Python numpy.uint8方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def test_bitmap_mask_resize():
    # resize with empty bitmap masks
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    resized_masks = bitmap_masks.resize((56, 72))
    assert len(resized_masks) == 0
    assert resized_masks.height == 56
    assert resized_masks.width == 72

    # resize with bitmap masks contain 1 instances
    raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...]
    bitmap_masks = BitmapMasks(raw_masks, 4, 4)
    resized_masks = bitmap_masks.resize((8, 8))
    assert len(resized_masks) == 1
    assert resized_masks.height == 8
    assert resized_masks.width == 8
    truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
                       [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0],
                       [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
                       [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]])
    assert (resized_masks.masks == truth).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def show():
    """Output the contents of the buffer to Unicorn HAT HD."""
    setup()
    if _addressing_enabled:
        for address in range(8):
            display = _displays[address]
            if display.enabled:
                if _buffer_width == _buffer_height or _rotation in [0, 2]:
                    window = display.get_buffer_window(numpy.rot90(_buf, _rotation))
                else:
                    window = display.get_buffer_window(numpy.rot90(_buf, _rotation))

                _spi.xfer2([_SOF + 1 + address] + (window.reshape(768) * _brightness).astype(numpy.uint8).tolist())
                time.sleep(_DELAY)
    else:
        _spi.xfer2([_SOF] + (numpy.rot90(_buf, _rotation).reshape(768) * _brightness).astype(numpy.uint8).tolist())

    time.sleep(_DELAY) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def draw_heatmap(img, heatmap, alpha=0.5):
    """Draw a heatmap overlay over an image."""
    assert len(heatmap.shape) == 2 or \
        (len(heatmap.shape) == 3 and heatmap.shape[2] == 1)
    assert img.dtype in [np.uint8, np.int32, np.int64]
    assert heatmap.dtype in [np.float32, np.float64]

    if img.shape[0:2] != heatmap.shape[0:2]:
        heatmap_rs = np.clip(heatmap * 255, 0, 255).astype(np.uint8)
        heatmap_rs = ia.imresize_single_image(
            heatmap_rs[..., np.newaxis],
            img.shape[0:2],
            interpolation="nearest"
        )
        heatmap = np.squeeze(heatmap_rs) / 255.0

    cmap = plt.get_cmap('jet')
    heatmap_cmapped = cmap(heatmap)
    heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
    heatmap_cmapped = heatmap_cmapped * 255
    mix = (1-alpha) * img + alpha * heatmap_cmapped
    mix = np.clip(mix, 0, 255).astype(np.uint8)
    return mix 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def wer(self, r, h):
        # initialisation
        d = np.zeros((len(r)+1)*(len(h)+1), dtype=np.uint8)
        d = d.reshape((len(r)+1, len(h)+1))
        for i in range(len(r)+1):
            for j in range(len(h)+1):
                if i == 0:
                    d[0][j] = j
                elif j == 0:
                    d[i][0] = i

        # computation
        for i in range(1, len(r)+1):
            for j in range(1, len(h)+1):
                if r[i-1] == h[j-1]:
                    d[i][j] = d[i-1][j-1]
                else:
                    substitution = d[i-1][j-1] + 1
                    insertion    = d[i][j-1] + 1
                    deletion     = d[i-1][j] + 1
                    d[i][j] = min(substitution, insertion, deletion)

        return d[len(r)][len(h)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def draw_bounding_boxes(image, gt_boxes, im_info):
  num_boxes = gt_boxes.shape[0]
  gt_boxes_new = gt_boxes.copy()
  gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2])
  disp_image = Image.fromarray(np.uint8(image[0]))

  for i in range(num_boxes):
    this_class = int(gt_boxes_new[i, 4])
    disp_image = _draw_single_box(disp_image, 
                                gt_boxes_new[i, 0],
                                gt_boxes_new[i, 1],
                                gt_boxes_new[i, 2],
                                gt_boxes_new[i, 3],
                                'N%02d-C%02d' % (i, this_class),
                                FONT,
                                color=STANDARD_COLORS[this_class % NUM_COLORS])

  image[0, :] = np.array(disp_image)
  return image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def z_color(self, z):
        z = float(z)
        alpha = 1.0

        if z <= self.mid_z:
            ns = lerp(z, self.near_z, self.mid_z)
            color = (1.0 - ns) * self.near_color + ns * self.mid_color
        else:  # z must be between self.mid_z and FAR_Z
            fs = lerp(z, self.mid_z, self.far_z)
            color = (1.0 - fs) * self.mid_color + fs * self.far_color

        alpha = 1.0 - lerp(z, self.min_z, self.max_z)

