Mercurial > ift6266

comparison transformations/local_elastic_distortions.py @ 52:c89defea1e65

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Modification aux déformations élastiques pour mettre en cache 50x10 champs de déformation, selon 10 niveaux de complexité
author fsavard
date Thu, 04 Feb 2010 16:53:50 -0500
parents a8ac3402eb45
children b3d76ebf2fac
comparison
equal deleted inserted replaced
51:81b9567ec4ae 52:c89defea1e65
21 import math 21 import math
22 import numpy 22 import numpy
23 import numpy.random 23 import numpy.random
24 import scipy.signal # convolve2d 24 import scipy.signal # convolve2d
25 25
26 _TEST_DIR = "/home/francois/Desktop/dist_tests/" 26 _TEST_DIR = "/u/savardf/ift6266/debug_images/"
27 27
28 def _raw_zeros(size): 28 def _raw_zeros(size):
29 return [[0 for i in range(size[1])] for j in range(size[0])] 29 return [[0 for i in range(size[1])] for j in range(size[0])]
30 30
31 class ElasticDistortionParams(): 31 class ElasticDistortionParams():
60 60
61 class LocalElasticDistorter(): 61 class LocalElasticDistorter():
62 def __init__(self, image_size=(32,32)): 62 def __init__(self, image_size=(32,32)):
63 self.image_size = image_size 63 self.image_size = image_size
64 64
65 self.current_complexity = 0.0 65 self.current_complexity_10 = 0
66 self.current_complexity = 0
66 67
67 # number of precomputed fields 68 # number of precomputed fields
68 # (principle: as complexity doesn't change often, we can 69 # (principle: as complexity doesn't change often, we can
69 # precompute a certain number of fields for a given complexity, 70 # precompute a certain number of fields for a given complexity,
70 # each with its own parameters. That way, we have good 71 # each with its own parameters. That way, we have good
71 # randomization, but we're much faster). 72 # randomization, but we're much faster).
72 self.to_precompute = 50 73 self.to_precompute_per_complexity = 50
73 74
74 # Both use ElasticDistortionParams 75 # Both use ElasticDistortionParams
75 self.current_params = None 76 self.current_params = None
76 self.precomputed_params = [] 77 self.precomputed_params = [[] for i in range(10)]
77 78
78 # 79 #
79 self.kernel_size = None 80 self.kernel_size = None
80 self.kernel = None 81 self.kernel = None
81 82
83 self.regenerate_parameters(0.0) 84 self.regenerate_parameters(0.0)
84 85
85 def get_settings_names(self): 86 def get_settings_names(self):
86 return ['alpha', 'sigma'] 87 return ['alpha', 'sigma']
87 88
89 def _floor_complexity(self, complexity):
90 return self._to_complexity_10(complexity) / 10.0
91
92 def _to_complexity_10(self, complexity):
93 return min(9, max(0, int(complexity * 10)))
94
88 def regenerate_parameters(self, complexity): 95 def regenerate_parameters(self, complexity):
89 if abs(complexity - self.current_complexity) > 1e-4: 96 complexity_10 = self._to_complexity_10(complexity)
90 self.current_complexity = complexity 97
91 98 if complexity_10 != self.current_complexity_10:
92 # complexity changed, fields must be regenerated 99 self.current_complexity_10 = complexity_10
93 self.precomputed_params = [] 100 self.current_complexity = self._floor_complexity(complexity)
94 101
95 if len(self.precomputed_params) <= self.to_precompute: 102 if len(self.precomputed_params[complexity_10]) <= self.to_precompute_per_complexity:
96 # not yet enough params generated, produce one more 103 # not yet enough params generated, produce one more
97 # and append to list 104 # and append to list
98 new_params = self._initialize_new_params() 105 new_params = self._initialize_new_params()
99 new_params = self._generate_fields(new_params) 106 new_params = self._generate_fields(new_params)
100 self.current_params = new_params 107 self.current_params = new_params
101 self.precomputed_params.append(new_params) 108 self.precomputed_params[complexity_10].append(new_params)
102 else: 109 else:
103 # if we have enough precomputed fields, just select one 110 # if we have enough precomputed fields, just select one
104 # at random and set parameters to match what they were 111 # at random and set parameters to match what they were
105 # when the field was generated 112 # when the field was generated
