"""
This module deals with making images (np arrays). It provides drawing
methods that are difficult to do with the existing Python libraries.
"""
import numpy as np
[docs]def blit(im1, im2, pos=None, mask=None, ismask=False):
""" Blit an image over another.
Blits ``im1`` on ``im2`` as position ``pos=(x,y)``, using the
``mask`` if provided. If ``im1`` and ``im2`` are mask pictures
(2D float arrays) then ``ismask`` must be ``True``.
"""
if pos is None:
pos = [0, 0]
# xp1,yp1,xp2,yp2 = blit area on im2
# x1,y1,x2,y2 = area of im1 to blit on im2
xp, yp = pos
x1 = max(0, -xp)
y1 = max(0, -yp)
h1, w1 = im1.shape[:2]
h2, w2 = im2.shape[:2]
xp2 = min(w2, xp + w1)
yp2 = min(h2, yp + h1)
x2 = min(w1, w2 - xp)
y2 = min(h1, h2 - yp)
xp1 = max(0, xp)
yp1 = max(0, yp)
if (xp1 >= xp2) or (yp1 >= yp2):
return im2
blitted = im1[y1:y2, x1:x2]
new_im2 = +im2
if mask is None:
new_im2[yp1:yp2, xp1:xp2] = blitted
else:
mask = mask[y1:y2, x1:x2]
if len(im1.shape) == 3:
mask = np.dstack(3 * [mask])
blit_region = new_im2[yp1:yp2, xp1:xp2]
new_im2[yp1:yp2, xp1:xp2] = (1.0 * mask * blitted + (1.0 - mask) * blit_region)
return new_im2.astype('uint8') if (not ismask) else new_im2
[docs]def color_gradient(size,p1,p2=None,vector=None, r=None, col1=0,col2=1.0,
shape='linear', offset = 0):
"""Draw a linear, bilinear, or radial gradient.
The result is a picture of size ``size``, whose color varies
gradually from color `col1` in position ``p1`` to color ``col2``
in position ``p2``.
If it is a RGB picture the result must be transformed into
a 'uint8' array to be displayed normally:
Parameters
------------
size
Size (width, height) in pixels of the final picture/array.
p1, p2
Coordinates (x,y) in pixels of the limit point for ``col1``
and ``col2``. The color 'before' ``p1`` is ``col1`` and it
gradually changes in the direction of ``p2`` until it is ``col2``
when it reaches ``p2``.
vector
A vector [x,y] in pixels that can be provided instead of ``p2``.
``p2`` is then defined as (p1 + vector).
col1, col2
Either floats between 0 and 1 (for gradients used in masks)
or [R,G,B] arrays (for colored gradients).
shape
'linear', 'bilinear', or 'circular'.
In a linear gradient the color varies in one direction,
from point ``p1`` to point ``p2``.
In a bilinear gradient it also varies symetrically form ``p1``
in the other direction.
In a circular gradient it goes from ``col1`` to ``col2`` in all
directions.
offset
Real number between 0 and 1 indicating the fraction of the vector
at which the gradient actually starts. For instance if ``offset``
is 0.9 in a gradient going from p1 to p2, then the gradient will
only occur near p2 (before that everything is of color ``col1``)
If the offset is 0.9 in a radial gradient, the gradient will
occur in the region located between 90% and 100% of the radius,
this creates a blurry disc of radius d(p1,p2).
Returns
--------
image
An Numpy array of dimensions (W,H,ncolors) of type float
representing the image of the gradient.
