doco update

Author: petercorke

machinevisiontoolbox/ImageCore.py

@@ -648,6 +648,7 @@ def umax(self):
             >>> from machinevisiontoolbox import Image
             >>> img = Image.Read('flowers1.png')
             >>> img.umax
+            >>> img.width
 
         :seealso: :meth:`width`
         """
@@ -668,6 +669,7 @@ def vmax(self):
             >>> from machinevisiontoolbox import Image
             >>> img = Image.Read('flowers1.png')
             >>> img.vmax
+            >>> img.height
 
         :seealso: :meth:`height`
         """
@@ -1676,9 +1678,16 @@ def red(self):
             >>> from machinevisiontoolbox import Image
             >>> img = Image.Read("flowers4.png")
             >>> red = img.red() # red plane
+            >>> red.disp()
             >>> red
             >>> red.iscolor
 
+        .. plot::
+
+            from machinevisiontoolbox import Image
+            Image.Read("flowers4.png").red().disp()
+
+
         :seealso: :meth:`plane` :meth:`green` :meth:`blue`
         """
         return self.plane("R")
@@ -1698,9 +1707,15 @@ def green(self):
             >>> from machinevisiontoolbox import Image
             >>> img = Image.Read("flowers4.png")
             >>> green = img.green() # green plane
+            >>> green.disp()
             >>> green
             >>> green.iscolor
 
+        .. plot::
+
+            from machinevisiontoolbox import Image
+            Image.Read("flowers4.png").green().disp()
+
         :seealso: :meth:`plane` :meth:`red` :meth:`blue`
         """
         return self.plane("G")
@@ -1720,9 +1735,15 @@ def blue(self):
             >>> from machinevisiontoolbox import Image
             >>> img = Image.Read("flowers4.png")
             >>> blue = img.blue() # blue plane
+            >>> blue.disp()
             >>> blue
             >>> blue.iscolor
 
+        .. plot::
+
+            from machinevisiontoolbox import Image
+            Image.Read("flowers4.png").blue().disp()
+
         :seealso: :meth:`plane` :meth:`red` :meth:`green`
         """
         return self.plane("B")

machinevisiontoolbox/ImageIO.py

@@ -67,7 +67,7 @@ def Read(cls, filename, alpha=False, rgb=True, **kwargs):
 
         :note:  If the path is not absolute it is first searched for relative
             to the current directory, and if not found, it is searched for in
-            the ``images`` folder of the ``mvtb_data`` package.
+            the ``images`` folder of the ```mvtb_data`` package <https://github.com/petercorke/machinevision-toolbox-python/tree/master/mvtb-data>`_.
 
         :seealso: :func:`~machinevisiontoolbox.base.imageio.iread` :func:`~machinevisiontoolbox.base.imageio.convert`  `cv2.imread <https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56>`_
         """

machinevisiontoolbox/ImageProcessing.py

@@ -281,8 +281,8 @@ def stretch(self, max=1, range=None, clip=True):
 
         :references:
 
-            - Robotics, Vision & Control, Section 12.1, P. Corke,
-              Springer 2011.
+            - Robotics, Vision & Control for Python, Section 12.1, P. Corke,
+              Springer 2023.
         """
 
         # TODO make all infinity values = None?

machinevisiontoolbox/ImageReshape.py

@@ -503,9 +503,10 @@ def decimate(self, m=2, sigma=None):
         :note:
 
             - If the image has multiple planes, each plane is decimated.
-            - Smoothing is used to eliminate aliasing artifacts and the
-              standard deviation should be chosen as a function of the maximum
-              spatial frequency in the image.
+            - Smoothing is applied to the image _before_ decimation to reduce
+              high-spatial-frequency components and reduce eliminate aliasing
+              artifacts.  The standard deviation should be chosen as a function
+              of the maximum spatial-frequency in the image.
 
