fix for python3, use 2to3 -f print, 2to3 -f except

checkerboard_detector/src/objectdetection_tf_publisher.py

@@ -86,7 +86,7 @@ def send_dynamic_tf_request(self, pos_x, pos_y, pos_z, ori_x, ori_y, ori_z, ori_
         try:
             res = set_dynamic_tf(set_tf_request)
         except rospy.ServiceException as exc:
-            print("Service did not process request: " + str(exc))
+            print(("Service did not process request: " + str(exc)))
 
 if __name__== '__main__':
     rospy.init_node('objectdetection_tf_publisher', anonymous=True)

cr_calibration/cr_calibration.py

@@ -91,7 +91,7 @@ def queue_cr(self, limg, linfo, rimg, rinfo):
             return False
 
         dist = self.calc_distance(self.static_pose, lframe)
-        print dist
+        print(dist)
         if dist > 0.012:
             self.frame_list = []
             self.static_pose = None
@@ -116,8 +116,8 @@ def queue_cr(self, limg, linfo, rimg, rinfo):
             # finish check
             if len(self.result_list) > 7 and self.last_err < 0.1:
                 rospy.loginfo("Finished size = %d, err = %f" % (len(self.result_list), self.last_err))
-                print "translation: [%f, %f, %f]\nrotation: [%f, %f, %f, %f]" % (ret.p.x(), ret.p.y(), ret.p.z(), qx, qy, qz, qw)
-                print "(make-coords :pos #f(%f %f %f) :rot (quaternion2matrix #f(%f %f %f %f)))" % (1000*ret.p.x(), 1000*ret.p.y(), 1000*ret.p.z(), qw, qx, qy, qz)
+                print("translation: [%f, %f, %f]\nrotation: [%f, %f, %f, %f]" % (ret.p.x(), ret.p.y(), ret.p.z(), qx, qy, qz, qw))
+                print("(make-coords :pos #f(%f %f %f) :rot (quaternion2matrix #f(%f %f %f %f)))" % (1000*ret.p.x(), 1000*ret.p.y(), 1000*ret.p.z(), qw, qx, qy, qz))
                 #print "<node pkg=\"tf\" type=\"static_transform_publisher\" name=\"cam_link_broadcaster\" args=\"%f %f %f %f %f %f %f link1 link2 30\" />" % (ret.p.x(), ret.p.y(), ret.p.z(), qw, qx, qy, qz)
                 exit(-1)
 
@@ -195,7 +195,7 @@ def find_checkerboard_pose(self, ros_image, cam_info):
     #we need to convert the ros image to an opencv image
         try:
             image = self.bridge.imgmsg_to_cv(ros_image, "mono8")
-        except CvBridgeError, e:
+        except CvBridgeError as e:
             rospy.logerror("Error importing image %s" % e)
             return
 

doc/conf.py

@@ -179,7 +179,7 @@
 
     # Additional stuff for the LaTeX preamble.
     'preamble': "".join((
-        '\usepackage[utf8]{inputenc}',
+        "\\usepackage[utf8]{inputenc}",
         # NO-BREAK SPACE
         '\DeclareUnicodeCharacter{00A0}{ }',
         # BOX DRAWINGS LIGHT VERTICAL AND RIGHT

jsk_pcl_ros/scripts/check_depth_error/depth_error_calibration.py

@@ -125,7 +125,7 @@ def setParameter(classifier):
     c0 = classifier.intercept_
     param = DepthCalibrationParameter()
     if not isValidClassifier(classifier):
-        print "parameters are list"
+        print("parameters are list")
         return
     if model == "linear":
         param.coefficients2 = [0, 0, 0, 0, 0]
@@ -168,7 +168,7 @@ def processData(x, y, u, v, cu, cv, fit = True):
             zs = value_cache[(uu, vv)]
             for z in zs:
                 if abs(z - y) < eps_z:
-                    print "duplicated value"
+                    print("duplicated value")
                     return
                 else:
                     value_cache[(uu, vv)].append(y)
@@ -180,22 +180,22 @@ def processData(x, y, u, v, cu, cv, fit = True):
         c_us.append(cu)
         c_vs.append(cv)
         if u > u_min and u < u_max and v < v_max and v > v_min:
-            print (x, y)
+            print((x, y))
             xs.append(genFeatureVector(x, u, v, cu, cv))
             ys.append(y)
             if fit:
                 classifier.fit(xs, ys)
                 try:
                     setParameter(classifier)
-                except rospy.service.ServiceException, e:
+                except rospy.service.ServiceException as e:
                     rospy.logfatal("failed to call service: %s" % (e.message))
             try:
-                print modelEquationString(classifier)
-            except Exception, e:
+                print(modelEquationString(classifier))
+            except Exception as e:
                 rospy.logwarn("failed to print model: %s" % e.message)
 
             else:
-                print "(%d, %d) is out of range" % (u, v)
+                print("(%d, %d) is out of range" % (u, v))
 
 def callback(msg):
     global xs, ys, classifier, u_min, u_max, v_min, v_max, raw_xs
@@ -338,7 +338,7 @@ def updatePlot():
             plot_img.shape = (h, w, 3)
             plt.close()
             pub_error_plot.publish(bridge.cv2_to_imgmsg(plot_img, "bgr8"))
-        except Exception, e:
+        except Exception as e:
             rospy.logerr(e.message)
 
 def generateFrequencyMap():
@@ -379,7 +379,7 @@ def main():
     height = args.height
     if args.models:
         for m in MODELS:
-            print m
+            print(m)
         return
     model = args.model
     if model not in MODELS:
@@ -409,22 +409,22 @@ def main():
         classifier.fit(xs, ys)
         try:
             setParameter(classifier)
-        except rospy.service.ServiceException, e:
+        except rospy.service.ServiceException as e:
             rospy.logfatal("failed to call service: %s" % (e.message))
     try:
         plt.show()
     finally:
         if not args.csv:
             csv_filename = "calibration-%s.csv" % datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
-            print "Save calibration parameters to %s" % (csv_filename)
+            print("Save calibration parameters to %s" % (csv_filename))
             with open(csv_filename, "w") as f:
                 for x, y, u, v, cu, cv in zip(raw_xs, ys, us, vs, c_us, c_vs):
                     f.write("%f,%f,%d,%d,%f,%f\n" % (x, y, u, v, cu, cv))
         dump = rospy.get_param("~dump_result_into_yaml", "query")
         if dump is True or \
            (dump == "query" and query_yes_no("Dump result into yaml file?")):
             yaml_filename = "calibration_parameter_%s.yaml" % datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
-            print "writing to %s" % yaml_filename
+            print("writing to %s" % yaml_filename)
             c = classifier.coef_
             if model == "quadratic-uv-abs" or model == "quadratic-uv-quadratic-abs":
                 use_abs = "True"

jsk_pcl_ros/scripts/draw_3d_circle.py

@@ -16,7 +16,7 @@
 import math
 
 def usage():
-  print "Usage: ", sys.argv[0], "id frame_id radius [r, g, b]"
+  print("Usage: ", sys.argv[0], "id frame_id radius [r, g, b]")
 
 class Drawer3DCircle:
   RESOLUTION = 20

jsk_pcl_ros/scripts/dump_depth_error.py

@@ -19,13 +19,13 @@ def callback(msg):
     for bin_index, msg_and_errs in bins.items():
         if len(msg_and_errs) < 5:
             continue
-        print "Bin: {0}m ~ {1}m".format(grid * bin_index, grid * (bin_index + 1))
-        print "   Sample:", len(msg_and_errs)
+        print("Bin: {0}m ~ {1}m".format(grid * bin_index, grid * (bin_index + 1)))
+        print("   Sample:", len(msg_and_errs))
         errs = [err for (msg, err) in msg_and_errs]
         mean = np.mean(errs, axis=0)
         stddev = np.std(errs, axis=0)
-        print "   Mean:", mean
-        print "   Stddev:", stddev
+        print("   Mean:", mean)
+        print("   Stddev:", stddev)
     data.xs.append(msg.true_depth)
     data.ys.append(msg.observed_depth)
     pub.publish(data)

jsk_pcl_ros/scripts/in_hand_recognition_manager.py

@@ -55,8 +55,8 @@ def pose_diff_cb(pose_stamped):
     try:
         teacher_pose_stamped.header.stamp = rospy.Time(0)
         teacher_pose_stamped_recog_frame = listener.transformPose(pose_stamped.header.frame_id, teacher_pose_stamped)
-    except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException, tf.Exception), e:
-        print "tf error: %s" % e
+    except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException, tf.Exception) as e:
+        print("tf error: %s" % e)
         return
     teacher_pose_mat = (get_mat_from_pose(teacher_pose_stamped_recog_frame.pose))
     diff_pose_mat = (get_mat_from_pose(pose_stamped.pose))
@@ -70,8 +70,8 @@ def pose_diff_cb(pose_stamped):
         try:
             new_pose_stamped.header.stamp = rospy.Time(0)
             new_pose_stamped_for_renew = listener.transformPose(teacher_pose_stamped.header.frame_id, new_pose_stamped)
-        except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException, tf.Exception), e:
-            print "tf error: %s" % e
+        except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException, tf.Exception) as e:
+            print("tf error: %s" % e)
             return
         pose_teacher_cb(new_pose_stamped_for_renew)
         renew_flag = False

