from warnings import warn import numpy as np from scipy import ndimage as ndi from .._shared.utils import _validate_interpolation_order def profile_line(image, src, dst, linewidth=1, order=None, mode=None, cval=0.0, *, reduce_func=np.mean): """Return the intensity profile of an image measured along a scan line. Parameters ---------- image : ndarray, shape (M, N[, C]) The image, either grayscale (2D array) or multichannel (3D array, where the final axis contains the channel information). src : array_like, shape (2, ) The coordinates of the start point of the scan line. dst : array_like, shape (2, ) The coordinates of the end point of the scan line. The destination point is *included* in the profile, in contrast to standard numpy indexing. linewidth : int, optional Width of the scan, perpendicular to the line order : int in {0, 1, 2, 3, 4, 5}, optional The order of the spline interpolation, default is 0 if image.dtype is bool and 1 otherwise. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional How to compute any values falling outside of the image. cval : float, optional If `mode` is 'constant', what constant value to use outside the image. reduce_func : callable, optional Function used to calculate the aggregation of pixel values perpendicular to the profile_line direction when `linewidth` > 1. If set to None the unreduced array will be returned. Returns ------- return_value : array The intensity profile along the scan line. The length of the profile is the ceil of the computed length of the scan line. Examples -------- >>> x = np.array([[1, 1, 1, 2, 2, 2]]) >>> img = np.vstack([np.zeros_like(x), x, x, x, np.zeros_like(x)]) >>> img array([[0, 0, 0, 0, 0, 0], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [0, 0, 0, 0, 0, 0]]) >>> profile_line(img, (2, 1), (2, 4)) array([1., 1., 2., 2.]) >>> profile_line(img, (1, 0), (1, 6), cval=4) array([1., 1., 1., 2., 2., 2., 4.]) The destination point is included in the profile, in contrast to standard numpy indexing. For example: >>> profile_line(img, (1, 0), (1, 6)) # The final point is out of bounds array([1., 1., 1., 2., 2., 2., 0.]) >>> profile_line(img, (1, 0), (1, 5)) # This accesses the full first row array([1., 1., 1., 2., 2., 2.]) For different reduce_func inputs: >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.mean) array([0.66666667, 0.66666667, 0.66666667, 1.33333333]) >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.max) array([1, 1, 1, 2]) >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.sum) array([2, 2, 2, 4]) The unreduced array will be returned when `reduce_func` is None or when `reduce_func` acts on each pixel value individually. >>> profile_line(img, (1, 2), (4, 2), linewidth=3, order=0, ... reduce_func=None) array([[1, 1, 2], [1, 1, 2], [1, 1, 2], [0, 0, 0]]) >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.sqrt) array([[1. , 1. , 0. ], [1. , 1. , 0. ], [1. , 1. , 0. ], [1.41421356, 1.41421356, 0. ]]) """ order = _validate_interpolation_order(image.dtype, order) if mode is None: warn("Default out of bounds interpolation mode 'constant' is " "deprecated. In version 0.19 it will be set to 'reflect'. " "To avoid this warning, set `mode=` explicitly.", FutureWarning, stacklevel=2) mode = 'constant' perp_lines = _line_profile_coordinates(src, dst, linewidth=linewidth) if image.ndim == 3: pixels = [ndi.map_coordinates(image[..., i], perp_lines, prefilter=order > 1, order=order, mode=mode, cval=cval) for i in range(image.shape[2])] pixels = np.transpose(np.asarray(pixels), (1, 2, 0)) else: pixels = ndi.map_coordinates(image, perp_lines, prefilter=order > 1, order=order, mode=mode, cval=cval) # The outputted array with reduce_func=None gives an array where the # row values (axis=1) are flipped. Here, we make this consistent. pixels = np.flip(pixels, axis=1) if reduce_func is None: intensities = pixels else: try: intensities = reduce_func(pixels, axis=1) except TypeError: # function doesn't allow axis kwarg intensities = np.apply_along_axis(reduce_func, arr=pixels, axis=1) return intensities def _line_profile_coordinates(src, dst, linewidth=1): """Return the coordinates of the profile of an image along a scan line. Parameters ---------- src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The end point of the scan line. linewidth : int, optional Width of the scan, perpendicular to the line Returns ------- coords : array, shape (2, N, C), float The coordinates of the profile along the scan line. The length of the profile is the ceil of the computed length of the scan line. Notes ----- This is a utility method meant to be used internally by skimage functions. The destination point is included in the profile, in contrast to standard numpy indexing. """ src_row, src_col = src = np.asarray(src, dtype=float) dst_row, dst_col = dst = np.asarray(dst, dtype=float) d_row, d_col = dst - src theta = np.arctan2(d_row, d_col) length = int(np.ceil(np.hypot(d_row, d_col) + 1)) # we add one above because we include the last point in the profile # (in contrast to standard numpy indexing) line_col = np.linspace(src_col, dst_col, length) line_row = np.linspace(src_row, dst_row, length) # we subtract 1 from linewidth to change from pixel-counting # (make this line 3 pixels wide) to point distances (the # distance between pixel centers) col_width = (linewidth - 1) * np.sin(-theta) / 2 row_width = (linewidth - 1) * np.cos(theta) / 2 perp_rows = np.stack([np.linspace(row_i - row_width, row_i + row_width, linewidth) for row_i in line_row]) perp_cols = np.stack([np.linspace(col_i - col_width, col_i + col_width, linewidth) for col_i in line_col]) return np.stack([perp_rows, perp_cols])