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Hi All,

I am currently writing a python script where I want to take a pixel pair from my depth camera, have it spit out its XYZ coordinates.

I am currently subscribing to two topics, camera/color/image_raw, and camera/depth/color/points.

I am quite new to this, and I had assumed that once I got the pixel position from my image raw, I could do something with that to find the matching depth point; giving me the needed XYZ.

It seems that they don't have the same resolution either, the image is 640x480 whereas the pointcloud is 1280x720.

If someone could help me with this, that would be a great help, I'm really struggling to understand how to get this :)

EDIT: With the help of Ranjit, I have found a solution to my issue.

Using image_geometry.PinholeCameraModel() and camera_model.projectPixelTo3dRay((U,V)) and then normalising as suggested works well enough for an X Y (accurate to 3cm from 1 meter away with an intel realsense d435).

From here, Instead of using a generator, I put the pointcloud directly into a ros_numpy array like this get_xyz_points(pointcloud2_to_array(data), remove_nans=True, dtype=np.float32).

From here, we simply searched through the array using a cDKTree tree = cKDTree(points)

Finally add the Z to the preexisting XY, to get a decent enough result.

EDIT 2: On request, here is the final function created to call the camera, and get the coordinates.

### Callback for converting a pixel XY and depth to XYZ ###
def xyzCallback(self, data):

#get global X&Y and check assignemnt
global intY
global intX

#Get the pointcloud into an array
xyz_array = get_xyz_points(pointcloud2_to_array(data), remove_nans=True, dtype=np.float32)

#Use curetn camera model to get somewhat accurate 3d postiion for X & Y
vector = cam_model.projectPixelTo3dRay((intX,intY))

#Noramlise the 3dray
ray_z = [el / vector[2] for el in vector]

#Assign vector results to easier accessable varaibles 
a = vector[0]
b = vector[1]

#Use cKDTree to search for and accurate Z with results closest to the one inputtted
points = xyz_array[:,0:2] # drops the z column
tree = cKDTree(points)
idx = tree.query((a, b))[1] # this returns a tuple, we want the index
result = xyz_array[idx, 2]

#Assign new Z to normalised X Y
ray_z[2] = result
positions = ray_z
print("Final position = ", positions)

Originally posted by LeeAClift on ROS Answers with karma: 21 on 2021-02-24

Post score: 1


Original comments

Comment by thedarknight on 2021-07-08:
Could you kindly share the complete code here? Thanks in advance :)

Comment by LeeAClift on 2021-07-08:
Of course, here's my GitHub, with the completed code. Specifically, you will want to look at XYZCallback, starting on line 96.

https://github.com/LeeAClift/gazebo_edge_trace/blob/main/src/EdgeDetectGazebo.py

Comment by thedarknight on 2021-07-08:
I can't seem to find the repository on your profile. Could you please check if it's the right link or have you made the repo private?

Comment by LeeAClift on 2021-07-09:
Many apologies, the GitHub is private as it contains work that hasn't been published yet. I will attach the function to the original post though.

Comment by TapleFlib on 2023-04-22:
Hello @LeeAclift , I'm making a similar project now, and I'm stuck, are you willing to share your python code here? Do you run the python code on a terminal and the moveit launch file on another one? do you know how to visualize x,y,z arrow on rviz/moveit display area? I want it to be like this video (x,y,z arrow appeared on the detected object) : https://www.youtube.com/watch?v=iJWHRW7sZW8&ab_channel=RSAConference

I'm quite new to this sorry to be such a nuisance with all these questions but I will be really grateful if you could help me !

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1 Answer 1

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Hello there,

I think by following these steps you might achieve results according to your requirement.

a) First you have to subscribe to camera_info, pixel_coordinate, and point cloud topic.

b) Then use image_geometry.PinholeCameraModel() and give your camera information by calling this function image_geometry.PinholeCameraModel().fromCameraInfo(__Camera_INFO__DATA). Note: this function should be called once.

c) Call get_depth function for obtaining the distance of that pixel into the point cloud.

def get_depth(self, x, y):
    gen = pc2.read_points(self.pc, field_names='z', skip_nans=False, uvs=[(x, y)])
    return next(gen)

d) Call camera_model.projectPixelTo3dRay((U,V)) this function and you will get output into vector. Note Z will be always 1.

e) you will have to normalize Z and then after from that vector multiply get_depth function result(distance) with the vector X and Y value.

For more information please refer to this post

It seems that they don't have the same resolution either, the image is 640x480 whereas the pointcloud is 1280x720.

When you will subscribe to camera info node and image_geometry.PinholeCameraModel().fromCameraInfo() I think that this will handle your all different resolution of point cloud and color image.


Originally posted by Ranjit Kathiriya with karma: 1622 on 2021-02-24

This answer was ACCEPTED on the original site

Post score: 5


Original comments

Comment by LeeAClift on 2021-02-25:
Thanks for your response Ranjit, it seems very close to the result I am looking for.

After implementing your suggestion, as seen below, I am stuck on the last part (part e). Parts a and b are done in different functions, but I believe I have done those bits correctly

### Callback for converting a pixel XY and depth to XYZ ###
def xyzCallback(self, data):
    print (intY)
    print (intX)

    gen = point_cloud2.read_points(data, field_names='z', skip_nans=False, uvs=[(intX, intY)])

    nextGen = next (gen)

    XY = np.array(cam_model.projectPixelTo3dRay((intX,intY)))

    print (XY)

    print(XY*nextGen)

The initial results just for XY print out an X, Y and Z coordinate which don't seem correct, I am assuming this is because I haven't normalised them. When I then multiply them by the next(Gen) I get an array of three NaN's.