         Example:
 

machinevisiontoolbox/VisualServo.py

@@ -501,8 +501,8 @@ def __init__(self, camera, eterm=0, lmbda=0.05, **kwargs):
             pbself.plot_p
 
         References::
-        - Robotics, Vision & Control, Chap 15
-        P. Corke, Springer 2011.
+        - Robotics, Vision & Control for Python, Chap 15
+          P. Corke, Springer 2023.
 
         .. note:: The history attribute is a vector of structures each of which is a snapshot at
             each simulation step of information about the image plane, camera pose, error,
@@ -621,8 +621,8 @@ def __init__(
         elements are the depth for the corresponding world points.
 
         References::
-            - Robotics, Vision & Control, Chap 15
-              P. Corke, Springer 2011.
+            - Robotics, Vision & Control for Python, Chap 15
+              P. Corke, Springer 2023.
 
         .. note::
             - The history property is a vector of structures each of which is a
@@ -887,8 +887,8 @@ def __init__(self, camera, eterm=0.01, plane=None, lmbda=0.08, **kwargs):
         The plane applies to all lines.
 
         References::
-            - Robotics, Vision & Control, Chap 15
-              P. Corke, Springer 2011.
+            - Robotics, Vision & Control for Python, Chap 15
+              P. Corke, Springer 2023.
 
         .. note::
             - The history property is a vector of structures each of which is a
@@ -1113,8 +1113,8 @@ def __init__(self, camera, eterm=0.08, plane=None, lmbda=0.04, **kwargs):
         The plane applies to all lines.
 
         References::
-            - Robotics, Vision & Control, Chap 15
-              P. Corke, Springer 2011.
+            - Robotics, Vision & Control for Python, Chap 15
+              P. Corke, Springer 2023.
 
         .. note::
             - The history property is a vector of structures each of which is a
@@ -1294,8 +1294,8 @@ def __init__(self, camera, eterm=0.001, lmbda=0.1, depth=None, **kwargs):
         The plane applies to all lines.
 
         References::
-            - Robotics, Vision & Control, Chap 15
-              P. Corke, Springer 2011.
+            - Robotics, Vision & Control for Python, Chap 15
+              P. Corke, Springer 2023.
 
         .. note::
             - The history property is a vector of structures each of which is a
@@ -1530,8 +1530,8 @@ def __init__(self, camera, eterm=0.01, lmbda=0.02, depth=None, **kwargs):
         The plane applies to all lines.
 
         References::
-            - Robotics, Vision & Control, Chap 15
-              P. Corke, Springer 2011.
+            - Robotics, Vision & Control for Python, Chap 15
+              P. Corke, Springer 2023.
 
         .. note::
             - The history property is a vector of structures each of which is a

machinevisiontoolbox/base/imageio.py

@@ -670,16 +670,21 @@ def iread(filename, *args, **kwargs):
         a 2D or 3D NumPy ndarray
 
     Loads an image from a file or URL, and returns the
-    image as a NumPy array, as well as the absolute path name.  The
-    image can by greyscale or color in any of the wide range of formats
-    supported by the OpenCV ``imread`` function.
+    image as a NumPy array, as well as the absolute path name.  
 
+    If the path is not absolute it is first searched for relative
+    to the current directory, and if not found, it is searched for in
+    the ``images`` folder of the 
+    ```mvtb_data`` package <https://github.com/petercorke/machinevision-toolbox-python/tree/master/mvtb-data>`_.
+    
     If ``file`` is a list or contains a wildcard, the result will be a list of
     ``(image, path)`` tuples.  They will be sorted by path.
 
-    Extra options can be passsed to perform datatype conversion, color to
-    grey scale conversion, gamma correction, image decimation or region of
-    interest windowing.  Details are given at :func:`convert`.
+    The image can by greyscale or color in any of the wide range of formats
+    supported by the OpenCV ``imread`` function. Extra options can be passsed to
+    perform datatype conversion, color to grey scale conversion, gamma
+    correction, image decimation or region of interest windowing.  Details are
+    given at :func:`convert`.
 
     Example: