Get HOG image features from OpenCV + Python?

Solution 1:

In python opencv you can compute hog like this:

 import cv2
 hog = cv2.HOGDescriptor()
 im = cv2.imread(sample)
 h = hog.compute(im)

Solution 2:

1. Get Inbuilt Documentation: Following command on your python console will help you know the structure of class HOGDescriptor:

 import cv2; 
 help(cv2.HOGDescriptor())

2. Example Code: Here is a snippet of code to initialize an cv2.HOGDescriptor with different parameters (The terms I used here are standard terms which are well defined in OpenCV documentation here):

import cv2
image = cv2.imread("test.jpg",0)
winSize = (64,64)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,
                        histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
#compute(img[, winStride[, padding[, locations]]]) -> descriptors
winStride = (8,8)
padding = (8,8)
locations = ((10,20),)
hist = hog.compute(image,winStride,padding,locations)

3. Reasoning: The resultant hog descriptor will have dimension as: 9 orientations X (4 corner blocks that get 1 normalization + 6x4 blocks on the edges that get 2 normalizations + 6x6 blocks that get 4 normalizations) = 1764. as I have given only one location for hog.compute().

4. One more way to initialize is from xml file which contains all parameter values:

hog = cv2.HOGDescriptor("hog.xml")

To get an xml file one can do following:

hog = cv2.HOGDescriptor()
hog.save("hog.xml")

and edit the respective parameter values in xml file.

Solution 3:

Here is a solution that uses only OpenCV:

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()

I have used HOG descriptor computation and visualization to understand the data layout and vectorized the loops over groups.