HOG特徵提取

Question

我想提取阿拉伯手寫的線條圖像的HOG特徵。代碼如下。所以，我需要關於如何輸入圖像以及如何輸出特徵的幫助。任何人都可以請幫我解決這個問題。

import numpy as np 
from scipy import sqrt, pi, arctan2, cos, sin 
from scipy.ndimage import uniform_filter 

def hog(image, orientations=9, pixels_per_cell=(8, 8), 
    cells_per_block=(3, 3), visualise=False, normalise=False): 
"""Extract Histogram of Oriented Gradients (HOG) for a given image. 

Compute a Histogram of Oriented Gradients (HOG) by 

    1. (optional) global image normalisation 
    2. computing the gradient image in x and y 
    3. computing gradient histograms 
    4. normalising across blocks 
    5. flattening into a feature vector 

Parameters 
---------- 
image : (M, N) ndarray 
    Input image (greyscale). 
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 HOG. 
normalise : bool, optional 
    Apply power law compression to normalise the image before 
    processing. 

Returns 
------- 
newarr : ndarray 
    HOG for the image as a 1D (flattened) array. 
hog_image : ndarray (if visualise=True) 
    A visualisation of the HOG 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 

""" 
image = np.atleast_2d(image) 

""" 
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 image.ndim > 3: 
    raise ValueError("Currently only supports grey-level images") 

if normalise: 
    image = sqrt(image) 

""" 
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. 
""" 

gx = np.zeros(image.shape) 
gy = np.zeros(image.shape) 
gx[:, :-1] = np.diff(image, n=1, axis=1) 
gy[:-1, :] = np.diff(image, n=1, axis=0) 

""" 
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 + 1e-15)) * (180/pi) + 90 

sy, sx = image.shape 
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)) 
for i in range(orientations): 
    #create new integral image for this orientation 
    # isolate orientations in this range 

    temp_ori = np.where(orientation < 180/orientations * (i + 1), 
         orientation, 0) 
    temp_ori = np.where(orientation >= 180/orientations * i, 
         temp_ori, 0) 
    # select magnitudes for those orientations 
    cond2 = temp_ori > 0 
    temp_mag = np.where(cond2, magnitude, 0) 

    orientation_histogram[:,:,i] = uniform_filter(temp_mag, size=(cy, cx))[cy/2::cy, cx/2::cx] 


# now for each cell, compute the histogram 
#orientation_histogram = np.zeros((n_cellsx, n_cellsy, orientations)) 

radius = min(cx, cy) // 2 - 1 
hog_image = None 
if visualise: 
    hog_image = np.zeros((sy, sx), dtype=float) 

if visualise: 
    from skimage import draw 

    for x in range(n_cellsx): 
     for y in range(n_cellsy): 
      for o in range(orientations): 
       centre = tuple([y * cy + cy // 2, x * cx + cx // 2]) 
       dx = radius * cos(float(o)/orientations * np.pi) 
       dy = radius * sin(float(o)/orientations * np.pi) 
       rr, cc = draw.bresenham(centre[0] - dx, centre[1] - dy, 
             centre[0] + dx, centre[1] + dy) 
       hog_image[rr, cc] += orientation_histogram[y, x, o] 

""" 
The fourth stage computes normalisation, which takes local groups of 
cells and contrast normalises their overall responses before passing 
to next stage. Normalisation introduces better invariance to illumination, 
shadowing, and edge contrast. It is performed by accumulating a measure 
of local histogram "energy" over local groups of cells that we call 
"blocks". The result is used to normalise each cell in the block. 
Typically each individual cell is shared between several blocks, but 
its normalisations are block dependent and thus different. The cell 
thus appears several times in the final output vector with different 
normalisations. This may seem redundant but it improves the performance. 
We refer to the normalised block descriptors as Histogram of Oriented 
Gradient (HOG) descriptors. 
""" 

n_blocksx = (n_cellsx - bx) + 1 
n_blocksy = (n_cellsy - by) + 1 
normalised_blocks = np.zeros((n_blocksy, n_blocksx, 
           by, bx, orientations)) 

for x in range(n_blocksx): 
    for y in range(n_blocksy): 
     block = orientation_histogram[y:y + by, x:x + bx, :] 
     eps = 1e-5 
     normalised_blocks[y, x, :] = block/sqrt(block.sum() ** 2 + eps) 

""" 
The final step collects the HOG descriptors from all blocks of a dense 
overlapping grid of blocks covering the detection window into a combined 
feature vector for use in the window classifier. 
""" 

if visualise: 
    return normalised_blocks.ravel(), hog_image 
else: 
    return normalised_blocks.ravel() 

Answer 1

您可以使用OpenCV庫將圖像文件讀取到NumPy數組中。