apply 2to3 -w -f xrange

jsk_pcl_ros/scripts/color_histogram_visualizer.py

@@ -111,7 +111,7 @@ def plot_hist_hue(self, hist):
             plt.subplot(gs[1], facecolor='silver')
         else:  # matplotlib version < 2.0.0
             plt.subplot(gs[1], axisbg='silver')
-        bars = plt.bar(range(2), hist[-2:], label=["white", "black"],
+        bars = plt.bar(list(range(2)), hist[-2:], label=["white", "black"],
                        width=1.0, linewidth=2.0)
         bars[0].set_facecolor((1.0, 1.0, 1.0, 1.0))
         bars[1].set_facecolor((0.0, 0.0, 0.0, 1.0))

jsk_pcl_ros/scripts/draw_3d_circle.py

@@ -43,7 +43,7 @@ def publish(self):
     point_array = PointArrayStamped()
     point_array.header.frame_id = self.frame_id
     point_array.header.stamp = now
-    for i in range(self.RESOLUTION + 1) + [0]:
+    for i in list(range(self.RESOLUTION + 1)) + [0]:
       theta = 2 * math.pi / self.RESOLUTION * i
       x = self.radius * math.cos(theta)
       y = self.radius * math.sin(theta)

jsk_pcl_ros/scripts/extract_top_polygon_likelihood.py

@@ -29,7 +29,7 @@ def unsubscribe(self):
     def callback(self, msg, msg_coef):
         if len(msg.polygons) > 0:
             #self._pub.publish(msg.histograms[0])
-            max_index = max(xrange(len(msg.polygons)), key=lambda i: msg.likelihood[i])
+            max_index = max(list(range(len(msg.polygons))), key=lambda i: msg.likelihood[i])
             res = PolygonArray()
             res.header = msg.header
             res.polygons = [msg.polygons[max_index]]

jsk_perception/node_scripts/draw_classification_result.py

@@ -39,7 +39,7 @@ def _draw(self, cls_msg, imgmsg):
         rgb = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='rgb8')
 
         n_results = len(cls_msg.labels)
-        for i in xrange(n_results):
+        for i in range(n_results):
             label = cls_msg.labels[i]
             color = self.cmap[label % len(self.cmap)] * 255
             legend_size = int(rgb.shape[0] * 0.1)

jsk_perception/node_scripts/rect_array_to_image_marker.py

@@ -24,7 +24,7 @@ def convert(self, msg):
         marker.type = ImageMarker2.LINE_LIST
         n_colors = min(len(msg.rects), 256)
         cmap = jsk_recognition_utils.color.labelcolormap(n_colors)
-        for rect, rect_i in zip(msg.rects, range(n_colors)):
+        for rect, rect_i in zip(msg.rects, list(range(n_colors))):
             points = [(rect.x, rect.y),
                       (rect.x, rect.y + rect.height),
                       (rect.x + rect.width, rect.y + rect.height),

jsk_perception/node_scripts/ssd_object_detector.py

@@ -174,7 +174,7 @@ def image_cb(self, msg):
             xmin = max(0, int(np.floor(bbox[1])))
             ymax = min(H, int(np.ceil(bbox[2])))
             xmax = min(W, int(np.ceil(bbox[3])))
-            indices = [range(W*y+xmin, W*y+xmax) for y in range(ymin, ymax)]
+            indices = [list(range(W*y+xmin, W*y+xmax)) for y in range(ymin, ymax)]
             indices = np.array(indices, dtype=np.int32).flatten()
             indices_msg = PointIndices(header=msg.header, indices=indices)
             cluster_indices_msg.cluster_indices.append(indices_msg)

jsk_perception/scripts/create_db_for_feature_based_object_recognition.py

@@ -111,7 +111,7 @@ def main():
     knn.fit(X, y)
     y_pred = knn.predict(X)
     # validation: must be all 1.0
-    print(classification_report(y, y_pred, labels=range(len(target_names)),
+    print(classification_report(y, y_pred, labels=list(range(len(target_names))),
                                 target_names=target_names))
 
