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