Python skimage.transform方法代码示例

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
           preserve_range=False, anti_aliasing=False, anti_aliasing_sigma=None):
    """A wrapper for Scikit-Image resize().
    Scikit-Image generates warnings on every call to resize() if it doesn't
    receive the right parameters. The right parameters depend on the version
    of skimage. This solves the problem by using different parameters per
    version. And it provides a central place to control resizing defaults.
    """
    if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
        # New in 0.14: anti_aliasing. Default it to False for backward
        # compatibility with skimage 0.13.
        return skimage.transform.resize(
            image, output_shape,
            order=order, mode=mode, cval=cval, clip=clip,
            preserve_range=preserve_range, anti_aliasing=anti_aliasing,
            anti_aliasing_sigma=anti_aliasing_sigma)
    else:
        return skimage.transform.resize(
            image, output_shape,
            order=order, mode=mode, cval=cval, clip=clip,
            preserve_range=preserve_range) 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def set_weights(net,model_file):
    '''
    Sets the parameters of the model using the weights stored in model_file
    Parameters
    ----------
    net: a Lasagne layer

    model_file: string
        path to the model that containes the weights

    Returns
    -------
    None

    '''
    with open(model_file) as f:
        print('Load pretrained weights from %s...' % model_file)
        model = pickle.load(f)
    print('Set the weights...')
    lasagne.layers.set_all_param_values(net, model,trainable=True)


######## Below, there are several helper functions to transform (lists of) images into the right format  ###### 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def box_refinement_graph(box, gt_box):
    """Compute refinement needed to transform box to gt_box.
    box and gt_box are [N, (y1, x1, y2, x2)]
    """
    box = tf.cast(box, tf.float32)
    gt_box = tf.cast(gt_box, tf.float32)

    height = box[:, 2] - box[:, 0]
    width = box[:, 3] - box[:, 1]
    center_y = box[:, 0] + 0.5 * height
    center_x = box[:, 1] + 0.5 * width

    gt_height = gt_box[:, 2] - gt_box[:, 0]
    gt_width = gt_box[:, 3] - gt_box[:, 1]
    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
    gt_center_x = gt_box[:, 1] + 0.5 * gt_width

    dy = (gt_center_y - center_y) / height
    dx = (gt_center_x - center_x) / width
    dh = tf.log(gt_height / height)
    dw = tf.log(gt_width / width)

    result = tf.stack([dy, dx, dh, dw], axis=1)
    return result 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def rescale(img, input_height, input_width):
    # print("Original image shape:" + str(img.shape) + " --> it should be in H, W, C!")
    # print("Model's input shape is %dx%d") % (input_height, input_width)
    aspect = img.shape[1] / float(img.shape[0])
    # print("Orginal aspect ratio: " + str(aspect))
    if(aspect > 1):
        # landscape orientation - wide image
        res = int(aspect * input_height)
        imgScaled = skimage.transform.resize(
            img,
            (input_width, res),
            preserve_range=False)
    if(aspect < 1):
        # portrait orientation - tall image
        res = int(input_width / aspect)
        imgScaled = skimage.transform.resize(
            img,
            (res, input_height),
            preserve_range=False)
    if(aspect == 1):
        imgScaled = skimage.transform.resize(
            img,
            (input_width, input_height),
            preserve_range=False)
    return imgScaled 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def __call__(self, sample):
        image, annots = sample['img'], sample['annot']

        rows, cols, cns = image.shape

        largest_side = max(rows, cols)

        scale = self.img_size / largest_side

        # resize the image with the computed scale
        image = skimage.transform.resize(image, (int(round(rows*scale)), int(round((cols*scale)))))
        rows, cols, cns = image.shape

        new_image = np.zeros((self.img_size, self.img_size, cns)).astype(np.float32)
        new_image[:rows, :cols, :] = image.astype(np.float32)

        annots[:, :4] *= scale

        return {'img': torch.from_numpy(new_image), 'annot': torch.from_numpy(annots), 'scale': scale} 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
           preserve_range=False, anti_aliasing=False, anti_aliasing_sigma=None):
    """A wrapper for Scikit-Image resize().

