Python numpy.ceil方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4):
  draw = ImageDraw.Draw(image)
  (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
  draw.line([(left, top), (left, bottom), (right, bottom),
             (right, top), (left, top)], width=thickness, fill=color)
  text_bottom = bottom
  # Reverse list and print from bottom to top.
  text_width, text_height = font.getsize(display_str)
  margin = np.ceil(0.05 * text_height)
  draw.rectangle(
      [(left, text_bottom - text_height - 2 * margin), (left + text_width,
                                                        text_bottom)],
      fill=color)
  draw.text(
      (left + margin, text_bottom - text_height - margin),
      display_str,
      fill='black',
      font=font)

  return image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def create_image_grid(images, grid_size=None):
    assert images.ndim == 3 or images.ndim == 4
    num, img_w, img_h = images.shape[0], images.shape[-1], images.shape[-2]

    if grid_size is not None:
        grid_w, grid_h = tuple(grid_size)
    else:
        grid_w = max(int(np.ceil(np.sqrt(num))), 1)
        grid_h = max((num - 1) // grid_w + 1, 1)

    grid = np.zeros(list(images.shape[1:-2]) + [grid_h * img_h, grid_w * img_w], dtype=images.dtype)
    for idx in range(num):
        x = (idx % grid_w) * img_w
        y = (idx // grid_w) * img_h
        grid[..., y : y + img_h, x : x + img_w] = images[idx]
    return grid 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def __iter__(self):
        indices = []
        for i, size in enumerate(self.group_sizes):
            if size == 0:
                continue
            indice = np.where(self.flag == i)[0]
            assert len(indice) == size
            np.random.shuffle(indice)
            num_extra = int(np.ceil(size / self.samples_per_gpu)
                            ) * self.samples_per_gpu - len(indice)
            indice = np.concatenate(
                [indice, np.random.choice(indice, num_extra)])
            indices.append(indice)
        indices = np.concatenate(indices)
        indices = [
            indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]
            for i in np.random.permutation(
                range(len(indices) // self.samples_per_gpu))
        ]
        indices = np.concatenate(indices)
        indices = indices.astype(np.int64).tolist()
        assert len(indices) == self.num_samples
        return iter(indices) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def get_deep_representations(model, X, batch_size=256):
    """
    TODO
    :param model:
    :param X:
    :param batch_size:
    :return:
    """
    # last hidden layer is always at index -4
    output_dim = model.layers[-4].output.shape[-1].value
    get_encoding = K.function(
        [model.layers[0].input, K.learning_phase()],
        [model.layers[-4].output]
    )

    n_batches = int(np.ceil(X.shape[0] / float(batch_size)))
    output = np.zeros(shape=(len(X), output_dim))
    for i in range(n_batches):
        output[i * batch_size:(i + 1) * batch_size] = \
            get_encoding([X[i * batch_size:(i + 1) * batch_size], 0])[0]

    return output 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def create_sprite_image(images):
    if isinstance(images, list):
        images = np.array(images)
    img_h = images.shape[1]
    img_w = images.shape[2]
    # sprite 可以理解為所有小圖片拚成的大正方形矩陣
    m = int(np.ceil(np.sqrt(images.shape[0])))

    # 使用全 1 來初始化最終的大圖片
    sprite_image = np.ones((img_h*m, img_w*m))

    for i in range(m):
        for j in range(m):
            # 計算當前圖片編號
            cur = i * m + j
            if cur < images.shape[0]:
                # 將小圖片的內容複製到最終的 sprite 圖像
                sprite_image[i*img_h:(i+1)*img_h,
                             j*img_w:(j+1)*img_w] = images[cur]
    return sprite_image

# 加載 mnist 數據，製定 one_hot=False，得到的 labels 就是一個數字，而不是一個向量 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def curve_length(self, start=None, end=None, precision=0.01):
        '''
        Calculates the length of the curve by dividing the curve up
        into pieces of parameterized-length <precision>.
        '''
        if start is None: start = self.t[0]
        if end is None: end = self.t[-1]
        from scipy import interpolate
        if self.order == 1:
            # we just want to add up along the steps...
            ii = [ii for (ii,t) in enumerate(self.t) if start < t and t < end]
            ts = np.concatenate([[start], self.t[ii], [end]])
            xy = np.vstack([[self(start)], self.coordinates[:,ii].T, [self(end)]])
            return np.sum(np.sqrt(np.sum((xy[1:] - xy[:-1])**2, axis=1)))
        else:
            t = np.linspace(start, end, int(np.ceil((end-start)/precision)))
            dt = t[1] - t[0]
            dx = interpolate.splev(t, self.splrep[0], der=1)
            dy = interpolate.splev(t, self.splrep[1], der=1)
            return np.sum(np.sqrt(dx**2 + dy**2)) * dt 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def visual(title, X, activation):
    '''create a grid of images and save it as a final image
    title : grid image name
    X : array of images
    '''
    assert len(X.shape) == 4

