pycharmers.utils.numpy_utils module

pycharmers.utils.numpy_utils.take_centers(a)

Take a center values.

Parameters

a (array) – Array

Example

>>> from pycharmers.utils import take_centers
>>> take_centers([0,1,2,3,4,5])
array([0.5, 1.5, 2.5, 3.5, 4.5])
>>> take_centers([[0,1,2,3,4,5],[1,2,3,4,5,6]])
array([[0.5, 1.5, 2.5, 3.5, 4.5],
    [1.5, 2.5, 3.5, 4.5, 5.5]])

pycharmers.utils.numpy_utils.confusion_matrix(y_true, y_pred)

Compute confusion matrix to evaluate the accuracy of a classification.

By definition a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to the number of observations known to be in group \(i\) and predicted to be in group \(j\).

Thus in binary classification, the count of true negatives is \(C_{0,0}\), false negatives is \(C_{1,0}\), true positives is \(C_{1,1}\) and false positives is \(C_{0,1}\).

Parameters

• y_true (array) – Ground truth (correct) target values.

• y_pred (array) – Estimated targets as returned by a classifier.

Returns

Confusion matrix whose i-th row and j-th column entry indicates

the number of samples with true label being i-th class and prediced label being j-th class.

Return type

cm (array)

References

Wikipedia entry for the Confusion matrix (Wikipedia and other references may use a different convention for axes)

Examples

>>> from pycharmers import confusion_matrix
>>> y_true = [2, 0, 2, 2, 0, 1]
>>> y_pred = [0, 0, 2, 2, 0, 2]
>>> confusion_matrix(y_true, y_pred)
array([[2, 0, 0],
    [0, 0, 1],
    [1, 0, 2]])
>>> # In the binary case, we can extract true positives, etc as follows:
>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
>>> (tn, fp, fn, tp)
(0, 2, 1, 1)

pycharmers.utils.numpy_utils.rotate2d(a, theta)

Rotate 2d vectors using Rotation matrix \(R(\theta)\)

\[\begin{split}R(\theta) = \left( \begin{array}{c} \cos\theta & -\sin\theta \\ \sin\theta & \cos\theta \end{array} \right)\end{split}\]

Using this matrix, the rotation of the vector can be expressed as follows.

\[\begin{split}\left( \begin{array}{c} x^{\prime} \\ y^{\prime} \end{array} \right) = \left( \begin{array}{c} \cos\theta & -\sin\theta \\ \sin\theta & \cos\theta \end{array} \right)\left( \begin{array}{c} x \\ y \end{array} \right)\end{split}\]

Parameters

• a (ndarray) – Array

• theta (float) – float value like 1/2*np.pi

pycharmers.utils.numpy_utils.replaceArray(a, old=255, 255, 255, new=0, 0, 0)

Replace an Array from old to new

Parameters

• old (tuple) – Old value.

• new (tuple) – New value.

Returns

New Array

Return type

np.ndarray

Examples

>>> import cv2
>>> import numpy as np
>>> from pycharmers.opencv import SAMPLE_LENA_IMG, cv2plot
>>> from pycharmers.utils import replaceArray
>>> img = cv2.imread(SAMPLE_LENA_IMG)
>>> img = replaceArray(img, old=[77, 66, 176], new=[0,0,0]).astype(np.uint8)
>>> cv2plot(img, is_cv2=True)

pycharmers.utils.numpy_utils.fill_between_angle(arr, s, e, center=None, is_radian=True)

Fill Between s and e.

Parameters

• arr (np.ndarray) – Input array.

• s (Number) – Start angle of fill.

• e (Number) – End angle of fill.

• center (tuple) – Center coordinates.

• is_radian (bool, optional) – whether s and e are defined in radians.

Returns

Whether it is a place to be filled.

Return type

np.ndarray

Examples

>>> import numpy as np
>>> from PIL import Image
>>> from pycharmers.utils import fill_between_angle
>>> arr = np.zeros(shape=(100,100,3)).astype(np.uint8)
>>> flag = fill_between_angle(arr, s=30, e=120, is_radian=False)
>>> Image.fromarray(np.where(flag, arr, 255))