def hof(flow, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(3, 3), visualise=False, normalise=False, motion_threshold=1.):
"""Extract Histogram of Optical Flow (HOF) for a given image.
Key difference between this and HOG is that flow is MxNx2 instead of MxN
Compute a Histogram of Optical Flow (HOF) by
1. (optional) global image normalisation
2. computing the dense optical flow
3. computing flow histograms
4. normalising across blocks
5. flattening into a feature vector
Parameters
----------
Flow : (M, N) ndarray
Input image (x and y flow images).
orientations : int
Number of orientation bins.
pixels_per_cell : 2 tuple (int, int)
Size (in pixels) of a cell.
cells_per_block : 2 tuple (int,int)
Number of cells in each block.
visualise : bool, optional
Also return an image of the hof.
normalise : bool, optional
Apply power law compression to normalise the image before
processing.
static_threshold : threshold for no motion
Returns
-------
newarr : ndarray
hof for the image as a 1D (flattened) array.
hof_image : ndarray (if visualise=True)
A visualisation of the hof image.
References
----------
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
* Dalal, N and Triggs, B, Histograms of Oriented Gradients for
Human Detection, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition 2005 San Diego, CA, USA
"""
flow = np.atleast_2d(flow)
"""
-1-
The first stage applies an optional global image normalisation
equalisation that is designed to reduce the influence of illumination
effects. In practice we use gamma (power law) compression, either
computing the square root or the log of each colour channel.
Image texture strength is typically proportional to the local surface
illumination so this compression helps to reduce the effects of local
shadowing and illumination variations.
"""
if flow.ndim < 3:
raise ValueError("Requires dense flow in both directions")
if normalise:
flow = sqrt(flow)
"""
-2-
The second stage computes first order image gradients. These capture
contour, silhouette and some texture information, while providing
further resistance to illumination variations. The locally dominant
colour channel is used, which provides colour invariance to a large
extent. Variant methods may also include second order image derivatives,
which act as primitive bar detectors - a useful feature for capturing,
e.g. bar like structures in bicycles and limbs in humans.
"""
if flow.dtype.kind == 'u':
# convert uint image to float
# to avoid problems with subtracting unsigned numbers in np.diff()
flow = flow.astype('float')
gx = np.zeros(flow.shape[:2])
gy = np.zeros(flow.shape[:2])
# gx[:, :-1] = np.diff(flow[:,:,1], n=1, axis=1)
# gy[:-1, :] = np.diff(flow[:,:,0], n=1, axis=0)
gx = flow[:,:,1]
gy = flow[:,:,0]
"""
-3-
The third stage aims to produce an encoding that is sensitive to
local image content while remaining resistant to small changes in
pose or appearance. The adopted method pools gradient orientation
information locally in the same way as the SIFT [Lowe 2004]
feature. The image window is divided into small spatial regions,
called "cells". For each cell we accumulate a local 1-D histogram
of gradient or edge orientations over all the pixels in the
cell. This combined cell-level 1-D histogram forms the basic
"orientation histogram" representation. Each orientation histogram
divides the gradient angle range into a fixed number of
predetermined bins. The gradient magnitudes of the pixels in the
cell are used to vote into the orientation histogram.
"""
magnitude = sqrt(gx**2 + gy**2)
orientation = arctan2(gy, gx) * (180 / pi) % 180
sy, sx = flow.shape[:2]
cx, cy = pixels_per_cell
bx, by = cells_per_block
n_cellsx = int(np.floor(sx // cx)) # number of cells in x
n_cellsy = int(np.floor(sy // cy)) # number of cells in y
# compute orientations integral images
orientation_histogram = np.zeros((n_cellsy, n_cellsx, orientations))
subsample = np.index_exp[cy / 2:cy * n_cellsy:cy, cx / 2:cx * n_cellsx:cx]
for i in range(orientations-1):
#create new integral image for this orientation
# isolate orientations in this range
temp_ori = np.where(orientation < 180 / orientations * (i + 1),
orientation, -1)
temp_ori = np.where(orientation >= 180 / orientations * i,
temp_ori, -1)
# select magnitudes for those orientations
cond2 = (temp_ori > -1) * (magnitude > motion_threshold)
temp_mag = np.where(cond2, magnitude, 0)
temp_filt = uniform_filter(temp_mag, size=(cy, cx))
orientation_histogram[:, :, i] = temp_filt[subsample]
''' Calculate the no-motion bin '''
temp_mag = np.where(magnitude <= motion_threshold, magnitude, 0)
temp_filt = uniform_filter(temp_mag, size=(cy, cx))
orientation_histogram[:, :, -1] = temp_filt[subsample]
# now for each cell, compute the histogram
hof_image = None
моя ошибка похожа на
orientation_histogram[:, :, i] = temp_filt[subsample]
TypeError: slice indices must be integers or None or have an __index__ method
Я пытался дать как int(i), но все равно получаю ту же ошибку
Я пытался переустановить numpy версии, но не смог
Что делать