jsk_pcl_ros/scripts/plot_depth_error.py

@@ -36,8 +36,8 @@
     plt.plot(X, line_y_ransac, "r--",
              label="{0} x + {1}".format(model_ransac.estimator_.coef_[0][0],
                                         model_ransac.estimator_.intercept_[0]))
-    print "{0} x + {1}".format(model_ransac.estimator_.coef_[0][0],
-                               model_ransac.estimator_.intercept_[0])
+    print("{0} x + {1}".format(model_ransac.estimator_.coef_[0][0],
+                               model_ransac.estimator_.intercept_[0]))
     plt.grid()
     plt.xlabel("Distance [m]")
     plt.ylabel("Standard Deviation [m]")

jsk_pcl_ros/scripts/plot_gaussian.py

@@ -7,7 +7,7 @@
 import sys
 
 if len(sys.argv) != 3:
-    print "plot_gaussian.py mean variance"
+    print("plot_gaussian.py mean variance")
     sys.exit(1)
 mean = float(sys.argv[1])
 variance = float(sys.argv[2])

jsk_pcl_ros/scripts/renew_tracking.py

@@ -18,7 +18,7 @@ def cloud_cb(cloud):
         setCloud = rospy.ServiceProxy('particle_filter_tracker/renew_model', SetPointCloud2)
         setCloud(cloud, track_target_name)
         rospy.loginfo("Success renew_tracking service");
-    except rospy.ServiceException, e:
+    except rospy.ServiceException as e:
             rospy.loginfo("Failed to call renew_tracking service")
 
 if __name__ == "__main__":

jsk_pcl_ros/scripts/tower_detect_viewer_server.py

@@ -301,8 +301,8 @@ def clickCB(self, msg):
     def imageCB(self, data):
         try:
             self.cv_image = self.bridge.imgmsg_to_cv(data, "bgr8")
-        except CvBridgeError, e:
-            print e
+        except CvBridgeError as e:
+            print(e)
     def publishState(self):
         self.state.publish()
     def spin(self):

jsk_perception/node_scripts/binpack_rect_array.py

@@ -132,7 +132,7 @@ def bin_pack(self, rects):
         #blocks.sort(key=lambda x: x.rect.width*x.rect.height, reverse=True)
         blocks.sort(reverse=True)
         packer.fit(blocks)
-        print (packer.root.rect.width, packer.root.rect.height)
+        print((packer.root.rect.width, packer.root.rect.height))
         return (packer.root.rect, blocks)
         # for block in blocks:
         #     if block.fit_location:

jsk_perception/node_scripts/matchtemplate.py

@@ -149,8 +149,8 @@ def set_reference (self, rect):
             cv_image = self.bridge.imgmsg_to_cv2(
                 self.reference_image_msg, "bgr8")
             self.set_template('',ref_image=cv_image, ref_rect=rect)
-        except CvBridgeError, e:
-            print e
+        except CvBridgeError as e:
+            print(e)
 
     #
     # Callback Functions (lock require)
@@ -164,7 +164,7 @@ def set_reference_point_callback (self, msg): # PointStamped
                 pt[1]-self.default_template_size[1]/2,
                 self.default_template_size[0], self.default_template_size[1])
         self.set_reference(rect)
-        print rect
+        print(rect)
         search_rect = (pt[0]-self.default_search_size[0]/2,
                        pt[1]-self.default_search_size[1]/2,
                        self.default_search_size[0],self.default_search_size[1])
@@ -331,8 +331,8 @@ def ser_image_callback (self, msg):
                             :reference_size[1], :reference_size[0], :] = \
                             reference_image.copy()
 
-            except CvBridgeError, e:
-                print e
+            except CvBridgeError as e:
+                print(e)
 
         self.lockobj.release()
 

jsk_perception/node_scripts/random_forest_server.py

@@ -40,7 +40,7 @@ def initWithFileModel(cls, filename):
     def classifyData(self, req):
         ret = []
         for data in req.data:
-            print data
+            print(data)
             ret.append(" ".join([
                 str(predict_data)
                 for predict_data in self.clf.predict([data.point])]))

jsk_perception/node_scripts/solidity_rag_merge.py

@@ -129,7 +129,7 @@ def masked_slic(img, mask, n_segments, compactness):
     n_labels = len(np.unique(labels))
     try:
         mask = ndi.binary_closing(mask, structure=np.ones((3, 3)), iterations=1)
-    except IndexError, e:
+    except IndexError as e:
         rospy.logerr(e)
         return
     labels[mask == 0] = 0  # set bg_label

