def eigen_texture(cv_image, blocksize=8, filtersize=3): gray_image = cv.cvCreateImage(cv.cvSize(cv_image.width, cv_image.height), cv.IPL_DEPTH_8U, 1) eig_tex = cv.cvCreateImage(cv.cvSize(cv_image.width*6, cv_image.height), cv.IPL_DEPTH_32F, 1) cv.cvCvtColor(cv_image, gray_image, cv.CV_BGR2GRAY) cv.cvCornerEigenValsAndVecs(gray_image, eig_tex, blocksize, filtersize) eig_tex_np = ut.cv2np(eig_tex) eig_tex_np = np.reshape(eig_tex_np, [cv_image.height, cv_image.width, 6]) return eig_tex_np[:,:,0:2]
def prepare(self, features_k_nearest_neighbors, nonzero_indices = None, all_save_load = False, regenerate_neightborhood_indices = False): #print np.shape(self.processor.pts3d_bound), 'shape pts3d_bound' imgTmp = cv.cvCloneImage(self.processor.img) self.imNP = ut.cv2np(imgTmp,format='BGR') self.processor.map2d = np.asarray(self.processor.map[0][0:2]) #copied from laser to image mapping if features_k_nearest_neighbors == None or features_k_nearest_neighbors == False: #use range self.kdtree2d = kdtree.KDTree(self.processor.pts3d_bound.T) #print len(nonzero_indices) #print np.shape(np.asarray((self.processor.pts3d_bound.T)[nonzero_indices])) if nonzero_indices != None: print getTime(), 'query ball tree for ', len(nonzero_indices), 'points' kdtree_query = kdtree.KDTree((self.processor.pts3d_bound.T)[nonzero_indices]) else: print getTime(), 'query ball tree' kdtree_query = kdtree.KDTree(self.processor.pts3d_bound.T) filename = self.processor.config.path+'/data/'+self.processor.scan_dataset.id+'_sphere_neighborhood_indices_'+str(self.processor.feature_radius)+'.pkl' if all_save_load == True and os.path.exists(filename) and regenerate_neightborhood_indices == False: #if its already there, load it: print getTime(), 'loading',filename self.kdtree_queried_indices = ut.load_pickle(filename) else: self.kdtree_queried_indices = kdtree_query.query_ball_tree(self.kdtree2d, self.processor.feature_radius, 2.0, 0.2) #approximate print getTime(), 'queried kdtree: ',len(self.kdtree_queried_indices),'points, radius:',self.processor.feature_radius if all_save_load == True: ut.save_pickle(self.kdtree_queried_indices, filename) #make dict out of list for faster operations? (doesn't seem to change speed significantly): #self.kdtree_queried_indices = dict(zip(xrange(len(self.kdtree_queried_indices)), self.kdtree_queried_indices)) else: #experiemental: use_20_nearest_neighbors == True #TODO: exclude invalid values in get_featurevector (uncomment code there) self.kdtree2d = kdtree.KDTree(self.processor.pts3d_bound.T) self.kdtree_queried_indices = [] print getTime(), 'kdtree single queries for kNN start, k=', features_k_nearest_neighbors count = 0 for point in ((self.processor.pts3d_bound.T)[nonzero_indices]): count = count + 1 result = self.kdtree2d.query(point, features_k_nearest_neighbors,0.2,2,self.processor.feature_radius) #existing = result[0][0] != np.Inf #print existing #print result[1] self.kdtree_queried_indices += [result[1]] #[existing] if count % 4096 == 0: print getTime(),count print getTime(), 'kdtree singe queries end' #convert to numpy array -> faster access self.kdtree_queried_indices = np.asarray(self.kdtree_queried_indices) #print self.kdtree_queried_indices #takes long to compute: #avg_len = 0 #minlen = 999999 #maxlen = 0 #for x in self.kdtree_queried_indices: # avg_len += len(x) # minlen = min(minlen, len(x)) # maxlen = max(maxlen, len(x)) #avg_len = avg_len / len(self.kdtree_queried_indices) #print getTime(), "range neighbors: avg_len", avg_len, 'minlen', minlen, 'maxlen', maxlen #create HSV numpy images: # compute the hsv version of the image image_size = cv.cvGetSize(self.processor.img) img_h = cv.cvCreateImage (image_size, 8, 1) img_s = cv.cvCreateImage (image_size, 8, 1) img_v = cv.cvCreateImage (image_size, 8, 1) img_hsv = cv.cvCreateImage (image_size, 8, 3) cv.cvCvtColor (self.processor.img, img_hsv, cv.CV_BGR2HSV) cv.cvSplit (img_hsv, img_h, img_s, img_v, None) self.imNP_h = ut.cv2np(img_h) self.imNP_s = ut.cv2np(img_s) self.imNP_v = ut.cv2np(img_v) textures = texture_features.eigen_texture(self.processor.img) self.imNP_tex1 = textures[:,:,0] self.imNP_tex2 = textures[:,:,1] self.debug_before_first_featurevector = True self.generate_voi_histogram(self.processor.point_of_interest,self.processor.voi_width)
def convert_cvimage_to_ROS(image): imgTmp = cv.cvCloneImage(image) imNP = ut.cv2np(imgTmp,format='BGR') ROS_image = np.reshape(imNP,(1,-1))[0] return ROS_image