diff --git a/.gitignore b/.gitignore
index 30bd60effde2c708c3b453273a5e4bb684859502..558af26895021178d5241b674d2e341ff27806aa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
-.idea
+/.vscode
*.bag
*.pyc
+.idea
.catkin_workspace
-# *CMakeLists.txt
\ No newline at end of file
+# *CMakeLists.txt
diff --git a/get_obj_dist/src/object_to_distance.py b/get_obj_dist/src/object_to_distance.py
index b42a91ad460c82836703cd0bd1966525b2c48cac..fb682489b0172209281521f8ea88090409d5cf5a 100755
--- a/get_obj_dist/src/object_to_distance.py
+++ b/get_obj_dist/src/object_to_distance.py
@@ -1,6 +1,5 @@
#!/usr/bin/env python
-import numpy as np
import sys
import cv2
import rospy
@@ -17,6 +16,7 @@ font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.75
font_color = (255, 255, 255)
line_type = 2
+marker_size = 5
size_of_moving_avg = 15
@@ -25,16 +25,19 @@ class obj_dist:
# Declare functional variables
self.bridge = CvBridge()
self.marker_array = MarkerArray()
- 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
+ self.moving_average = [[3] * size_of_moving_avg] * marker_size
+ self.set_marker_array(marker_size, 'camera_color_optical_frame', 'package://get_obj_dist/human_model.STL')
+
+ # 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/depth/image_rect_raw', 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,
self.depth_image_sub],
@@ -43,6 +46,7 @@ class obj_dist:
ts.registerCallback(self.callback)
def callback(self, bbx, image, depth):
+ print('working lol')
if bbx.length:
cv_depth = self.bridge.imgmsg_to_cv2(depth, 'passthrough')
cv_image = self.bridge.imgmsg_to_cv2(image, 'bgr8')
@@ -57,12 +61,13 @@ 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)
+ self.moving_average[person_id].pop()
+ self.moving_average[person_id].insert(0, filtered_depth.sum() / _size / 1000.0)
- current_avg = sum(self.moving_average) / size_of_moving_avg
+ current_avg = sum(self.moving_average[person_id]) / size_of_moving_avg
x_meter = get_x_in_meters(box.xmin, box.xmax, current_avg)
cv2.putText(cv_image, '{} meters / Person {}'.format(round(current_avg, 2), person_id),
@@ -79,14 +84,13 @@ 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()
@@ -124,5 +128,5 @@ def main(args):
if __name__ == '__main__':
- # rospy.set_param("use_sim_time", 'true')
+ rospy.set_param("use_sim_time", 'true')
main(sys.argv)
diff --git a/get_obj_dist/src/depth_cam.py b/get_obj_dist/src/tune_depth.py
similarity index 100%
rename from get_obj_dist/src/depth_cam.py
rename to get_obj_dist/src/tune_depth.py
diff --git a/get_obj_dist/src/utils.py b/get_obj_dist/src/utils.py
index 77a08112d0f50ba2d05f4c4138409f755abbb482..8d2a3a31a40f47b260b48563e44381acec56c8d4 100644
--- a/get_obj_dist/src/utils.py
+++ b/get_obj_dist/src/utils.py
@@ -1,12 +1,17 @@
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_1d, 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
diff --git a/human_detection/launch/hdm_full.launch b/human_detection/launch/hdm_full.launch
new file mode 100644
index 0000000000000000000000000000000000000000..a0d3db1c9fb62fad5f6fef2d54ebf00b918fe337
--- /dev/null
+++ b/human_detection/launch/hdm_full.launch
@@ -0,0 +1,4 @@
+<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
diff --git a/human_detection/src/reference.py b/human_detection/src/reference.py
index a94bb1d56fd4ea924a1e152cb0fa9e322f399987..c9f45d7ac5037b57a75937ae382249902399ee21 100755
--- a/human_detection/src/reference.py
+++ b/human_detection/src/reference.py
