features.py 文件源码

def hog_feature(im):
  """Compute Histogram of Gradient (HOG) feature for an image

       Modified from skimage.feature.hog
       http://pydoc.net/Python/scikits-image/0.4.2/skimage.feature.hog

     Reference:
       Histograms of Oriented Gradients for Human Detection
       Navneet Dalal and Bill Triggs, CVPR 2005

    Parameters:
      im : an input grayscale or rgb image

    Returns:
      feat: Histogram of Gradient (HOG) feature

  """

  # convert rgb to grayscale if needed
  if im.ndim == 3:
    image = rgb2gray(im)
  else:
    image = np.at_least_2d(im)

  sx, sy = image.shape # image size
  orientations = 9 # number of gradient bins
  cx, cy = (8, 8) # pixels per cell

  gx = np.zeros(image.shape)
  gy = np.zeros(image.shape)
  gx[:, :-1] = np.diff(image, n=1, axis=1) # compute gradient on x-direction
  gy[:-1, :] = np.diff(image, n=1, axis=0) # compute gradient on y-direction
  grad_mag = np.sqrt(gx ** 2 + gy ** 2) # gradient magnitude
  grad_ori = np.arctan2(gy, (gx + 1e-15)) * (180 / np.pi) + 90 # gradient orientation

  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_cellsx, n_cellsy, orientations))
  for i in range(orientations):
    # create new integral image for this orientation
    # isolate orientations in this range
    temp_ori = np.where(grad_ori < 180 / orientations * (i + 1),
                        grad_ori, 0)
    temp_ori = np.where(grad_ori >= 180 / orientations * i,
                        temp_ori, 0)
    # select magnitudes for those orientations
    cond2 = temp_ori > 0
    temp_mag = np.where(cond2, grad_mag, 0)
    orientation_histogram[:,:,i] = uniform_filter(temp_mag, size=(cx, cy))[cx/2::cx, cy/2::cy].T

  return orientation_histogram.ravel()