Python Image.fromarray方法代碼示例

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [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: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def _prepare_sample_data(self, submission_type):
    """Prepares sample data for the submission.

    Args:
      submission_type: type of the submission.
    """
    # write images
    images = np.random.randint(0, 256,
                               size=[BATCH_SIZE, 299, 299, 3], dtype=np.uint8)
    for i in range(BATCH_SIZE):
      Image.fromarray(images[i, :, :, :]).save(
          os.path.join(self._sample_input_dir, IMAGE_NAME_PATTERN.format(i)))
    # write target class for targeted attacks
    if submission_type == 'targeted_attack':
      target_classes = np.random.randint(1, 1001, size=[BATCH_SIZE])
      target_class_filename = os.path.join(self._sample_input_dir,
                                           'target_class.csv')
      with open(target_class_filename, 'w') as f:
        for i in range(BATCH_SIZE):
          f.write((IMAGE_NAME_PATTERN + ',{1}\n').format(i, target_classes[i])) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def draw_keypoints_on_image_array(image,
                                  keypoints,
                                  color='red',
                                  radius=2,
                                  use_normalized_coordinates=True):
  """Draws keypoints on an image (numpy array).

  Args:
    image: a numpy array with shape [height, width, 3].
    keypoints: a numpy array with shape [num_keypoints, 2].
    color: color to draw the keypoints with. Default is red.
    radius: keypoint radius. Default value is 2.
    use_normalized_coordinates: if True (default), treat keypoint values as
      relative to the image.  Otherwise treat them as absolute.
  """
  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
  draw_keypoints_on_image(image_pil, keypoints, color, radius,
                          use_normalized_coordinates)
  np.copyto(image, np.array(image_pil)) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def __call__(self, video):
    """
    Args:
        img (numpy array): Input image, shape (... x H x W x C), dtype uint8.
    Returns:
        PIL Image: Color jittered image.
    """
    transforms = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
    reshaped_video = video.reshape((-1, *video.shape[-3:]))
    n_channels = video.shape[-1]
    for i in range(reshaped_video.shape[0]):
      img = reshaped_video[i]
      if n_channels == 1:
        img = img.squeeze(axis=2)
      img = Image.fromarray(img)
      for t in transforms:
        img = t(img)
      img = np.array(img)
      if n_channels == 1:
        img = img[..., np.newaxis]
      reshaped_video[i] = img
    video = reshaped_video.reshape(video.shape)
    return video 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def save_images(images, filenames, output_dir):
  """Saves images to the output directory.

  Args:
    images: array with minibatch of images
    filenames: list of filenames without path
      If number of file names in this list less than number of images in
      the minibatch then only first len(filenames) images will be saved.
    output_dir: directory where to save images
  """
  for i, filename in enumerate(filenames):
    # Images for inception classifier are normalized to be in [-1, 1] interval,
    # so rescale them back to [0, 1].
    with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:
      img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)
      Image.fromarray(img).save(f, format='PNG') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def Chainer2PIL(data, rescale=True):
    data = np.array(data)
    if rescale:
        data *= 256
        # data += 128
    if data.dtype != np.uint8:
        data = np.clip(data, 0, 255)
        data = data.astype(np.uint8)
    if data.shape[0] == 1:
        buf = data.astype(np.uint8).reshape((data.shape[1], data.shape[2]))
    else:
        buf = np.zeros((data.shape[1], data.shape[2], data.shape[0]), dtype=np.uint8)
        for i in range(3):
            a = data[i,:,:]
            buf[:,:,i] = a
    img = Image.fromarray(buf)
    return img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def main():
    """Module main execution"""
    # Initialization variables - update to change your model and execution context
    model_prefix = "FCN8s_VGG16"
    epoch = 19

    # By default, MXNet will run on the CPU. Change to ctx = mx.gpu() to run on GPU.
    ctx = mx.cpu()

    fcnxs, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(model_prefix, epoch)
    fcnxs_args["data"] = mx.nd.array(get_data(args.input), ctx)
    data_shape = fcnxs_args["data"].shape
    label_shape = (1, data_shape[2]*data_shape[3])
    fcnxs_args["softmax_label"] = mx.nd.empty(label_shape, ctx)
    exector = fcnxs.bind(ctx, fcnxs_args, args_grad=None, grad_req="null", aux_states=fcnxs_args)
    exector.forward(is_train=False)
    output = exector.outputs[0]
    out_img = np.uint8(np.squeeze(output.asnumpy().argmax(axis=1)))
    out_img = Image.fromarray(out_img)
    out_img.putpalette(get_palette())
    out_img.save(args.output) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def resolve(ctx):
    from PIL import Image
    if isinstance(ctx, list):
        ctx = [ctx[0]]
    net.load_parameters('superres.params', ctx=ctx)
    img = Image.open(opt.resolve_img).convert('YCbCr')
    y, cb, cr = img.split()
    data = mx.nd.expand_dims(mx.nd.expand_dims(mx.nd.array(y), axis=0), axis=0)
    out_img_y = mx.nd.reshape(net(data), shape=(-3, -2)).asnumpy()
    out_img_y = out_img_y.clip(0, 255)
    out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode='L')

