Update the doc

rfezzani · Jun 3, 2019 · cf3aaef · cf3aaef
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diff --git a/README.md b/README.md
@@ -4,8 +4,9 @@ Pyimof (for **Py**thon **im**age **o**ptical **f**low) is a pure
 python package for dense [optical
 flow](https://en.wikipedia.org/wiki/Optical_flow) estimation. Please
 visit the [documentation](https://pyimof.readthedocs.io/en/latest/)
-where some examples are provided in the
+and the
 [gallery](https://pyimof.readthedocs.io/en/latest/examples/index.html)
+for more details.
 
 ## Quick Example
 

diff --git a/pyimof/display.py b/pyimof/display.py
@@ -53,8 +53,9 @@ def flow_to_color(u, v, cmap=None, scale=True):
     magnitude.
 
     Any colormap compatible with matplotlib can be applyed but
-    circuler colormaps are recommanded: 'huv', 'twilight',
-    'twilight_shifted' and the builtin 'middlebury' colormaps.
+    circular colormaps are recommanded ( for example 'huv',
+    'twilight', 'twilight_shifted' and the builtin 'middlebury'
+    colormaps).
 
     If cmap is None, the HSV image defined using optical flow
     orientation (hue) and magnitude (saturation) is returned.
@@ -66,11 +67,9 @@ def flow_to_color(u, v, cmap=None, scale=True):
     v : ~numpy.ndarray
         The vertical component of the vector field.
     cmap : str (optional)
-        The colormap used to color code the input vector field
-        (default: None)
-    scale : bool
-        whether to scale output saturation according to magnitude
-        (default: True).
+        The colormap used to color code the input vector field.
+    scale : bool (optional)
+        whether to scale output saturation according to magnitude.
 
     Returns
     -------
@@ -124,12 +123,10 @@ def color_wheel(u=None, v=None, nr=50, ntheta=1025):
         The horizontal component of the vector field (default: None).
     v : ~numpy.ndarray (optional)
         The vertical component of the vector field (default: None).
-    nr : int
-        The number of steps used to discretise the wheel radius
-        (default: 50).
-    ntheta : int
-        The number of steps used to discretise the wheel sectors
-        (default:1025).
+    nr : int (optional)
+        The number of steps used to discretise the wheel radius.
+    ntheta : int (optional)
+        The number of steps used to discretise the wheel sectors.
 
     Returns
     -------
@@ -182,16 +179,14 @@ def plot(u, v, ax=None, cmap='middlebury', scale=True, colorwheel=True):
         The vertical component of the vector field.
     ax : ~matplotlib.pyplot.Axes (optional)
         Optional matplotlib axes used to plot the image. If None, the
-        image is displayed in a tight figure (default: None).
-    cmap : str
-        The colormap used to color code the input vector field
-        (default: 'middlebury')
-    scale : bool
-        whether to scale output saturation according to magnitude
-        (default: True).
-    colorwheel : bool
+        image is displayed in a tight figure.
+    cmap : str (optional)
+        The colormap used to color code the input vector field.
+    scale : bool (optional)
+        whether to scale output saturation according to magnitude.
+    colorwheel : bool (optional)
         whether to display the color wheel describing the images
-        colors or not (default: True).
+        colors or not.
 
     Returns
     -------
@@ -240,20 +235,20 @@ def quiver(u, v, c=None, bg=None, ax=None, step=None, nvec=50, bg_cmap=None,
     v : ~numpy.ndarray (with shape m×n)
         The vertical component of the vector field.
     c : ~numpy.ndarray (optional (with shape m×n))
-        Optional array of values used to color the arrows.
+        Values used to color the arrows.
     bg : ~numpy.ndarray (2D or 3D optional)
         Background image.
     ax : ~matplotlib.pyplot.Axes (optional)
         Axes used to plot the image. If None, the image is displayed
-        in a tight figure (default: None).
+        in a tight figure.
     step : int (optional)
         The grid step used to display the vector field. If None, it is
-        computed using the nvec parameter (default: None).
+        computed using the nvec parameter.
     nvec : int
         The maximum number of vector over all the grid dimentions. It
-        is ignored if the step parameter is not None (default: 50).
+        is ignored if the step parameter is not None.
     bg_cmap : str (optional)
-        The colormap used to color the background image (default: None).
+        The colormap used to color the background image.
 
     Notes
     -----

diff --git a/pyimof/solvers.py b/pyimof/solvers.py
@@ -153,24 +153,24 @@ def tvl1(I0, I1, dt=0.2, lambda_=15, tau=0.3, nwarp=5, niter=10,
     dt : float
         Time step of the numerical scheme. Convergence is proved for
         values dt < 0.125, but it can be larger for faster
-        convergence (default: 0.2).
+        convergence.
     lambda_ : float
         Attachement parameter. The smaller this parameter is,
-        the smoother is the solutions (default: 15).
+        the smoother is the solutions.
     tau : float
         Tightness parameter. It should have a small value in order to
         maintain attachement and regularization parts in
-        correspondence (default: 0.3).
+        correspondence.
     nwarp : int
-        Number of times I1 is warped (default: 5).
+        Number of times I1 is warped.
     niter : int
-        Number of fixed point iteration (default: 10).
+        Number of fixed point iteration.
     tol : float
         Tolerance used as stopping criterion based on the L² distance
-        between two consecutive values of (u, v) (default: 1e-4).
+        between two consecutive values of (u, v).
     prefilter : bool
         whether to prefilter the estimated optical flow before each
-        image warp (default: False).
+        image warp.
 
     Returns
     -------
@@ -294,19 +294,13 @@ def ilk(I0, I1, rad=7, nwarp=10, prefilter=False):
         The first gray scale image of the sequence.
     I1 : ~numpy.ndarray
         The second gray scale image of the sequence.
-    u0 : ~numpy.ndarray
-        Initialization for the horizontal component of the vector
-        field.
-    v0 : ~numpy.ndarray
-        Initialization for the vertical component of the vector
-        field.
     rad : int
-        Radius of the window considered around each pixel (default: 7).
+        Radius of the window considered around each pixel.
     nwarp : int
-        Number of times I1 is warped (default: 10).
+        Number of times I1 is warped.
     prefilter : bool
         whether to prefilter the estimated optical flow before each
-        image warp (default: False).
+        image warp.
 
     Returns
     -------

diff --git a/pyimof/util.py b/pyimof/util.py
@@ -131,12 +131,11 @@ def get_pyramid(I, downscale=2.0, nlevel=10, min_size=16):
     I : ~numpy.ndarray
         The image to be preprocessed (Gray scale or RGB).
     downscale : float
-        The pyramid downscale factor (default: 2)
+        The pyramid downscale factor.
     nlevel : int
-        The maximum number of pyramid levels (default: 10).
+        The maximum number of pyramid levels.
     min_size : int
-        The minimum size for any dimension of the pyramid levels
-        (default: 16).
+        The minimum size for any dimension of the pyramid levels.
 
     Returns
     -------
@@ -170,12 +169,11 @@ def coarse_to_fine(I0, I1, solver, downscale=2, nlevel=10, min_size=16):
     solver : callable
         The solver applyed at each pyramid level.
     downscale : float
-        The pyramid downscale factor (default: 2)
+        The pyramid downscale factor.
     nlevel : int
-        The maximum number of pyramid levels (default: 10).
+        The maximum number of pyramid levels.
     min_size : int
-        The minimum size for any dimension of the pyramid levels
-        (default: 16).
+        The minimum size for any dimension of the pyramid levels.
 
     Returns
     -------