Histogram Approach

get_obj_dist/src/object_to_distance.py

@@ -28,12 +28,14 @@ class obj_dist:
         self.moving_average = [3] * size_of_moving_avg
         self.set_marker_array(5, 'camera_color_optical_frame', 'package://get_obj_dist/human_model.STL')
 
-        # Initializing ROS Topics
+        # Initiale publishers
         self.dist_pub = rospy.Publisher('/obj_to_dist/human_distance', Image, queue_size=10)
         self.render_pub = rospy.Publisher('/obj_to_dist/show_people_marker_array', MarkerArray, queue_size=10)
 
+        # Initialize subscribers
         self.bbx_sub = message_filters.Subscriber('/human_detected_image/bounding_box', box_list)
         self.human_image_sub = message_filters.Subscriber('/human_detected_image/image', Image)
+        # self.depth_image_sub = message_filters.Subscriber('/camera/aligned_depth_to_color/image_raw', Image)
         self.depth_image_sub = message_filters.Subscriber('/camera/depth/image_rect_raw', Image)
 
         ts = message_filters.ApproximateTimeSynchronizer([self.bbx_sub, self.human_image_sub,
@@ -57,7 +59,8 @@ class obj_dist:
 
     def get_human_distance(self, cv_depth, cv_image, box, person_id):
         roi_depth = cv_depth[box.ymin:box.ymax, box.xmin:box.xmax]
-        filtered_depth, _size = filter_background(roi_depth)
+        # filtered_depth, _size = filter_background(roi_depth)
+        filtered_depth, _size = dynamic_background(roi_depth)
         if _size:
             self.moving_average.pop()
             self.moving_average.insert(0, filtered_depth.sum() / _size / 1000.0)
@@ -79,14 +82,13 @@ class obj_dist:
             return -1, -1, -1
 
     def set_marker_array(self, size, frame_id, package):
-        # self.marker_array.markers = [Marker()] * size
-        for i in range(0, size):
+        for counter in range(0, size):
             self.marker_array.markers.append(Marker())
-            self.marker_array.markers[i].header.frame_id = frame_id
-            self.marker_array.markers[i].lifetime = rospy.Duration()
-            self.marker_array.markers[i].id = i
-            self.marker_array.markers[i].type = Marker.MESH_RESOURCE
-            self.marker_array.markers[i].mesh_resource = package
+            self.marker_array.markers[counter].header.frame_id = frame_id
+            self.marker_array.markers[counter].lifetime = rospy.Duration()
+            self.marker_array.markers[counter].id = counter
+            self.marker_array.markers[counter].type = Marker.MESH_RESOURCE
+            self.marker_array.markers[counter].mesh_resource = package
 
     def set_model_coordinates(self, cood, person_id):
         self.marker_array.markers[person_id].header.stamp = rospy.Time.now()

get_obj_dist/src/utils.py

 import numpy as np
+import matplotlib.pyplot as plt
+
+_noise_limit = 100
+_diff = 1000
+_bin = [i * _diff for i in range(0, 11)]
 
 
 # Really basic way of separating foreground and background.
-def filter_background(roi):
+def filter_background(roi, max_depth=8000):
     # Anything further than 8000mm, we consider it as background
     # Anything less than 100mm is consider noise
-    ret_val = np.ma.masked_greater(roi, 8000)
-    ret_val = np.ma.masked_less(ret_val, 100)
+    ret_val = np.ma.masked_greater(roi, max_depth)
+    ret_val = np.ma.masked_less(ret_val, _noise_limit)
     unique, counts = np.unique(ret_val.mask, return_counts=True)
     _dict = dict(zip(unique, counts))
     if False in _dict:
@@ -15,6 +20,19 @@ def filter_background(roi):
         return ret_val, 0
 
 
+def dynamic_background(roi):
+    # Anything less than 100mm is sure noise
+    # roi = np.ma.masked_less(roi, 100)
+    roi_1d = roi.flatten()
+    hist, bins = np.histogram(roi, bins=_bin, density=True)
+    max_bin = hist.argmax() + 1
+
+    # plt.hist(roi_1d, bins=_bin, density=True)
+    # plt.title('Hello')
+    # plt.show()
+    return filter_background(roi, max_bin * _diff)
+
+
 def get_x_in_meters(xmin, xmax, z_i):
     # Tune z_c to get better value lol.
     # 500 is literally randomly chosen lol

human_detection/launch/hdm_full.launch

+<launch>
+    <node pkg="human_detection" type="webcam.py" name="webcam"/>
+    <node pkg="get_obj_dist" type="object_to_distance.py" name="object_to_distance"/>
+</launch>
\ No newline at end of file

human_detection/src/webcam.py

@@ -147,7 +147,6 @@ class human_detector:
             cvt_msg = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
             cvt_msg.header.stamp = rospy.Time.now()
             self.image_pub.publish(cvt_msg)
-            # print(cvt_msg.header)
         except CvBridgeError as e:
             print(e)
  

launch/HDM_full.launch

-<launch>
-    <node pkg="human_detection" type="webcam" name="webcam"/>
-    <node pkg="get_obj_dist" type="object_to_distance" name="object_to_distance"/>
-
-</launch>
\ No newline at end of file