    X = X.transpose((0, 2, 3, 1))
    if activation == 'sigmoid':
        X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
    elif activation == 'tanh':
        X = np.clip((X+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)
    n = np.ceil(np.sqrt(X.shape[0]))
    buff = np.zeros((int(n*X.shape[1]), int(n*X.shape[2]), int(X.shape[3])), dtype=np.uint8)
    for i, img in enumerate(X):
        fill_buf(buff, i, img, X.shape[1:3])
    cv2.imwrite('%s.jpg' % (title), buff) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
    # Plots training images overlaid with targets
    imgs = imgs.cpu().numpy()
    targets = targets.cpu().numpy()
    # targets = targets[targets[:, 1] == 21]  # plot only one class

    fig = plt.figure(figsize=(10, 10))
    bs, _, h, w = imgs.shape  # batch size, _, height, width
    bs = min(bs, 16)  # limit plot to 16 images
    ns = np.ceil(bs ** 0.5)  # number of subplots

    for i in range(bs):
        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
        boxes[[0, 2]] *= w
        boxes[[1, 3]] *= h
        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
        plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
        plt.axis('off')
        if paths is not None:
            s = Path(paths[i]).name
            plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
    fig.tight_layout()
    fig.savefig(fname, dpi=200)
    plt.close() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def __call__(self, input):
        with tf.variable_scope(self.name, reuse=self._reuse):
            if not self._reuse:
                print('\033[93m'+self.name+'\033[0m')
            _ = input
            num_channel = [32, 64, 128, 256, 256, 512]
            num_layer = np.ceil(np.log2(min(_.shape.as_list()[1:3]))).astype(np.int)
            for i in range(num_layer):
                ch = num_channel[i] if i < len(num_channel) else 512
                _ = conv2d(_, ch, self._is_train, info=not self._reuse,
                           norm=self._norm_type, name='conv{}'.format(i+1))
            _ = conv2d(_, int(num_channel[i]/4), self._is_train, k=1, s=1,
                       info=not self._reuse, norm='None', name='conv{}'.format(i+2))
            _ = conv2d(_, self._num_class+1, self._is_train, k=1, s=1, info=not self._reuse,
                       activation_fn=None, norm='None',
                       name='conv{}'.format(i+3))
            _ = tf.squeeze(_)
            if not self._reuse: 
                log.info('discriminator output {}'.format(_.shape.as_list()))
            self._reuse = True
            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
            return tf.nn.sigmoid(_), _ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def move(self, P0, P1):
        """
        Move a point P0 to a new legal location

        :param P0: ndarray[2]
        :param P1: ndarray[2]
        :return: ndarray[2]
        """
        x_dist, y_dist = P1 - P0
        tdist = np.sqrt(y_dist**2+x_dist**2)

        if self.is_in(P1):
            return P1
        else:
            x_steps = int(np.sign(x_dist) * np.ceil(abs(x_dist / self.dx)))#, self.max_step
            y_steps = int(np.sign(y_dist) * np.ceil(abs(y_dist / self.dy)))#, self.max_step
            i0, j0 = self.locate_ij(P0)
            P2 = self.locate_xy(i0, j0)
            P_off = P2 - P0
            self.loop_i = 0
            i1, j1 = self.valid_move(i0, j0, x_steps, y_steps, P_off)
            P2 = self.locate_xy(i1, j1) + P_off

            return P2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def tile_images(img_nhwc):
    """
    Tile N images into one big PxQ image
    (P,Q) are chosen to be as close as possible, and if N
    is square, then P=Q.

