Jason wrote:

My uber-abstraction is due to recently fleeing from C (abstraction

made hard) and Lisp (no workable GUI stuff) so I'm stuck in the

middle. Anyway, I appreciate the comments.

Not so fast. I think abstraction is *good* - only not well suited for the

inner loop of an image transformation algorithm.

Your warping seems to boil down to an affine transform, so whatever fancy

stuff you do as a preparation - with points, lines and all that nice

object-oriented stuff - you always end up with two equations

u = ax + by + c

v = dx + ey + f

and that's the only thing you should calculate repeatedly if you want

efficiency.

Using that recipe reduced calculation time for a small 350x223 pixel image

from 10 to 0.33 (0.2 with psyco) seconds. Here's the code, and I'm

confident you'll recognize it :-)

(no testing performed, but the images *do* look similar)

<warp.py>

""" call with

--psyco to use psyco

--old to use the original algorithm

an image file as the *last* parameter

"""

from Tkinter import *

import Image

import ImageTk

from sys import exit

from math import sqrt

if "--psyco" in sys.argv:

import psyco

psyco.full()

class Point:

# A Point in the plane

def __init__(self, int1, int2):

# Constructor

self.x = float(int1)

self.y = float(int2)

def __add__(self, other):

# Add two points

return Point(self.x + other.x, self.y + other.y)

def __sub__(self, other):

# Sub two points

return Point(self.x - other.x, self.y - other.y)

def __mul__(self, other):

# Either mult by a constant or dot product

if type(other) == float or type(other) == int:

return Point(self.x*other, self.y*other)

else:

return self.x*other.x + self.y*other.y

def __div__(self,other):

# division by a constant

if type(other) == float or type(other) == int:

return Point(self.x/other, self.y/other)

def __rmul__(self, other):

# multiplication by a constant

return Point(self.x*other, self.y*other)

def __rdiv__(self, other):

# division by a constant

return Point(other/self.x, other/self.y)

def __str__(self):

# printing represenation

return '(%s, %s)' % (self.x, self.y)

def length(self):

# regular length

return sqrt(pow(self.x, 2) + pow(self.y, 2))

def perpindicular(self):

# 90 deg rotation

return Point(self.y, -self.x)

def to_tuple(self):

# makes a tuple of ints

return (int(self.x), int(self.y))

class WarpLine:

# The lines used to warp the image

def __init__(self, x0, y0, x1, y1, id):

# Constructor - just two points - id not used yet.

self.id = 0

self.point1 = Point(x0, y0)

self.point2 = Point(x1, y1)

def __str__(self):

# Printing

return '%s->%s' % (self.point1, self.point2)

def length(self):

# Segment length

return sqrt(pow(self.point2.x-self.point1.x, 2) +

pow(self.point2.y-self.point1.y, 2))

def getUV(self, point):

# v = shortest distance of point to line

# u = the parameterization of the closest point from v

diff = (self.point2 - self.point1)

u = ((point - self.point1) * diff) / (diff * diff)

v = ((point - self.point1) * diff.perpindicular()) / sqrt(diff *

diff)

return u, v

def transformPoint(self, line, point):

# finds transform of point based on self and line

diff = (line.point2 - line.point1)

u, v = self.getUV(point)

return line.point1 + u * diff + (v * diff.perpindicular())

/sqrt(diff * diff)

class Picture:

# A simple image class

def __init__(self, file):

# Load up an image

self.data = Image.open(file)

def in_bounds(self, pt):

# is point in our bounds?

if pt.x < 0 or pt.y < 0 or pt.x > self.data.size[0] - 1 or pt.y >

self.data.size[1] - 1:

return 0

else:

return 1

def coefficients(self, transform=None):

orig = transform(Point(0, 0))

p = transform(Point(1, 0)) - orig

q = transform(Point(0, 1)) - orig

a, b, c = p.x, q.x, orig.x

d, e, f = p.y, q.y, orig.y

return a, b, c, d, e, f

def warp_new(self, source, line1, line2):

""" psyco doesn't like lambdas, so I had to factor it out.

Does anybody know why?

"""

self._warp(source,

*self.coefficients(lambda p: line1.transformPoint(line2, p)))

def _warp(self, source, a, b, c, d, e, f):

width, height = self.data.size

dest = [0] * (width*height)

src = source.data.getdata()

yoff = 0

for y in range(height):

for x in range(width):

u = int(a*x+b*y+c)

v = int(d*x+e*y+f)

if u >= 0 and u < width and v >= 0 and v < height:

dest[x + yoff] = src[u + v*width]

yoff += width

self.data.putdata(dest)

def warp_old(self, source, line1, line2):

# Do transformPoint on each pixel, save results

# This is the slow part of the program

dest = list(self.data.getdata())

src = source.data.getdata()

for x in range(0, self.data.size[0] - 1):

for y in range(0, self.data.size[1] - 1):

xy = line1.transformPoint(line2,Point(x,y)).to_tuple()

if self.in_bounds(Point(xy[0], xy[1])):

dest[x + y*self.data.size[0]] = src[xy[0] +

xy[1]*self.data.size[0]]

else:

dest[x + y*self.data.size[0]] = 0

self.data.putdata(dest)

def show(self):

# show the image

root = Tk()

canvas = Canvas(root,

width=self.data.size[0],height=self.data.size[1])

canvas.pack()

photo = ImageTk.PhotoImage(self.data)

disp = canvas.create_image(0, 0, anchor=NW, image=photo)

mainloop()

if __name__ == "__main__":

import time

p1 = Picture(sys.argv[-1])

line1 = WarpLine(0, 0, 200, 50, None)

line2 = WarpLine(-100, 0, 150, 0, None)

start = time.time()

if "--old" in sys.argv:

p1.warp_old(p1, line1, line2)

else:

p1.warp_new(p1, line1, line2)

print time.time() - start

p1.show()

</warp.py>

I'm sure there's room for improvement. E. g., you could devise a clipping

algorithm to not calculate all the black points. By the way, the Python

Imaging Library (PIL) has such a transform built in - but that might spoil

the fun.

Peter