@@ -8,43 +8,47 @@ from std_msgs.msg import String
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
+
class image_converter:
- def __init__(self):
- self.image_pub = rospy.Publisher("image_topic_2",Image, queue_size=10)
+ def __init__(self):
+ self.image_pub = rospy.Publisher("image_topic_2", Image, queue_size=10)
- self.bridge = CvBridge()
- self.image_sub = rospy.Subscriber("/camera/color/image_raw",Image,self.callback)
+ self.bridge = CvBridge()
+ self.image_sub = rospy.Subscriber("/camera/color/image_raw", Image, self.callback)
- def callback(self,data):
- try:
- cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
-
- except CvBridgeError as e:
- print(e)
+ def callback(self, data):
+ try:
+ cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
- (rows,cols,channels) = cv_image.shape
- if cols > 60 and rows > 60 :
- cv2.circle(cv_image, (50,50), 10, 255)
+ except CvBridgeError as e:
+ print(e)
- cv2.imshow("Image window", cv_image)
- cv2.waitKey(3)
+ (rows, cols, channels) = cv_image.shape
+ if cols > 60 and rows > 60:
+ cv2.circle(cv_image, (50, 50), 10, 255)
+
+ cv2.imshow("Image window", cv_image)
+ cv2.waitKey(3)
+
+ try:
+ ret_img = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
+ ret_img.header = data.header
+ print(rospy.Time.now())
+ self.image_pub.publish(ret_img)
+ except CvBridgeError as e:
+ print(e)
- try:
- ret_img = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
- ret_img.header = data.header
- self.image_pub.publish(ret_img)
- except CvBridgeError as e:
- print(e)
def main(args):
- ic = image_converter()
- rospy.init_node('image_converter', anonymous=True)
- try:
- rospy.spin()
- except KeyboardInterrupt:
- print("Shutting down")
- cv2.destroyAllWindows()
+ ic = image_converter()
+ rospy.init_node('image_converter', anonymous=True)
+ try:
+ rospy.spin()
+ except KeyboardInterrupt:
+ print("Shutting down")
+ cv2.destroyAllWindows()
+
if __name__ == '__main__':
- main(sys.argv)
\ No newline at end of file
+ main(sys.argv)
diff --git a/human_detection/src/webcam.py b/human_detection/src/webcam.py
index f26ca35b6482da22ccf6df14c6f9aba2b4637fe7..1075dcd807a81bcfba40300c0995462bba627c16 100755
--- a/human_detection/src/webcam.py
+++ b/human_detection/src/webcam.py
@@ -68,7 +68,7 @@ class human_detector:
self.image_sub = rospy.Subscriber("/camera/color/image_raw", Image, self.callback)
self.image_pub = rospy.Publisher("/human_detected_image/image", Image, queue_size=10)
- self.bbx_pub = rospy.Publisher('/human_detected_image/bounding_box', box_list, queue_size=10)
+ self.bbx_pub = rospy.Publisher("/human_detected_image/bounding_box", box_list, queue_size=10)
def callback(self, data):
t0 = time.time()
@@ -126,7 +126,7 @@ class human_detector:
counter = 0
list_length = 0
bbx_list = box_list()
- bbx_list.header.stamp = rospy.Time.now()
+ bbx_list.header.stamp = data.header.stamp
for score in scores[0]:
if score > 0.6:
@@ -145,12 +145,10 @@ class human_detector:
try:
cvt_msg = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
- cvt_msg.header.stamp = rospy.Time.now()
+ cvt_msg.header.stamp = data.header.stamp
self.image_pub.publish(cvt_msg)
- # print(cvt_msg.header)
except CvBridgeError as e:
print(e)
-
# Append Accuracy
# if scores[0][0] > 0.01:
diff --git a/human_detection_demo.mp4 b/human_detection_demo.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..57d2884ed18681a37ae14fefb84453876510cbf3
Binary files /dev/null and b/human_detection_demo.mp4 differ