jsk_perception/sample/random_forest_client_sample.py

@@ -51,17 +51,17 @@
             answer = 0
         req_point.point = target
         req.data.append(req_point)
-        print OKGREEN,"Send Request         ====================>         Answer",ENDC
-        print OKGREEN,"    ",req_point.point,"       : ",str(answer),ENDC
+        print(OKGREEN,"Send Request         ====================>         Answer",ENDC)
+        print(OKGREEN,"    ",req_point.point,"       : ",str(answer),ENDC)
         response = predict_data(req)
-        print WARNING,"Get the result : ",ENDC
-        print WARNING,response.classifications,ENDC
+        print(WARNING,"Get the result : ",ENDC)
+        print(WARNING,response.classifications,ENDC)
         succeed = int(float(response.classifications[0])) == answer
         if succeed:
-            print OKBLUE,"Succeed!!!",ENDC
+            print(OKBLUE,"Succeed!!!",ENDC)
         else:
-            print FAIL,"FAIL...",FAIL
-        print "--- --- --- ---"
+            print(FAIL,"FAIL...",FAIL)
+        print("--- --- --- ---")
 
         # Config for plotting
         fig = plt.figure()

jsk_perception/scripts/check_cascadeclassifier.py

@@ -6,7 +6,7 @@
 
 def main():
     if len(sys.argv) < 3:
-        print "Usage: check_cascadeclassifier.py cascade.xml [image_files ...]"
+        print("Usage: check_cascadeclassifier.py cascade.xml [image_files ...]")
         sys.exit(1)
     classifier_file = sys.argv[1]
     files = sys.argv[2:]
@@ -20,9 +20,9 @@ def main():
                     cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
                 cv2.imshow('img', img)
                 cv2.waitKey(100)
-        except KeyboardInterrupt, e:
+        except KeyboardInterrupt as e:
             raise e
-        except Exception, e:
+        except Exception as e:
             pass
 
 if __name__ == "__main__":

jsk_perception/scripts/create_bof_dataset.py

@@ -30,8 +30,8 @@ def cmd_extract_bof(data_path, output, data_size=1):
     bof = BagOfFeatures()
     try:
         bof.fit(X)
-    except MemoryError, e:
-        print('data_size: {} ({} * {})'.format(n_data, data_size, n_data_all))
+    except MemoryError as e:
+        print(('data_size: {} ({} * {})'.format(n_data, data_size, n_data_all)))
         print(e)
     # save bof extractor
     print('saving bof')

jsk_perception/scripts/create_sift_dataset.py

@@ -53,7 +53,7 @@ def create_sift_dataset():
     targets, pos_list, scale_list, ori_list, desc_list = [], [], [], [], []
     for i, (filename, target) in enumerate(zip(bunch_files.filenames,
                                                bunch_files.target)):
-        print('filename: {}, label: {}'.format(filename, target))
+        print(('filename: {}, label: {}'.format(filename, target)))
         targets.append(target)
         # extract feature
         img = cv2.imread(filename, 0)

jsk_perception/scripts/opencv_traindata_rejector.py

@@ -30,7 +30,7 @@ def main():
                 width = int(width)
                 height = int(height)
                 if os.path.exists(fname):
-                    print "Opening " + fname
+                    print("Opening " + fname)
                     img = cv2.imread(fname)
                     cv2.rectangle(img, (x, y), 
                                   (x + width, y + height),

jsk_perception/scripts/sklearn_classifier_trainer.py

@@ -41,14 +41,14 @@ def main():
         clf = LogisticRegression()
     else:
         raise ValueError('unsupported classifier')
-    print('fitting {0}'.format(args.classifier))
+    print(('fitting {0}'.format(args.classifier)))
     clf.fit(X_train, y_train)
     clf.target_names_ = target_names
     with gzip.open(args.output, 'wb') as f:
         pickle.dump(clf, f)
     y_pred = clf.predict(X_test)
-    print('score of classifier: {}'.format(accuracy_score(y_test, y_pred)))
-    print(classification_report(y_test, y_pred, target_names=target_names))
+    print(('score of classifier: {}'.format(accuracy_score(y_test, y_pred))))
+    print((classification_report(y_test, y_pred, target_names=target_names)))
 
 
 if __name__ == '__main__':

jsk_perception/test/test_sparse_image.py

@@ -25,8 +25,8 @@ def _publish(self):
         try:
             self.edgemsg.header.stamp = rospy.Time.now()
             self.pub.publish(self.edgemsg)
-        except CvBridgeError, e:
-            print e
+        except CvBridgeError as e:
+            print(e)
 
     def _check_img_msg_diff(self, msg1, msg2):
         cnt = 0