    Scikit-Image generates warnings on every call to resize() if it doesn't
    receive the right parameters. The right parameters depend on the version
    of skimage. This solves the problem by using different parameters per
    version. And it provides a central place to control resizing defaults.
    """
    if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
        # New in 0.14: anti_aliasing. Default it to False for backward
        # compatibility with skimage 0.13.
        return skimage.transform.resize(
            image, output_shape,
            order=order, mode=mode, cval=cval, clip=clip,
            preserve_range=preserve_range, anti_aliasing=anti_aliasing,
            anti_aliasing_sigma=anti_aliasing_sigma)
    else:
        return skimage.transform.resize(
            image, output_shape,
            order=order, mode=mode, cval=cval, clip=clip,
            preserve_range=preserve_range) 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def load_image_array(image_file, image_size):
	img = skimage.io.imread(image_file)
	# GRAYSCALE
	if len(img.shape) == 2:
		img_new = np.ndarray( (img.shape[0], img.shape[1], 3), dtype = 'uint8')
		img_new[:,:,0] = img
		img_new[:,:,1] = img
		img_new[:,:,2] = img
		img = img_new

	img_resized = skimage.transform.resize(img, (image_size, image_size))

	# FLIP HORIZONTAL WIRH A PROBABILITY 0.5
	if random.random() > 0.5:
		img_resized = np.fliplr(img_resized)
	
	
	return img_resized.astype('float32') 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def __init__(self, root_dir, set_name='train2017', transform=None):
        """
        Args:
            root_dir (string): COCO directory.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        self.root_dir = root_dir
        self.set_name = set_name
        self.transform = transform

        self.coco = COCO(os.path.join(self.root_dir, 'annotations',
                                      'instances_' + self.set_name + '.json'))
        self.image_ids = self.coco.getImgIds()

        self.load_classes() 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def _convert_grid_indices_crs(self, grid_indices, old_crs, new_crs):
        if _OLD_PYPROJ:
            x2, y2 = pyproj.transform(old_crs, new_crs, grid_indices[:,1],
                                    grid_indices[:,0])
        else:
            x2, y2 = pyproj.transform(old_crs, new_crs, grid_indices[:,1],
                                      grid_indices[:,0], errcheck=True,
                                      always_xy=True)
        yx2 = np.column_stack([y2, x2])
        return yx2

    # def _convert_outer_indices_crs(self, affine, shape, old_crs, new_crs):
    #     y1, x1 = self.grid_indices(affine=affine, shape=shape)
    #     lx, _ = pyproj.transform(old_crs, new_crs,
    #                               x1, np.repeat(y1[0], len(x1)))
    #     rx, _ = pyproj.transform(old_crs, new_crs,
    #                               x1, np.repeat(y1[-1], len(x1)))
    #     __, by = pyproj.transform(old_crs, new_crs,
    #                               np.repeat(x1[0], len(y1)), y1)
    #     __, uy = pyproj.transform(old_crs, new_crs,
    #                               np.repeat(x1[-1], len(y1)), y1)
    #     return by, uy, lx, rx 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def load_img(path):
    """Returns a numpy array of an image specified by its path.
    
    Args:
        path: string representing the file path of the image to load
        
    Returns:
        resized_img: numpy array representing the loaded RGB image
        shape: the image shape
    """

    # Load image [height, width, depth]
    img = skimage.io.imread(path) / 255.0
    assert (0 <= img).all() and (img <= 1.0).all()

