,7 +273,7 @@ def haralick_descriptors(matrix: np.ndarray) -> list[float]:
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> concurrency = matrix_concurrency(mask_1, (0, 1))
>>> haralick_descriptors(concurrency)
>>> [float(f) for f in haralick_descriptors(concurrency)]
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
"""
# Function np.indices could be used for bigger input types,
@ -335,7 +335,7 @@ def get_descriptors(
return np.concatenate(descriptors, axis=None)
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> float:
"""
Simple method for calculating the euclidean distance between two points,
with type np.ndarray.
@ -346,7 +346,7 @@ def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
>>> euclidean(a, b)
3.3166247903554
"""
return np.sqrt(np.sum(np.square(point_1 - point_2)))
return float(np.sqrt(np.sum(np.square(point_1 - point_2))))
def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]: