def main():
# Parameters
images_directory = './data/images'
categories_file = '../categories.json'
if not os.path.isdir(images_directory):
loader = ImageLoader(output_directory=images_directory,
categories_file=categories_file)
loader.load()
experience_file = './data/experience-new-ratings.csv'
number_of_experiences, ids_for_categories = load_experience_data(
experience_file, n_ratings=2)
number_of_true_images_for_provider = 8
number_of_noise_images_for_provider = 2
number_of_images_in_collage = 6
output_directory = './data/generated-data'
features_original_images_file = './data/images/features-images-1'
image_generator = ImageGenerator(images_directory, categories_file,
features_original_images_file)
image_generator.generate(
number_of_true_images_for_provider=number_of_true_images_for_provider,
number_of_noise_images_for_provider=number_of_noise_images_for_provider,
number_of_images=number_of_experiences,
ids=ids_for_categories,
number_of_images_in_collage=number_of_images_in_collage,
output_directory=output_directory)
evaluator = Evaluator(output_directory,
features_file='./data/features-generated-data')
evaluator.visualize(show='ratings')
evaluator.classify()
def main():
needs_convert = not os.path.exists('data_raw')
loader = ImageLoader('data_raw', 'dat', dtype=np.uint8)
if needs_convert:
loader.convert_to_raw('data_jpg')
loader.load(0.1)
model = ImageResUp('resup', loader)
model.create()
model.compile()
model.train(10, 80, 10)
model.generate(16)
def get_test_data_set():
"""
Get the test data set.
"""
image_loader = ImageLoader('data/t10k-images.idx3-ubyte', 10000)
label_loader = LabelLoader('data/t10k-labels.idx1-ubyte', 10000)
return image_loader.load(), label_loader.load()
def get_training_data_set():
"""
Get the train data set.
"""
image_loader = ImageLoader('data/train-images.idx3-ubyte', 60000)
label_loader = LabelLoader('data/train-labels.idx1-ubyte', 60000)
return image_loader.load(), label_loader.load()
class OpenFileDialog(QDialog):
def __init__(self, filename, parent = None):
QDialog.__init__(self, parent)
self.ui = Ui_Dialog()
self.ui.setupUi(self)
self.ui.thresholdSlider.valueChanged.connect(self.redraw)
self.ui.invertedCheckBox.toggled.connect(self.redraw)
self.ui.progress.setEnabled(False)
fileExt = filename.split('.')[-1]
if fileExt == 'gbr':
self.ui.imageBox.setVisible(False)
self._loader = GerberLoader(self)
elif fileExt in ['png', 'bmp', 'jpg']:
self.ui.gerberBox.setVisible(False)
self._loader = ImageLoader(self)
if not self._loader.load(filename):
self.close() #FIXME
self._loader.progress.connect(self.ui.progress.setValue)
self._loader.loaded.connect(self._loaded)
if fileExt == 'gbr':
self.redraw()
else:
self.ui.thresholdSlider.setValue(127)
def redraw(self):
self._loader.stop()
self.ui.progress.setValue(0)
self.ui.progress.setEnabled(True)
self.t = time()
if type(self._loader) == GerberLoader:
self._loader.run(0.05)
else:
threshold = self.ui.thresholdSlider.value()
inverted = self.ui.invertedCheckBox.isChecked()
self._loader.run((threshold, inverted))
def _loaded(self, image):
print time() - self.t
self.ui.progress.setValue(0)
self.ui.progress.setEnabled(False)
self._image = image
self.ui.view.setFixedSize(image.size())
self.ui.view.setPixmap(QPixmap.fromImage(image))
def image(self):
return self._image
def closeEvent(self, event): #FIXME
print 'qqqq'
from image_loader import ImageLoader
import sys
import json
import time
from cvtools import cv_load_image
with open(sys.argv[1], 'r') as f:
d = json.load(f)
imloader = ImageLoader('opencv', 'bgr')
st = time.time()
for ix, i in enumerate(d['images']):
img = imloader.load(i['file_name'])
sys.stdout.write("{}\r".format((time.time() - st) / (ix + 1)))
sys.stdout.flush()
def load_image(self, item):
try:
loader = ImageLoader(self.gui, item['thumbnailImg'])
loader.load()
except Exception as ex:
print ex
