这里是仅使用OpenCV的一个解决方案：

import numpy as np

import cv2

import matplotlib.pyplot as plt

img = cv2.cvtColor(cv2.imread("/home/me/Downloads/cat.jpg"),

cv2.COLOR_BGR2GRAY)

cell_size = (8, 8) # h x w in pixels

block_size = (2, 2) # h x w in cells

nbins = 9 # number of orientation bins

winSize is the size of the image cropped to an multiple of the cell size

hog = cv2.HOGDescriptor(_winSize=(img.shape[1] // cell_size[1] * cell_size[1],

img.shape[0] // cell_size[0] * cell_size[0]),

_blockSize=(block_size[1] * cell_size[1],

block_size[0] * cell_size[0]),

_blockStride=(cell_size[1], cell_size[0]),

_cellSize=(cell_size[1], cell_size[0]),

_nbins=nbins)

n_cells = (img.shape[0] // cell_size[0], img.shape[1] // cell_size[1])

hog_feats = hog.compute(img)\

.reshape(n_cells[1] - block_size[1] + 1,

n_cells[0] - block_size[0] + 1,

block_size[0], block_size[1], nbins) \

.transpose((1, 0, 2, 3, 4)) # index blocks by rows first

hog_feats now contains the gradient amplitudes for each direction,

for each cell of its group for each group. Indexing is by rows then columns.

gradients = np.zeros((n_cells[0], n_cells[1], nbins))

count cells (border cells appear less often across overlapping groups)

cell_count = np.full((n_cells[0], n_cells[1], 1), 0, dtype=int)

for off_y in range(block_size[0]):

for off_x in range(block_size[1]):

gradients[off_y:n_cells[0] - block_size[0] + off_y + 1,

off_x:n_cells[1] - block_size[1] + off_x + 1] += \

hog_feats[:, :, off_y, off_x, :]

cell_count[off_y:n_cells[0] - block_size[0] + off_y + 1,

off_x:n_cells[1] - block_size[1] + off_x + 1] += 1

Average gradients

gradients /= cell_count

Preview

plt.figure()

plt.imshow(img, cmap='gray')

plt.show()

bin = 5 # angle is 360/nbins * direction

plt.pcolor(gradients[:, :, bin])

plt.gca().invert_yaxis()

plt.gca().set_aspect('equal', adjustable='box')

plt.colorbar()

plt.show()