Examples
---------
>>> grad = color_gradient(blabla).astype('uint8')
"""
# np-arrayize and change x,y coordinates to y,x
w,h = size
col1 = np.array(col1).astype(float)
col2 = np.array(col2).astype(float)
if shape == 'bilinear':
if vector is None:
vector = np.array(p2) - np.array(p1)
m1, m2 = [ color_gradient(size, p1, vector=v, col1 = 1.0, col2 = 0,
shape = 'linear', offset= offset)
for v in [vector,-vector]]
arr = np.maximum(m1, m2)
if col1.size > 1:
arr = np.dstack(3*[arr])
return arr*col1 + (1-arr)*col2
p1 = np.array(p1[::-1]).astype(float)
if vector is None and p2:
p2 = np.array(p2[::-1])
vector = p2-p1
else:
vector = np.array(vector[::-1])
p2 = p1 + vector
if vector:
norm = np.linalg.norm(vector)
M = np.dstack(np.meshgrid(range(w),range(h))[::-1]).astype(float)
if shape == 'linear':
n_vec = vector/norm**2 # norm 1/norm(vector)
p1 = p1 + offset*vector
arr = (M- p1).dot(n_vec)/(1-offset)
arr = np.minimum(1,np.maximum(0,arr))
if col1.size > 1:
arr = np.dstack(3*[arr])
return arr*col1 + (1-arr)*col2
elif shape == 'radial':
if r is None:
r = norm
if r == 0:
arr = np.ones((h,w))
else:
arr = (np.sqrt(((M - p1) ** 2).sum(axis=2))) - offset * r
arr = arr / ((1-offset)*r)
arr = np.minimum(1.0, np.maximum(0, arr))
if col1.size > 1:
arr = np.dstack(3*[arr])
return (1-arr)*col1 + arr*col2
[docs]def color_split(size,x=None,y=None,p1=None,p2=None,vector=None,
col1=0,col2=1.0, grad_width=0):
"""Make an image splitted in 2 colored regions.
Returns an array of size ``size`` divided in two regions called 1 and
2 in wht follows, and which will have colors col& and col2
respectively.
Parameters
-----------
x: (int)
If provided, the image is splitted horizontally in x, the left
region being region 1.
y: (int)
If provided, the image is splitted vertically in y, the top region
being region 1.
p1,p2:
Positions (x1,y1),(x2,y2) in pixels, where the numbers can be
floats. Region 1 is defined as the whole region on the left when
going from ``p1`` to ``p2``.
p1, vector:
``p1`` is (x1,y1) and vector (v1,v2), where the numbers can be
floats. Region 1 is then the region on the left when starting
in position ``p1`` and going in the direction given by ``vector``.
gradient_width
If not zero, the split is not sharp, but gradual over a region of
width ``gradient_width`` (in pixels). This is preferable in many
situations (for instance for antialiasing).
Examples
---------
>>> size = [200,200]
>>> # an image with all pixels with x<50 =0, the others =1
>>> color_split(size, x=50, col1=0, col2=1)
>>> # an image with all pixels with y<50 red, the others green
>>> color_split(size, x=50, col1=[255,0,0], col2=[0,255,0])
>>> # An image splitted along an arbitrary line (see below)
>>> color_split(size, p1=[20,50], p2=[25,70] col1=0, col2=1)
"""
if grad_width or ( (x is None) and (y is None)):
if p2 is not None:
vector = (np.array(p2) - np.array(p1))
elif x is not None:
vector = np.array([0,-1.0])
p1 = np.array([x, 0])
elif y is not None:
vector = np.array([1.0, 0.0])
p1 = np.array([0,y])
x,y = vector
vector = np.array([y,-x]).astype('float')
norm = np.linalg.norm(vector)
vector = max(0.1, grad_width) * vector / norm
return color_gradient(size,p1,vector=vector,
col1 = col1, col2 = col2, shape='linear')
else:
w, h = size
shape = (h, w) if np.isscalar(col1) else (h, w, len(col1))
arr = np.zeros(shape)
if x:
arr[:,:x] = col1
arr[:,x:] = col2
elif y:
arr[:y] = col1
arr[y:] = col2
return arr
# if we are here, it means we didn't exit with a proper 'return'
print( "Arguments in color_split not understood !" )
raise
[docs]def circle(screensize, center, radius, col1=1.0, col2=0, blur=1):
""" Draw an image with a circle.
Draws a circle of color ``col1``, on a background of color ``col2``,
on a screen of size ``screensize`` at the position ``center=(x,y)``,
with a radius ``radius`` but slightly blurred on the border by ``blur``
pixels
"""
offset = 1.0*(radius-blur)/radius if radius else 0
return color_gradient(screensize,p1=center,r=radius, col1=col1,
col2=col2, shape='radial', offset=offset)