106 idx = numpy.random.randint(0, len(self.precomputed_params)) 113 idx = numpy.random.randint(0, len(self.precomputed_params[complexity_10]))
107 self.current_params = self.precomputed_params[idx] 114 self.current_params = self.precomputed_params[complexity_10][idx]
108 115
109 # don't return anything, to avoid storing deterministic parameters 116 # don't return anything, to avoid storing deterministic parameters
110 return [] # self.current_params.alpha_sigma() 117 return [] # self.current_params.alpha_sigma()
111 118
112 def get_parameters_determined_by_complexity(self, complexity): 119 def get_parameters_determined_by_complexity(self, complexity):
113 tmp_params = self._initialize_new_params(complexity) 120 tmp_params = self._initialize_new_params(_floor_complexity(complexity))
114 return tmp_params.alpha_sigma() 121 return tmp_params.alpha_sigma()
115 122
116 # adapted from http://blenderartists.org/forum/showthread.php?t=163361 123 # adapted from http://blenderartists.org/forum/showthread.php?t=163361
117 def _gen_gaussian_kernel(self, sigma): 124 def _gen_gaussian_kernel(self, sigma):
118 # the kernel size can change DRAMATICALLY the time 125 # the kernel size can change DRAMATICALLY the time
345 def _complexity_benchmark(): 352 def _complexity_benchmark():
346 imgpath = os.path.join(_TEST_DIR, "d.png") 353 imgpath = os.path.join(_TEST_DIR, "d.png")
347 dist = LocalElasticDistorter((32,32)) 354 dist = LocalElasticDistorter((32,32))
348 orig_img = _load_image(imgpath) 355 orig_img = _load_image(imgpath)
349 356
350 # time the first 10 357 for cpx in (0.21, 0.35):
351 t1 = time.time() 358 # time the first 10
352 for i in range(10): 359 t1 = time.time()
353 dist.regenerate_parameters(0.2) 360 for i in range(10):
354 img = dist.transform_image(orig_img) 361 dist.regenerate_parameters(cpx)
355 t2 = time.time() 362 img = dist.transform_image(orig_img)
356 363 t2 = time.time()
357 print "first 10, total = ", t2-t1, ", avg=", (t2-t1)/10 364
358 365 print "first 10, total = ", t2-t1, ", avg=", (t2-t1)/10
359 # time the next 40 366
360 t1 = time.time() 367 # time the next 40
361 for i in range(40): 368 t1 = time.time()
362 dist.regenerate_parameters(0.2) 369 for i in range(40):
363 img = dist.transform_image(orig_img) 370 dist.regenerate_parameters(cpx)
364 t2 = time.time() 371 img = dist.transform_image(orig_img)
365 372 t2 = time.time()
366 print "next 40, total = ", t2-t1, ", avg=", (t2-t1)/40 373
367 374 print "next 40, total = ", t2-t1, ", avg=", (t2-t1)/40
368 # time the next 50 375
369 t1 = time.time() 376 # time the next 50
370 for i in range(50): 377 t1 = time.time()
371 dist.regenerate_parameters(0.2) 378 for i in range(50):
372 img = dist.transform_image(orig_img) 379 dist.regenerate_parameters(cpx)
373 t2 = time.time() 380 img = dist.transform_image(orig_img)
374 381 t2 = time.time()
375 print "next 50, total = ", t2-t1, ", avg=", (t2-t1)/50 382
376 383 print "next 50, total = ", t2-t1, ", avg=", (t2-t1)/50
377 # time the next 1000 384
385 # time the next 1000
386 t1 = time.time()
387 for i in range(1000):
388 dist.regenerate_parameters(cpx)
389 img = dist.transform_image(orig_img)
390 t2 = time.time()
391
392 print "next 1000, total = ", t2-t1, ", avg=", (t2-t1)/1000
393
394 # time the next 1000 with old complexity
378 t1 = time.time() 395 t1 = time.time()
379 for i in range(1000): 396 for i in range(1000):
380 dist.regenerate_parameters(0.2) 397 dist.regenerate_parameters(0.21)
381 img = dist.transform_image(orig_img) 398 img = dist.transform_image(orig_img)
382 t2 = time.time() 399 t2 = time.time()
383 400
384 print "next 1000, total = ", t2-t1, ", avg=", (t2-t1)/1000 401 print "next 1000, total = ", t2-t1, ", avg=", (t2-t1)/1000
402
385 403
386 404
387 405
388 def _save_image(img, path): 406 def _save_image(img, path):
389 img2 = Image.fromarray((img * 255).astype('uint8'), "L") 407 img2 = Image.fromarray((img * 255).astype('uint8'), "L")
425 import pylab 443 import pylab
426 import Image 444 import Image
427 import os.path 445 import os.path
428 #_distorter_tests() 446 #_distorter_tests()
429 #_benchmark() 447 #_benchmark()
430 _specific_test() 448 #_specific_test()
431 #_complexity_tests() 449 #_complexity_tests()
432 #_complexity_benchmark() 450 _complexity_benchmark()
433 451
434 452
435 453