    input: img_nhwc, list or array of images, ndim=4 once turned into array
        n = batch index, h = height, w = width, c = channel
    returns:
        bigim_HWc, ndarray with ndim=3
    """
    img_nhwc = np.asarray(img_nhwc)
    N, h, w, c = img_nhwc.shape
    H = int(np.ceil(np.sqrt(N)))
    W = int(np.ceil(float(N)/H))
    img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)])
    img_HWhwc = img_nhwc.reshape(H, W, h, w, c)
    img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4)
    img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c)
    return img_Hh_Ww_c 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def split4(data,  max_stride, margin):
    splits = []
    data = torch.Tensor.numpy(data)
    _,c, z, h, w = data.shape

    w_width = np.ceil(float(w / 2 + margin)/max_stride).astype('int')*max_stride
    h_width = np.ceil(float(h / 2 + margin)/max_stride).astype('int')*max_stride
    pad = int(np.ceil(float(z)/max_stride)*max_stride)-z
    leftpad = pad/2
    pad = [[0,0],[0,0],[leftpad,pad-leftpad],[0,0],[0,0]]
    data = np.pad(data,pad,'constant',constant_values=-1)
    data = torch.from_numpy(data)
    splits.append(data[:, :, :, :h_width, :w_width])
    splits.append(data[:, :, :, :h_width, -w_width:])
    splits.append(data[:, :, :, -h_width:, :w_width])
    splits.append(data[:, :, :, -h_width:, -w_width:])
    
    return torch.cat(splits, 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def split8(data,  max_stride, margin):
    splits = []
    if isinstance(data, np.ndarray):
        c, z, h, w = data.shape
    else:
        _,c, z, h, w = data.size()
    
    z_width = np.ceil(float(z / 2 + margin)/max_stride).astype('int')*max_stride
    w_width = np.ceil(float(w / 2 + margin)/max_stride).astype('int')*max_stride
    h_width = np.ceil(float(h / 2 + margin)/max_stride).astype('int')*max_stride
    for zz in [[0,z_width],[-z_width,None]]:
        for hh in [[0,h_width],[-h_width,None]]:
            for ww in [[0,w_width],[-w_width,None]]:
                if isinstance(data, np.ndarray):
                    splits.append(data[np.newaxis, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
                else:
                    splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])

                
    if isinstance(data, np.ndarray):
        return np.concatenate(splits, 0)
    else:
        return torch.cat(splits, 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def split32(data,  max_stride, margin):
    splits = []
    _,c, z, h, w = data.size()
    
    z_width = np.ceil(float(z / 2 + margin)/max_stride).astype('int')*max_stride
    w_width = np.ceil(float(w / 4 + margin)/max_stride).astype('int')*max_stride
    h_width = np.ceil(float(h / 4 + margin)/max_stride).astype('int')*max_stride
    
    w_pos = [w*3/8-w_width/2,
             w*5/8-w_width/2]
    h_pos = [h*3/8-h_width/2,
             h*5/8-h_width/2]

    for zz in [[0,z_width],[-z_width,None]]:
        for hh in [[0,h_width],[h_pos[0],h_pos[0]+h_width],[h_pos[1],h_pos[1]+h_width],[-h_width,None]]:
            for ww in [[0,w_width],[w_pos[0],w_pos[0]+w_width],[w_pos[1],w_pos[1]+w_width],[-w_width,None]]:
                splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
    
    return torch.cat(splits, 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ceil [as 別名]
def split64(data,  max_stride, margin):
    splits = []
    _,c, z, h, w = data.size()
    
    z_width = np.ceil(float(z / 4 + margin)/max_stride).astype('int')*max_stride
    w_width = np.ceil(float(w / 4 + margin)/max_stride).astype('int')*max_stride
    h_width = np.ceil(float(h / 4 + margin)/max_stride).astype('int')*max_stride
    
    z_pos = [z*3/8-z_width/2,
             z*5/8-z_width/2]
    w_pos = [w*3/8-w_width/2,
             w*5/8-w_width/2]
    h_pos = [h*3/8-h_width/2,
             h*5/8-h_width/2]

    for zz in [[0,z_width],[z_pos[0],z_pos[0]+z_width],[z_pos[1],z_pos[1]+z_width],[-z_width,None]]:
        for hh in [[0,h_width],[h_pos[0],h_pos[0]+h_width],[h_pos[1],h_pos[1]+h_width],[-h_width,None]]:
            for ww in [[0,w_width],[w_pos[0],w_pos[0]+w_width],[w_pos[1],w_pos[1]+w_width],[-w_width,None]]:
                splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
    
    return torch.cat(splits, 0)