Updated Implementation of Roberts Algorithm

doc/examples/plot_edge_filter.py

@@ -0,0 +1,23 @@
+import matplotlib
+import matplotlib.pyplot as plt
+
+from skimage.data import camera
+from skimage.filter import roberts,sobel
+
+image = camera()
+edge_roberts = roberts(image)
+edge_sobel = sobel(image)
+
+plt.figure(figsize=(8, 2.5))
+plt.subplot(1, 2, 1)
+plt.imshow(edge_roberts, cmap=plt.cm.gray)
+plt.title('Roberts Edge Detection')
+plt.axis('off')
+
+plt.subplot(1, 2, 2)
+plt.imshow(edge_sobel, cmap=plt.cm.gray)
+plt.title('Sobel Edge Detection')
+plt.axis('off')
+
+
+plt.show()

skimage/filter/__init__.py

@@ -2,7 +2,7 @@ from .lpi_filter import *
 from .ctmf import median_filter
 from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
-                    hprewitt, vprewitt)
+                    hprewitt, vprewitt, roberts , pdroberts, ndroberts)
 from ._denoise import denoise_tv_chambolle, tv_denoise
 from ._denoise_cy import denoise_bilateral, denoise_tv_bregman
 from ._rank_order import rank_order

skimage/filter/edges.py

@@ -338,3 +338,91 @@ def vprewitt(image, mask=None):
                                        [1, 0, -1],
                                        [1, 0, -1]]).astype(float) / 3.0))
     return _mask_filter_result(result, mask)
+
+
+def roberts(image, mask=None):
+    """Find the edge magnitude using Roberts' Cross Operator.
+
+    Parameters
+	----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : ndarray
+        The Roberts' Cross edge map.
+    """
+    return np.sqrt(pdroberts(image, mask)**2 + ndroberts(image, mask)**2)
+
+
+def pdroberts(image, mask=None):
+    """Find the cross edges of an image using the Roberts' Cross operator.
+    The kernel is applied to the input image, to produce separate measurements
+    of the gradient component one orientation.
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : ndarray
+        The Robert edge map.
+
+    Notes
+    -----
+    We use the following kernel and return the absolute value of the
+    result at each point::
+
+      1   0
+      0  -1
+
+    """
+    image = img_as_float(image)
+    result = np.abs(convolve(image,
+                             np.array([[ 1, 0],
+                                       [ 0, -1]]).astype(float) / 1.0 ))
+    return _mask_filter_result(result, mask)
+
+
+def ndroberts(image, mask=None):
+    """Find the cross edges of an image using the Roberts' Cross operator.
+    The kernel is applied to the input image, to produce separate measurements
+    of the gradient component one orientation.
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : ndarray
+        The Robert edge map.
+
+    Notes
+    -----
+    We use the following kernel and return the absolute value of the
+    result at each point::
+
+      0   1
+     -1   0
+
+    """
+    image = img_as_float(image)
+    result = np.abs(convolve(image,
+                             np.array([[0, 1],
+                                       [-1, 0]]).astype(float) / 1.0))
+    return _mask_filter_result(result, mask)