def chog(img): ''' Hand Contour Based Histograms of Oriented Gradients ''' img = ip.normalize(img) num_contours = 10 num_bins = 16 con = ip.hand_contour(img, num_contours) features = np.array([]) mid_x = img.shape[1] / 2 mid_y = img.shape[0] / 2 for i in range(1,num_contours+1): con_img = img.copy() con_img[con!=i] = 0 gx = cv2.Sobel(con_img, cv2.CV_32F, 1, 0) gy = cv2.Sobel(con_img, cv2.CV_32F, 0, 1) mag, ang = cv2.cartToPolar(gx, gy) bins = np.int32(num_bins*ang/(2*np.pi)) bin_cells = bins[:mid_y,:mid_x], bins[mid_y:,:mid_x], bins[:mid_y,mid_x:], bins[mid_y: mid_x:] mag_cells = mag[:mid_y,:mid_x], mag[mid_y:,:mid_x], mag[:mid_y,mid_x:], mag[mid_y: mid_x:] hists = [np.bincount(b.ravel(), m.ravel(), num_bins) for b, m in zip(bin_cells, mag_cells)] features = np.concatenate((features, np.hstack(hists))) return features
def hod(img): ''' Contour based Histograms of Depth Data ''' img = ip.normalize(img) num_contours = 10 # number of contours to be used for each hand num_bins = 20 # number of depth bins for the histograms con = ip.hand_contour(img, num_contours) features = np.array([]) # initialize features for i in range(1,num_contours+1): # get features for each contour hist = np.histogram(img[con==i], bins=num_bins, range=(0, 255)) # get hist of depth values for contour features = np.concatenate((features, hist[0])) # update features return features
