Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/util/_invert.py

@@ -0,0 +1,74 @@
+import numpy as np
+from .dtype import dtype_limits
+
+
+def invert(image, signed_float=False):
+    """Invert an image.
+
+    Invert the intensity range of the input image, so that the dtype maximum
+    is now the dtype minimum, and vice-versa. This operation is
+    slightly different depending on the input dtype:
+
+    - unsigned integers: subtract the image from the dtype maximum
+    - signed integers: subtract the image from -1 (see Notes)
+    - floats: subtract the image from 1 (if signed_float is False, so we
+      assume the image is unsigned), or from 0 (if signed_float is True).
+
+    See the examples for clarification.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    signed_float : bool, optional
+        If True and the image is of type float, the range is assumed to
+        be [-1, 1]. If False and the image is of type float, the range is
+        assumed to be [0, 1].
+
+    Returns
+    -------
+    inverted : ndarray
+        Inverted image.
+
+    Notes
+    -----
+    Ideally, for signed integers we would simply multiply by -1. However,
+    signed integer ranges are asymmetric. For example, for np.int8, the range
+    of possible values is [-128, 127], so that -128 * -1 equals -128! By
+    subtracting from -1, we correctly map the maximum dtype value to the
+    minimum.
+
+    Examples
+    --------
+    >>> img = np.array([[100,  0, 200],
+    ...                 [  0, 50,   0],
+    ...                 [ 30,  0, 255]], np.uint8)
+    >>> invert(img)
+    array([[155, 255,  55],
+           [255, 205, 255],
+           [225, 255,   0]], dtype=uint8)
+    >>> img2 = np.array([[ -2, 0, -128],
+    ...                  [127, 0,    5]], np.int8)
+    >>> invert(img2)
+    array([[   1,   -1,  127],
+           [-128,   -1,   -6]], dtype=int8)
+    >>> img3 = np.array([[ 0., 1., 0.5, 0.75]])
+    >>> invert(img3)
+    array([[1.  , 0.  , 0.5 , 0.25]])
+    >>> img4 = np.array([[ 0., 1., -1., -0.25]])
+    >>> invert(img4, signed_float=True)
+    array([[-0.  , -1.  ,  1.  ,  0.25]])
+    """
+    if image.dtype == 'bool':
+        inverted = ~image
+    elif np.issubdtype(image.dtype, np.unsignedinteger):
+        max_val = dtype_limits(image, clip_negative=False)[1]
+        inverted = np.subtract(max_val, image, dtype=image.dtype)
+    elif np.issubdtype(image.dtype, np.signedinteger):
+        inverted = np.subtract(-1, image, dtype=image.dtype)
+    else:  # float dtype
+        if signed_float:
+            inverted = -image
+        else:
+            inverted = np.subtract(1, image, dtype=image.dtype)
+    return inverted