 

jsk_recognition_utils/node_scripts/rect_array_to_cluster_point_indices.py

@@ -57,7 +57,7 @@ def _convert(self, rects_msg, img_height=None, img_width=None):
             xmin = max(0, int(np.floor(rect.x)))
             ymax = min(H, int(np.ceil(rect.y + rect.height)))
             xmax = min(W, int(np.ceil(rect.x + rect.width)))
-            indices = [range(W*y+xmin, W*y+xmax) for y in range(ymin, ymax)]
+            indices = [list(range(W*y+xmin, W*y+xmax)) for y in range(ymin, ymax)]
             indices_msg.indices = np.array(indices, dtype=np.int32).flatten()
             cpi_msg.cluster_indices.append(indices_msg)
         self.pub.publish(cpi_msg)

jsk_recognition_utils/python/jsk_recognition_utils/mask.py

@@ -13,7 +13,7 @@ def bounding_rect_of_mask(img, mask):
 
 def descent_closing(mask, init_selem, n_times):
     S = init_selem.shape
-    for i in xrange(n_times):
+    for i in range(n_times):
         selem = np.ones((S[0] * (n_times - i), S[1] * (n_times - i)))
         mask = binary_closing(mask, selem=selem)
     return mask

posedetectiondb/src/GatherDetectionResults.py

@@ -78,7 +78,7 @@ def UniformlySampleSpace(self,bandwidth,bandthresh,delta=0.02):
         kdtree = pyANN.KDTree(self.measurements/bandwidth)
         sampledists = zeros(samplepoints.shape[0])
         goodpoints = []
-        for i in xrange(samplepoints.shape[0]):
+        for i in range(samplepoints.shape[0]):
             neighs,dists,kball = kdtree.kFRSearchArray(samplepoints[i:(i+1),:],5.0**2,32,0.0001)
             sampledists[i] = sum(exp(-dists[neighs>=0]))
         uniformpoints = samplepoints[sampledists>bandthresh,:]*bandwidth
@@ -90,7 +90,7 @@ def Prune(self,rawposes, nsize, thresh2, neighsize,giveupiters=100):
         """rawposes is Nx7"""
         iter = 1
         poses = array(rawposes)
-        indices = range(poses.shape[0])
+        indices = list(range(poses.shape[0]))
         N = poses.shape[0]
         nochange=0
         while N > nsize:

posedetectiondb/src/ObjectProjection.py

@@ -69,7 +69,7 @@ def Compute3DPositionImage(self,Tcamera, KK, imagewidth,imageheight,ROI=None,buf
         newKK = array(KK)
         newKK[0,2] -= offset[0]
         newKK[1,2] -= offset[1]
-        inds = array(range(width*height))
+        inds = array(list(range(width*height)))
         imagepoints = array((mod(inds,width),floor(inds/width)))
         camerapoints = transpose(dot(linalg.inv(newKK), r_[imagepoints,ones((1,imagepoints.shape[1]))]))
         hitindices,hitpositions = self.Get3DPointsFromImageRays(camerapoints, Tcamera)

sound_classification/scripts/create_dataset.py

@@ -113,7 +113,7 @@ def split():
         # copy train and test data
         # resize and augment data (multiple args.augment times)
         image_num_per_class = min(args.number, file_num)
-        selected_images = random.sample(range(file_num), image_num_per_class)
+        selected_images = random.sample(list(range(file_num)), image_num_per_class)
         for i, file_name in enumerate(np.array(file_names)[selected_images]):
             if file_name.endswith('.png') is not True:
                 continue

sound_classification/scripts/draw_classification_result.py

@@ -67,7 +67,7 @@ def _draw(self, cls_msg, imgmsg):
         rgb = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='rgb8')
 
         n_results = len(cls_msg.labels)
-        for i in xrange(n_results):
+        for i in range(n_results):
             label = cls_msg.labels[i]
             color = self.cmap[label % len(self.cmap)] * 255
             legend_size = int(rgb.shape[0] * 0.1)