        if z <= -self.min_z:
            alpha = 0.0

        # gl_FragColor = vec4(color, alpha) * texture2D( texture, gl_PointCoord )

        return (color * alpha).astype(np.uint8).tolist() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def add_image(self, img):
        if self.print_progress and self.cur_images % self.progress_interval == 0:
            print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
            sys.stdout.flush()
        if self.shape is None:
            self.shape = img.shape
            self.resolution_log2 = int(np.log2(self.shape[1]))
            assert self.shape[0] in [1, 3]
            assert self.shape[1] == self.shape[2]
            assert self.shape[1] == 2**self.resolution_log2
            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
            for lod in range(self.resolution_log2 - 1):
                tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
        assert img.shape == self.shape
        for lod, tfr_writer in enumerate(self.tfr_writers):
            if lod:
                img = img.astype(np.float32)
                img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
            quant = np.rint(img).clip(0, 255).astype(np.uint8)
            ex = tf.train.Example(features=tf.train.Features(feature={
                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
            tfr_writer.write(ex.SerializeToString())
        self.cur_images += 1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def create_mnist(tfrecord_dir, mnist_dir):
    print('Loading MNIST from "%s"' % mnist_dir)
    import gzip
    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
        images = np.frombuffer(file.read(), np.uint8, offset=16)
    with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
        labels = np.frombuffer(file.read(), np.uint8, offset=8)
    images = images.reshape(-1, 1, 28, 28)
    images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
    assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (60000,) and labels.dtype == np.uint8
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 9
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0
    
    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

#---------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
    print('Loading MNIST from "%s"' % mnist_dir)
    import gzip
    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
        images = np.frombuffer(file.read(), np.uint8, offset=16)
    images = images.reshape(-1, 28, 28)
    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
    assert np.min(images) == 0 and np.max(images) == 255
    
    with TFRecordExporter(tfrecord_dir, num_images) as tfr:
        rnd = np.random.RandomState(random_seed)
        for idx in range(num_images):
            tfr.add_image(images[rnd.randint(images.shape[0], size=3)])

#---------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def create_cifar100(tfrecord_dir, cifar100_dir):
    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
    import pickle
    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
        data = pickle.load(file, encoding='latin1')
    images = data['data'].reshape(-1, 3, 32, 32)
    labels = np.array(data['fine_labels'])
    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (50000,) and labels.dtype == np.int32
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 99
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0

    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

#---------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
    network_pkl = misc.locate_network_pkl(run_id, snapshot)
    if png_prefix is None:
        png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
    random_state = np.random.RandomState(random_seed)

    print('Loading network from "%s"...' % network_pkl)
    G, D, Gs = misc.load_network_pkl(run_id, snapshot)

    result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
    for png_idx in range(num_pngs):
        print('Generating png %d / %d...' % (png_idx, num_pngs))
        latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
        labels = np.zeros([latents.shape[0], 0], np.float32)
        images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
        misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
    open(os.path.join(result_subdir, '_done.txt'), 'wt').close()

#----------------------------------------------------------------------------
# Generate MP4 video of random interpolations using a previously trained network.
# To run, uncomment the appropriate line in config.py and launch train.py. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
        self.resolution         = resolution
        self.resolution_log2    = int(np.log2(resolution))
        self.shape              = [num_channels, resolution, resolution]
        self.dtype              = dtype
        self.dynamic_range      = dynamic_range
        self.label_size         = label_size
        self.label_dtype        = label_dtype
        self._tf_minibatch_var  = None
        self._tf_lod_var        = None
        self._tf_minibatch_np   = None
        self._tf_labels_np      = None

        assert self.resolution == 2 ** self.resolution_log2
        with tf.name_scope('Dataset'):
            self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
            self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def __init__(self, dataset, oversample_thr):
        self.dataset = dataset
        self.oversample_thr = oversample_thr
        self.CLASSES = dataset.CLASSES

        repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
        repeat_indices = []
        for dataset_index, repeat_factor in enumerate(repeat_factors):
            repeat_indices.extend([dataset_index] * math.ceil(repeat_factor))
        self.repeat_indices = repeat_indices

        flags = []
        if hasattr(self.dataset, 'flag'):
            for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
                flags.extend([flag] * int(math.ceil(repeat_factor)))
            assert len(flags) == len(repeat_indices)
        self.flag = np.asarray(flags, dtype=np.uint8) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def __call__(self, results):
        """Call function to corrupt image.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Result dict with images corrupted.
        """

        if corrupt is None:
            raise RuntimeError('imagecorruptions is not installed')
        if 'img_fields' in results:
            assert results['img_fields'] == ['img'], \
                'Only single img_fields is allowed'
        results['img'] = corrupt(
            results['img'].astype(np.uint8),
            corruption_name=self.corruption,
            severity=self.severity)
        return results 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint8 [as 別名]
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    """Convert tensor to images.

    Args:
        tensor (torch.Tensor): Tensor that contains multiple images
        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
        std (tuple[float], optional): Standard deviation of images.
            Defaults to (1, 1, 1).
        to_rgb (bool, optional): Whether convert the images to RGB format.
            Defaults to True.

    Returns:
        list[np.ndarray]: A list that contains multiple images.
    """
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs