Python numpy.nonzero方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def vis_mask(img, mask, bbox_color, show_parss=False):
    """Visualizes a single binary mask."""
    img = img.astype(np.float32)
    idx = np.nonzero(mask)

    border_color = cfg.VIS.SHOW_SEGMS.BORDER_COLOR
    border_thick = cfg.VIS.SHOW_SEGMS.BORDER_THICK

    mask_color = bbox_color if cfg.VIS.SHOW_SEGMS.MASK_COLOR_FOLLOW_BOX else _WHITE
    mask_color = np.asarray(mask_color)
    mask_alpha = cfg.VIS.SHOW_SEGMS.MASK_ALPHA

    _, contours, _ = cv2.findContours(mask.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
    if cfg.VIS.SHOW_SEGMS.SHOW_BORDER:
        cv2.drawContours(img, contours, -1, border_color, border_thick, cv2.LINE_AA)

    if cfg.VIS.SHOW_SEGMS.SHOW_MASK and not show_parss:
        img[idx[0], idx[1], :] *= 1.0 - mask_alpha
        img[idx[0], idx[1], :] += mask_alpha * mask_color

    return img.astype(np.uint8) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def vis_parsing(img, parsing, colormap, show_segms=True):
    """Visualizes a single binary parsing."""
    img = img.astype(np.float32)
    idx = np.nonzero(parsing)

    parsing_alpha = cfg.VIS.SHOW_PARSS.PARSING_ALPHA
    colormap = colormap_utils.dict2array(colormap)
    parsing_color = colormap[parsing.astype(np.int)]

    border_color = cfg.VIS.SHOW_PARSS.BORDER_COLOR
    border_thick = cfg.VIS.SHOW_PARSS.BORDER_THICK

    img[idx[0], idx[1], :] *= 1.0 - parsing_alpha
    # img[idx[0], idx[1], :] += alpha * parsing_color
    img += parsing_alpha * parsing_color

    if cfg.VIS.SHOW_PARSS.SHOW_BORDER and not show_segms:
        _, contours, _ = cv2.findContours(parsing.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        cv2.drawContours(img, contours, -1, border_color, border_thick, cv2.LINE_AA)

    return img.astype(np.uint8) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def plot_force_arrows(self, f):
        """Add arrows to the plot for each force."""
        arrowprops = {"arrowstyle": "->", "connectionstyle": "arc3",
            "lw": "2", "color": 0}
        cmap = plt.cm.get_cmap("hsv", f.shape[1] + 1)
        for load_i in range(f.shape[1]):
            nz = np.nonzero(f[:, load_i])
            arrowprops["color"] = cmap(load_i)
            for i in range(nz[0].shape[0]):
                x, y = id_to_xy(nz[0][i] // 2, self.nelx, self.nely)
                x = max(min(x, self.nelx - 1), 0)
                y = max(min(y, self.nely - 1), 0)
                z = int(nz[0][i] % 2)
                mag = -50 * f[nz[0][i], load_i]
                self.ax.annotate("", xy=(x, y), xycoords="data",
                    xytext = (0 if z else mag, mag if z else 0),
                    textcoords="offset points", arrowprops=arrowprops) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.

  For each box in boxlist1, we want its IOA to be more than minoverlap with
  at least one of the boxes in boxlist2. If it does not, we remove it.

  Args:
    boxlist1: BoxList holding N boxes.
    boxlist2: BoxList holding M boxes.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned boxlist with size [N', 4].
  """
  intersection_over_area = ioa(boxlist2, boxlist1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_boxlist1 = gather(boxlist1, keep_inds)
  return new_boxlist1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def rollout_iterator(self):
        """
        Iterate through all the rollouts in the dataset sequentially
        """
        end_indices = np.nonzero(self._dones)[0] + 1

        states = np.asarray(self._states)
        actions = np.asarray(self._actions)
        next_states = np.asarray(self._next_states)
        rewards = np.asarray(self._rewards)
        dones = np.asarray(self._dones)

        start_idx = 0
        for end_idx in end_indices:
            indices = np.arange(start_idx, end_idx)
            yield states[indices], actions[indices], next_states[indices], rewards[indices], dones[indices]
            start_idx = end_idx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def random_iterator(self, batch_size):
        """
        Iterate once through all (s, a, r, s') in batches in a random order
        """
        all_indices = np.nonzero(np.logical_not(self._dones))[0]
        np.random.shuffle(all_indices)

        states = np.asarray(self._states)
        actions = np.asarray(self._actions)
        next_states = np.asarray(self._next_states)
        rewards = np.asarray(self._rewards)
        dones = np.asarray(self._dones)

        i = 0
        while i < len(all_indices):
            indices = all_indices[i:i+batch_size]

            yield states[indices], actions[indices], next_states[indices], rewards[indices], dones[indices]

            i += batch_size

    ###############
    ### Logging ###
    ############### 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def log(self):
        end_idxs = np.nonzero(self._dones)[0] + 1

        returns = []

        start_idx = 0
        for end_idx in end_idxs:
            rewards = self._rewards[start_idx:end_idx]
            returns.append(np.sum(rewards))

            start_idx = end_idx

        logger.record_tabular('ReturnAvg', np.mean(returns))
        logger.record_tabular('ReturnStd', np.std(returns))
        logger.record_tabular('ReturnMin', np.min(returns))
        logger.record_tabular('ReturnMax', np.max(returns))

##################
### Tensorflow ###
################## 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def _step1(state):
    """Steps 1 and 2 in the Wikipedia page."""

    # Step 1: For each row of the matrix, find the smallest element and
    # subtract it from every element in its row.
    state.C -= state.C.min(axis=1)[:, np.newaxis]
    # Step 2: Find a zero (Z) in the resulting matrix. If there is no
    # starred zero in its row or column, star Z. Repeat for each element
    # in the matrix.
    for i, j in zip(*np.nonzero(state.C == 0)):
        if state.col_uncovered[j] and state.row_uncovered[i]:
            state.marked[i, j] = 1
            state.col_uncovered[j] = False
            state.row_uncovered[i] = False

    state._clear_covers()
    return _step3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def pred_to_dict(text, pred, prob):
    res = {"company": ("", 0), "date": ("", 0), "address": ("", 0), "total": ("", 0)}
    keys = list(res.keys())

    seps = [0] + (numpy.nonzero(numpy.diff(pred))[0] + 1).tolist() + [len(pred)]
    for i in range(len(seps) - 1):
        pred_class = pred[seps[i]] - 1
        if pred_class == -1:
            continue

        new_key = keys[pred_class]
        new_prob = prob[seps[i] : seps[i + 1]].max()
        if new_prob > res[new_key][1]:
            res[new_key] = (text[seps[i] : seps[i + 1]], new_prob)

    return {k: regex.sub(r"[\t\n]", " ", v[0].strip()) for k, v in res.items()} 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def _complexity_dimension(signal, delay=1, dimension_max=20, method="afnn", R=10.0, A=2.0):

    # Initalize vectors
    if isinstance(dimension_max, int):
        dimension_seq = np.arange(1, dimension_max + 1)
    else:
        dimension_seq = np.array(dimension_max)

    # Method
    method = method.lower()
    if method in ["afnn"]:
        E, Es = _embedding_dimension_afn(signal, dimension_seq=dimension_seq, delay=delay, show=False)
        E1 = E[1:] / E[:-1]
        E2 = Es[1:] / Es[:-1]
        min_dimension = [i for i, x in enumerate(E1 >= 0.85 * np.max(E1)) if x][0] + 1
        optimize_indices = [E1, E2]
        return dimension_seq, optimize_indices, min_dimension

    if method in ["fnn"]:
        f1, f2, f3 = _embedding_dimension_ffn(signal, dimension_seq=dimension_seq, delay=delay, R=R, A=A)
        min_dimension = [i for i, x in enumerate(f3 <= 1.85 * np.min(f3[np.nonzero(f3)])) if x][0]
        optimize_indices = [f1, f2, f3]
        return dimension_seq, optimize_indices, min_dimension
    else:
        raise ValueError("NeuroKit error: complexity_dimension(): 'method' not recognized.") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def _fractal_correlation(signal, r_vals, dist):
    """References
    -----------
    - `nolds <https://github.com/CSchoel/nolds/blob/master/nolds/measures.py>`_
    """
    n = len(signal)

    corr = np.zeros(len(r_vals))
    for i, r in enumerate(r_vals):
        corr[i] = 1 / (n * (n - 1)) * np.sum(dist < r)

    # filter zeros from csums
    nonzero = np.nonzero(corr)[0]
    r_vals = r_vals[nonzero]
    corr = corr[nonzero]

    return r_vals, corr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def pixel_list_to_array(pixel_locations, shape):
    ''' Transforms a list of pixel locations into a 2D array.

    :param tuple pixel_locations: A tuple of two lists representing the x and y
        coordinates of the locations of a set of pixels (i.e. the output of
        numpy.nonzero(valid_pixels) where valid_pixels is a 2D boolean array
        representing the pixel locations)
    :param list active_pixels: A list the same length as the x and y coordinate
        lists within pixel_locations representing whether a pixel location
        should be represented in the mask or not
    :param tuple shape: The shape of the output array consisting of a tuple
        of (height, width)

    :returns: A 2-D boolean array representing active pixels
    '''
    mask = numpy.zeros(shape, dtype=numpy.bool)
    mask[pixel_locations] = True

    return mask 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def trim_pixel_list(pixel_locations, active_pixels):
    ''' Trims the list of pixel locations to only the active pixels.

    :param tuple pixel_locations: A tuple of two lists representing the x and y
        coordinates of the locations of a set of pixels (i.e. the output of
        numpy.nonzero(valid_pixels) where valid_pixels is a 2D boolean array
        representing the pixel locations)
    :param list active_pixels: A list the same length as the x and y coordinate
        lists within pixel_locations representing whether a pixel location
        should be represented in the mask or not

    :returns: A tuple of two lists representing the x and y coordinates of the
        locations of active pixels
    '''
    active_pixels = numpy.nonzero(active_pixels)[0]
    return (pixel_locations[0][active_pixels],
            pixel_locations[1][active_pixels]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def _uniform_weight_alpha(sum_masked_arrays, output_datatype):
    '''Calculates the cumulative mask of a list of masked array

    Input:
        sum_masked_arrays (list of numpy masked arrays): The list of
            masked arrays to find the cumulative mask of, each element
            represents one band.
            (sums_masked_array.mask has a 1 for a no data pixel and
            a 0 otherwise)
        output_datatype (numpy datatype): The output datatype

    Output:
        output_alpha (numpy uint16 array): The output mask
            (0 for a no data pixel, uint16 max value otherwise)
    '''

    output_alpha = numpy.ones(sum_masked_arrays[0].shape)
    for band_sum_masked_array in sum_masked_arrays:
        output_alpha[numpy.nonzero(band_sum_masked_array.mask == 1)] = 0

    output_alpha = output_alpha.astype(output_datatype) * \
        numpy.iinfo(output_datatype).max

    return output_alpha 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nonzero [as 別名]
def create_pixel_plots(candidate_path, reference_path, base_name,
                       last_band_alpha=False, limits=None, custom_alpha=None):
    c_ds, c_alpha, c_band_count = _open_image_and_get_info(
        candidate_path, last_band_alpha)
    r_ds, r_alpha, r_band_count = _open_image_and_get_info(
        reference_path, last_band_alpha)

    _assert_consistent(c_alpha, r_alpha, c_band_count, r_band_count)

    if custom_alpha != None:
        combined_alpha = custom_alpha
    else:
        combined_alpha = numpy.logical_and(c_alpha, r_alpha)
    valid_pixels = numpy.nonzero(combined_alpha)

    for band_no in range(1, c_band_count + 1):
        c_band = gimage.read_single_band(c_ds, band_no)
        r_band = gimage.read_single_band(r_ds, band_no)
        file_name = '{}_{}.png'.format(base_name, band_no)
        display.plot_pixels(file_name, c_band[valid_pixels],
                            r_band[valid_pixels], limits)