    # Crop image from center
    short_edge = min(img.shape[:2])
    yy = int((img.shape[0] - short_edge) / 2)
    xx = int((img.shape[1] - short_edge) / 2)
    shape = list(img.shape)

    crop_img = img[yy: yy + short_edge, xx: xx + short_edge]
    resized_img = skimage.transform.resize(crop_img, (shape[0], shape[1]))
    return resized_img, shape 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def __init__(self, root_dir, transform=None, loader = pil_loader):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        if transform == None :
            transform = torchvision.transforms.Compose([torchvision.transforms.Resize(224),
                                                        torchvision.transforms.RandomHorizontalFlip(p=0.5),
                                                        torchvision.transforms.RandomVerticalFlip(p=0.5),
                                                        torchvision.transforms.ToTensor()])
        self.root_dir = root_dir
        self.transform = transform
        self.loader = loader

        self.images = os.listdir(os.path.join(self.root_dir))

        self.image_class = np.array(pd.read_csv('val_details.txt', sep='\t')[['mage','class']]).astype('str')
        self.class_dic = {}
        for i in self.image_class :
            self.class_dic[i[0]]=i[1] 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def minimize_mask(bbox, mask, mini_shape):
    """Resize masks to a smaller version to reduce memory load.
    Mini-masks can be resized back to image scale using expand_masks()

    See inspect_data.ipynb notebook for more details.
    """
    mini_mask = np.zeros(mini_shape + (mask.shape[-1],), dtype=bool)
    for i in range(mask.shape[-1]):
        m = mask[:, :, i]
        y1, x1, y2, x2 = bbox[i][:4]
        m = m[y1:y2, x1:x2]
        if m.size == 0:
            raise Exception("Invalid bounding box with area of zero")
        # Resize with bilinear interpolation
        m = skimage.transform.resize(m, mini_shape, order=1, mode="constant")
        mini_mask[:, :, i] = np.around(m).astype(np.bool)
    return mini_mask 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def expand_mask(bbox, mini_mask, image_shape):
    """Resizes mini masks back to image size. Reverses the change
    of minimize_mask().

    See inspect_data.ipynb notebook for more details.
    """
    mask = np.zeros(image_shape[:2] + (mini_mask.shape[-1],), dtype=bool)
    for i in range(mask.shape[-1]):
        m = mini_mask[:, :, i]
        y1, x1, y2, x2 = bbox[i][:4]
        h = y2 - y1
        w = x2 - x1
        # Resize with bilinear interpolation
        m = skimage.transform.resize(m, (h, w), order=1, mode="constant")
        mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool)
    return mask


# TODO: Build and use this function to reduce code duplication 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def rgb2caffe(im, out_size=(128, 171)):
    '''
    Converts an RGB image to caffe format and downscales it as needed by C3D

    Parameters
    ----------
    im numpy array
        an RGB image
    downscale

    Returns
    -------
    a caffe image (channel,height, width) in BGR format

    '''
    im=np.copy(im)
    if len(im.shape)==2: # Make sure the image has 3 channels
        im = color.gray2rgb(im)

    h, w, _ = im.shape
    im = skimage.transform.resize(im, out_size, preserve_range=True)
    im = np.swapaxes(np.swapaxes(im, 1, 2), 0, 1)

    # Convert to BGR
    im = im[::-1, :, :]

    return np.array(im,theano.config.floatX) 

# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import transform [as 别名]
def box_refinement(box, gt_box):
    """Compute refinement needed to transform box to gt_box.
    box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is
    assumed to be outside the box.
    """
    box = box.astype(np.float32)
    gt_box = gt_box.astype(np.float32)

    height = box[:, 2] - box[:, 0]
    width = box[:, 3] - box[:, 1]
    center_y = box[:, 0] + 0.5 * height
    center_x = box[:, 1] + 0.5 * width

    gt_height = gt_box[:, 2] - gt_box[:, 0]
    gt_width = gt_box[:, 3] - gt_box[:, 1]
    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
    gt_center_x = gt_box[:, 1] + 0.5 * gt_width

    dy = (gt_center_y - center_y) / height
    dx = (gt_center_x - center_x) / width
    dh = np.log(gt_height / height)
    dw = np.log(gt_width / width)

    return np.stack([dy, dx, dh, dw], axis=1)


############################################################
#  Dataset
############################################################