    out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
    out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
    out_img = Image.merge('YCbCr', [out_img_y, out_img_cb, out_img_cr]).convert('RGB')

    out_img.save('resolved.png') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def main():
    width = 512
    height = 384

    context = Context()

    context.set_ray_type_count(1)

    context['result_buffer'] = Buffer.empty((height, width, 4), buffer_type='o', dtype=np.float32, drop_last_dim=True)

    ray_gen_program = Program('draw_color.cu', 'draw_solid_color')

    ray_gen_program['draw_color'] = np.array([0.462, 0.725, 0.0], dtype=np.float32)

    entry_point = EntryPoint(ray_gen_program)
    entry_point.launch(size=(width, height))

    result_array = context['result_buffer'].to_array()
    result_array *= 255
    result_image = Image.fromarray(result_array.astype(np.uint8)[:, :, :3])

    ImageWindow(result_image) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def addNoiseAndGray(surf):
    # https://stackoverflow.com/questions/34673424/how-to-get-numpy-array-of-rgb-colors-from-pygame-surface
    imgdata = pygame.surfarray.array3d(surf)
    imgdata = imgdata.swapaxes(0, 1)
    # print('imgdata shape %s' % imgdata.shape)  # shall be IMG_HEIGHT * IMG_WIDTH
    imgdata2 = noise_generator('s&p', imgdata)

    img2 = Image.fromarray(np.uint8(imgdata2))
    # img2.save('/home/zhichyu/Downloads/2sp.jpg')
    grayscale2 = ImageOps.grayscale(img2)
    # grayscale2.save('/home/zhichyu/Downloads/2bw2.jpg')
    # return grayscale2

    array = np.asarray(np.uint8(grayscale2))
    # print('array.shape %s' % array.shape)
    selem = disk(random.randint(0, 1))
    eroded = erosion(array, selem)
    return eroded 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def main():
    # location of depth module, config and parameters
    module_fn = 'models/depth.py'
    config_fn = 'models/depth.conf'#網絡結構
    params_dir = 'weights/depth'#網絡相關參數

    # load depth network
    machine = net.create_machine(module_fn, config_fn, params_dir)

    # demo image
    rgb = Image.open('demo_nyud_rgb.jpg')
    rgb = rgb.resize((320, 240), Image.BICUBIC)

    # build depth inference function and run
    rgb_imgs = np.asarray(rgb).reshape((1, 240, 320, 3))
    pred_depths = machine.infer_depth(rgb_imgs)

    # save prediction
    (m, M) = (pred_depths.min(), pred_depths.max())
    depth_img_np = (pred_depths[0] - m) / (M - m)
    depth_img = Image.fromarray((255*depth_img_np).astype(np.uint8))
    depth_img.save('demo_nyud_depth_prediction.png') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def save_image(fn, img, **kwargs):
    '''
    Save an image img to filename fn in the current output dir.
    kwargs the same as for PIL Image.save()
    '''
    (h, w, c) = img.shape
    if not isinstance(img, np.ndarray):
        img = np.array(img)
    if c == 1:
        img = np.concatenate((img,)*3, axis=2)
    if img.dtype.kind == 'f':
        img = (img * 255).astype('uint8')
    elif img.dtype.kind == 'f':
        img = img.astype('uint8')
    else:
        raise ValueError('bad dtype: %s' % img.dtype)
    i = Image.fromarray(img)
    with open(fn, 'w') as f:
        i.save(f, **kwargs) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def __call__(self, np_image):
        """
        Args:
            img (PIL Image): Image to be cropped and resized.
        Returns:
            PIL Image: Randomly cropped and resized image.
        """

        if self.size is None:
            size = np_image.shape
        else:
            size = self.size

        image = Image.fromarray(np_image)
        i, j, h, w = self.get_params(image, self.scale, self.ratio)
        image = resized_crop(image, i, j, h, w, size, self.interpolation)
        np_image = np.array(image)
        return np_image 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def save_annotation(label, filename, add_colormap=True):
    '''
    Saves the given label to image on disk.
    Args:
    label: The numpy array to be saved. The data will be converted to uint8 and saved as png image.
    save_dir: The directory to which the results will be saved.
    filename: The image filename.
    add_colormap: Add color map to the label or not.
    colormap_type: Colormap type for visualization.
    '''
    # Add colormap for visualizing the prediction.

    colored_label = label_to_color_image(label) if add_colormap else label

    image = Image.fromarray(colored_label.astype(dtype=np.uint8))
    image.save(filename) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import fromarray [as 別名]
def __getitem__(self, index):

        img_path = self.files[self.split][index].rstrip()
        lbl_path = os.path.join(self.annotations_base,
                                img_path.split(os.sep)[-2],
                                os.path.basename(img_path)[:-15] + 'gtFine_labelIds.png')

        _img = Image.open(img_path).convert('RGB')
        _tmp = np.array(Image.open(lbl_path), dtype=np.uint8)
        _tmp = self.encode_segmap(_tmp)
        _target = Image.fromarray(_tmp)

        sample = {'image': _img, 'label': _target}

        if self.split == 'train':
            return self.transform_tr(sample)
        elif self.split == 'val':
            return self.transform_val(sample)
        elif self.split == 'test':
            return self.transform_ts(sample)