def setUp(self): reset_plugins() # Generic image collection with images of different shapes. self.images = ImageCollection(self.pattern) # Image collection with images having shapes that match. self.images_matched = ImageCollection(self.pattern_matched) # Same images as a collection of frames self.frames_matched = MultiImage(self.pattern_matched)
def setUp(self): # This multipage TIF file was created with imagemagick: # convert im1.tif im2.tif -adjoin multipage.tif use_plugin('pil') paths = [os.path.join(data_dir, 'multipage_rgb.tif'), os.path.join(data_dir, 'no_time_for_that_tiny.gif')] self.imgs = [MultiImage(paths[0]), MultiImage(paths[0], conserve_memory=False), MultiImage(paths[1]), MultiImage(paths[1], conserve_memory=False), ImageCollection(paths[0]), ImageCollection(paths[1], conserve_memory=False), ImageCollection(os.pathsep.join(paths))]
def setUp(self): # This multipage TIF file was created with imagemagick: # convert im1.tif im2.tif -adjoin multipage.tif paths = [ os.path.join(data_dir, 'multipage.tif'), os.path.join(data_dir, 'no_time_for_that.gif') ] self.imgs = [ MultiImage(paths[0]), MultiImage(paths[0], conserve_memory=False), MultiImage(paths[1]), MultiImage(paths[1], conserve_memory=False), ImageCollection(paths[0]), ImageCollection(paths[1], conserve_memory=False), ImageCollection('%s:%s' % (paths[0], paths[1])) ]
def test_custom_load_func(self): def load_fn(x): return x ic = ImageCollection(os.pathsep.join(self.pattern), load_func=load_fn) assert_equal(ic[0], self.pattern[0])
def load_from_dir(directory, extension): """ load_from_dir: Takes in the directory path and extension of images to be read, and reads them in. Args: (String) directroy: path to directory (String) extension: extension of the files to be read Returns: (Numpy Array)data: The normal data w the 3 channels (Numpy Array) data_gray: the data converted to gray scale format """ data = np.array( ImageCollection('{a}/*.{b}'.format(a=directory, b=extension), load_func=imread)) data_gray = np.array( ImageCollection('{a}/*.{b}'.format(a=directory, b=extension), load_func=imread_grayscale)) return (data, data_gray)
def test_custom_load(self): load_pattern = [(1, 'one'), (2, 'two')] def load_fn(x): return x ic = ImageCollection(load_pattern, load_func=load_fn) assert_equal(ic[1], (2, 'two'))
def imgs(): use_plugin('pil') paths = [ os.path.join(data_dir, 'multipage_rgb.tif'), os.path.join(data_dir, 'no_time_for_that_tiny.gif') ] imgs = [ MultiImage(paths[0]), MultiImage(paths[0], conserve_memory=False), MultiImage(paths[1]), MultiImage(paths[1], conserve_memory=False), ImageCollection(paths[0]), ImageCollection(paths[1], conserve_memory=False), ImageCollection(os.pathsep.join(paths)) ] yield imgs reset_plugins()
def test_custom_load_func_sequence(self): filename = fetch('data/no_time_for_that_tiny.gif') def reader(frameno): vid = imageio.get_reader(filename) return vid.get_data(frameno) ic = ImageCollection(range(24), load_func=reader) # the length of ic should be that of the given load_pattern sequence assert len(ic) == 24 # GIF file has frames of size 25x14 with 4 channels (RGBA) assert ic[0].shape == (25, 14, 4)
def test_custom_load_func_w_kwarg(self): load_pattern = os.path.join(data_dir, 'no_time_for_that_tiny.gif') def load_fn(f, step): vid = imageio.get_reader(f) seq = [v for v in vid.iter_data()] return seq[::step] ic = ImageCollection(load_pattern, load_func=load_fn, step=3) # Each file should map to one image (array). assert len(ic) == 1 # GIF file has 24 frames, so 24 / 3 equals 8. assert len(ic[0]) == 8
def test_imagecollection_input(): """Test function for ImageCollection. The new behavior (implemented in 0.16) allows the `pattern` argument to accept a list of strings as the input. Notes ----- If correct, `images` will receive three images. """ pattern = [ os.path.join(data_dir, pic) for pic in ['palette_gray.png', 'chessboard_GRAY_U16.tif', 'rocket.jpg'] ] images = ImageCollection(pattern) assert len(images) == 3
def test_imagecollection_input(): """Test function for ImageCollection. The new behavior (implemented in 0.16) allows the `pattern` argument to accept a list of strings as the input. Notes ----- If correct, `images` will receive three images. """ # Ensure that these images are part of the legacy datasets # this means they will always be available in the user's install # regarless of the availability of pooch pattern = [ os.path.join(data_dir, pic) for pic in ['coffee.png', 'chessboard_GRAY.png', 'rocket.jpg'] ] images = ImageCollection(pattern) assert len(images) == 3
def get_sample_of_class(classs, labels, input_path, num=-1): """ Returns images sampled from files by class """ existing_files = pd.DataFrame( [path.splitext(f)[0] for f in os.listdir(input_path)], columns=[labels.columns[0]]) class_labels = pd.DataFrame(labels[labels['level'] == classs]['image'], columns=[labels.columns[0]]) class_labels = class_labels.merge(existing_files, on=class_labels.columns[0]) if num == -1: num = class_labels.count() files = [ path.join(raw_train, c + ".jpeg") for c in class_labels[:num][class_labels.columns[0]] ] return ImageCollection(files, conserve_memory=True)
def setUp(self): # Generic image collection with images of different shapes. self.images = ImageCollection(self.pattern) # Image collection with images having shapes that match. self.images_matched = ImageCollection(self.pattern_matched)
def get_images(path): pattern = os.path.abspath(path) + os.sep + '*.jpg' return ImageCollection(pattern, load_func=imread_convert)