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comparison transformations/pipeline.py @ 109:9c45e0071b52

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Adapté le générateur d'images de fontes pour utiliser en amont du pipeline
author boulanni <nicolas_boulanger@hotmail.com>
date Tue, 16 Feb 2010 13:10:06 -0500
parents a7cd8dd3221c
children b84a0d009af8
comparison
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108:a7cd8dd3221c 109:9c45e0071b52
52 from local_elastic_distortions import LocalElasticDistorter 52 from local_elastic_distortions import LocalElasticDistorter
53 from slant import Slant 53 from slant import Slant
54 from Occlusion import Occlusion 54 from Occlusion import Occlusion
55 from add_background_image import AddBackground 55 from add_background_image import AddBackground
56 from affine_transform import AffineTransformation 56 from affine_transform import AffineTransformation
57 from ttf2jpg import ttf2jpg
57 58
58 if DEBUG: 59 if DEBUG:
59 from visualizer import Visualizer 60 from visualizer import Visualizer
60 # Either put the visualizer as in the MODULES_INSTANCES list 61 # Either put the visualizer as in the MODULES_INSTANCES list
61 # after each module you want to visualize, or in the 62 # after each module you want to visualize, or in the
209 img = ft.read(nist.train_data) 210 img = ft.read(nist.train_data)
210 labels = ft.read(nist.train_labels) 211 labels = ft.read(nist.train_labels)
211 if prob_ocr: 212 if prob_ocr:
212 ocr_img = ft.read(nist.ocr_data) 213 ocr_img = ft.read(nist.ocr_data)
213 ocr_labels = ft.read(nist.ocr_labels) 214 ocr_labels = ft.read(nist.ocr_labels)
215 ttf = ttf2jpg()
214 216
215 for i in xrange(num_img): 217 for i in xrange(num_img):
216 r = numpy.random.rand() 218 r = numpy.random.rand()
217 if r <= prob_font: 219 if r <= prob_font:
218 pass #get font 220 yield ttf.generate_image()
219 elif r <= prob_font + prob_captcha: 221 elif r <= prob_font + prob_captcha:
220 pass #get captcha 222 pass #get captcha
221 elif r <= prob_font + prob_captcha + prob_ocr: 223 elif r <= prob_font + prob_captcha + prob_ocr:
222 j = numpy.random.randint(len(ocr_labels)) 224 j = numpy.random.randint(len(ocr_labels))
223 yield ocr_img[j].astype(numpy.float32)/255, ocr_labels[j] 225 yield ocr_img[j].astype(numpy.float32)/255, ocr_labels[j]