error surface -- multiprocessing

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index 51b0266..35bbc80 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -53,75 +53,45 @@ class V(object):
 
 def projection(point, line, vector):
     return V(*intersection(g_line(point, point + vector.normal), g_line(*line)))
+
+def job(args):
+    X, Y, im_l, a, b, c, d, s, v1, k, hough, size = args
+    return [distance(im_l, 
+                     get_grid(a + X[y] * s * v1, 
+                              b + Y[y] * s * v1, 
+                              c, d, hough, size),
+                     size) for y in range(0,2 * k)]
     
-def error_surface(lines, a, b, c, d, hough, size, v1):
+def error_surface(im_l, a, b, c, d, hough, size, v1):
     import matplotlib.pyplot as plt
     from matplotlib import cm
-    import threading
-    import Queue
+    import multiprocessing
     import time
     import sys
     import pickle
 
-    class Worker(threading.Thread):
-        def __init__(self, q_in, q_out, job):
-            threading.Thread.__init__(self)
-            self.q_in = q_in
-            self.q_out = q_out
-            self.job = job
-
-        def run(self):
-            while True:
-                x = self.q_in.get()
-                try:
-                    self.q_out.put((x, self.job(x)))
-                except Exception:
-                    pass
-                print x
-                self.q_in.task_done()
-
     X = []
     Y = []
     Z = []
     s = 0.001
-    k = 200
+    k = 250
     for i in range(-k, k):
         X.append(range(-k, k))
         Y.append(2*k*[i])
 
-    job = lambda x: [distance(lines, get_grid(a + X[x][y] * s * v1, 
-                                              b + Y[x][y] * s * v1, 
-                                              c, d, hough, size),
-                                size) for y in range(0,2 * k)]
-
-    q_in = Queue.Queue()
-    q_out = Queue.Queue()
-    for i in range(4):
-        t = Worker(q_in, q_out, job)
-        t.daemon = True
-        t.start()
-        
-    start = time.time()
-    for x in range(0, 2*k):
-        q_in.put(x)
-
-    q_in.join()
+    tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, k, hough, size) for x in xrange(0, 2 * k)]
+    #everything is passed by value here; can it somehow be passed by reference?
 
+    pool = multiprocessing.Pool(None)
+    
+    start = time.time()
+    Z = pool.map(job, tasks, 1)
     print time.time() - start
 
-    while True:
-        try:
-            Z.append(q_out.get_nowait())
-        except Queue.Empty:
-            break
-
-    Z.sort()
-    Z = [t for (x, t) in Z]
-
     s_file = open('surface' + str(k), 'w')
     pickle.dump((X, Y, Z), s_file)
     s_file.close()
-    plt.imshow(Z, cmap=cm.gnuplot2, interpolation='bicubic', 
+    plt.imshow(Z, cmap=cm.jet, interpolation='bicubic', 
                origin='upper', extent=(-k, k, -k, k), aspect='equal')
     plt.colorbar()
 
@@ -140,10 +110,18 @@ def find(lines, size, l1, l2, bounds, hough, do_something):
     c = projection(c, l2, v2) 
     d = projection(d, l2, v2) 
 
-    #error_surface(lines, a, b, c, d, hough, size, v1)
+    im_l = Image.new('L', size)
+    dr_l = ImageDraw.Draw(im_l)
+    for line in sum(lines, []):
+        dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
+    im_l = im_l.filter(MyGaussianBlur(radius=50))
+    #GaussianBlur is undocumented class, may not work in future versions of PIL
+    im_l = im_l.tostring()
+
+    #error_surface(im_l, a, b, c, d, hough, size, v1)
 
     grid = get_grid(a, b, c, d, hough, size)
-    dist = distance(lines, grid, size)
+    dist = distance(im_l, grid, size)
     print dist
    
     s = 0.02
@@ -151,7 +129,7 @@ def find(lines, size, l1, l2, bounds, hough, do_something):
         ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
         grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, 
                            c, d, hough, size), t) for t in ts1]
-        distances = [(distance(lines, grid, size), 
+        distances = [(distance(im_l, grid, size), 
                       grid, t) for grid, t in grids]
         distances.sort(reverse=True)
         if distances[0][0] > dist:
@@ -171,7 +149,7 @@ def find(lines, size, l1, l2, bounds, hough, do_something):
         ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
         grids = [(get_grid(a, b, 
                            c + t[0] * v2, d + t[1] * v2, hough, size), t) for t in ts1]
-        distances = [(distance(lines, grid, size), 
+        distances = [(distance(im_l, grid, size), 
                       grid, t) for grid, t in grids]
         distances.sort(reverse=True)
         if distances[0][0] > dist:
@@ -199,20 +177,15 @@ def get_grid(a, b, c, d, hough, size):
     grid = g_grid(corners)
     return grid
 
-def distance(lines, grid, size):
-    im_l = Image.new('L', size)
-    dr_l = ImageDraw.Draw(im_l)
-    for line in sum(lines, []):
-        dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
-    im_l = im_l.filter(MyGaussianBlur(radius=15))
-    #GaussianBlur is undocumented class, may not work in future versions of PIL
+def distance(im_l, grid, size):
     im_g = Image.new('L', size)
     dr_g = ImageDraw.Draw(im_g)
     for line in grid[0] + grid[1]:
         dr_g.line(line, width=1, fill=255)
     #im_g = im_g.filter(MyGaussianBlur(radius=3))
+    #GaussianBlur is undocumented class, may not work in future versions of PIL
     #im_d, distance = combine(im_l, im_g)
-    distance = pcf.combine(im_l.tostring(), im_g.tostring())
+    distance = pcf.combine(im_l, im_g.tostring())
     return distance
 
 def combine(bg, fg):