Have you got any ideas about what I am doing wrong?

Comment by Ranjit Kathiriya on 2021-02-25:
Hello LeeAClift,

XY = np.array(cam_model.projectPixelTo3dRay((intX,intY)))

In this code line, you are obtaining the vector that means on point cloud you are getting X and Y from 3d space. The output of this line should be in X, Y, Z Note: over here your Z always be 1 or around 1.

But before performing this did you check that you are getting distance for a particular pixel from point cloud by using the get_depth() function which was in point C? If you received the distance then start finding the vector which is your XY = np.array(cam_model.projectPixelTo3dRay((intX,intY))) .

For your reference, I am attaching a link that can be helpful to you. If you have any questions feel free to ask.

Comment by Ranjit Kathiriya on 2021-02-25:
Continue.

As I have maintained in the above comment about the Z value that it will be 1 or near to 1. For Eg. 0.98 or any random number. To make this number to 1 you first have to normalize this Z value to 1 and then multiply your X and Y with your obtained depth.

vector = self.camera_model.projectPixelTo3dRay((X,Y))
ray_z = [el / vector[2] for el in vector]
positions = [el * depth[0] for el in ray_z]

Here, this position will be X, Y, Z of the point cloud.

Comment by Ranjit Kathiriya on 2021-02-25:
If you want to check that your output in 3dspace means point cloud is true or false. You can check it by following doing reverse engineering by obtaining your U, V from X, Y, Z.

For that you can use the function called camera_model.project3dToPixel((positions[0], positions[1], positions[2])) and pass your X,Y,Z and you will get the U, V and compare it with your previous U,V value. If your code seems to be right then both values should be similar.

Comment by LeeAClift on 2021-02-25:
Hi Ranjit, thank you for the continuation.

Just so my understanding is correct, in the code above, vector gives the nono-normalised X and Y.

From here, we then normalise the Z with ray_Z, and then finally combine the now normalised vector with the depth we had gotten from get_depth?

After running this, I am still getting the issue that the result of gen = point_cloud2.read_points(data, field_names='z', skip_nans=False, uvs=[(intX, intY)]) is always giving a NaN, and when combined by doing positions = [el * depth[0] for el in ray_z], I get an array of 3 NaN's.

Am I doing something wrong? Does Get_distance have to be its own function, and not just a line of code in my current function?

Comment by Ranjit Kathiriya on 2021-02-25:\

vector gives the nono-normalised X and Y

Yes, you will get X, Y, Z but make a note: Z will be 1 or around 1. If you are getting Z around 0.89, or 0.98 it is normal so for that you have to normalize Z by ray_z = [el / vector[2] for el in vector] here vector[2] is your Z value.

Now to obtain your Z value you need to call your gen = point_cloud2.read_points(data, field_names='z', skip_nans=False, uvs=[(intX, intY)]) nextGen = next (gen) it will not be NaN. This line of code will always give you the exact distance in millimeters from the camera to the U,V pixel. I think you are doing something wrong if you are getting NaN values over here.

positions = [el * depth[0] for el in ray_z]

By multiplying, you will able to get pixel on 3d world(point cloud).

Comment by LeeAClift on 2021-02-25:
Thank you once again for your quick responses Ranjit, it is very much appreciated.

It seems I am doing something wrong here, but what, I do not know. I am quite new to robotic vision as a general field, hence many of my questions. I seem to be getting NaN whenever I access my pointcloud via point_cloud2.read_points, but if I use point_cloud2.get_xyz_points I can see them.

I know its a big ask, and you've done so much already, but is there any chance you could provide a complete class or function from the many snippets you've provided me, as I'm certain I am doing something wrong somewhere, and something like that would probably solve it.

Comment by Ranjit Kathiriya on 2021-02-25:
Hey LeeAclift,

I was giving you a code snippet from below link but this code is of finding 3d point from the depth camera and plot it into a point cloud. In place of the point cloud, you can also use a depth camera for obtaining the distance.

Comment by Ranjit Kathiriya on 2021-02-26:
Can you please! tell me what camera and which node you are obtaining your RGB channel and point cloud channel?

Comment by LeeAClift on 2021-02-26:
Hi Ranjit, thank you for the code last night, I will try to implement it soon (once I've finished teaching).

Camera-wise, I am using an Intel Realsense D435i simulated in Gazebo.

For topics, I am subscribing to /camera/color/image_raw for my RGB channel, and for my point cloud I am subscribing to /camera/depth/color/points.

I have also listed the rest of the rest of the topics provided by the camera, incase there's an easier option I never considered:

/camera/color/camera_info /camera/color/image_raw /camera/depth/camera_info /camera/depth/color/points /camera/depth/image_raw /camera/infra1/camera_info /camera/infra1/image_raw /camera/infra2/camera_info /camera/infra2/image_raw

I was looking through the Image_Geometry library you recommended, and I did see that there were functions built for stereoscopic cameras, specifically projectPixelTo3d, which look like they would be usable, but I will try implementing the code you sent me first.

Comment by Ranjit Kathiriya on 2021-02-26:
Can you please! confirm that your cloud-point is organized or not? You can check that by rostopic echo cloud_point_topic_name and over here check is_dense flag is true or false. If it is false that means that your cloud point is organized.

An unorganized cloud is dense, so is_dense = true because it has no invalid points. An organized cloud has invalid points and so is_dense = false.

Summarising: For this application, you need an organized cloud point that means the is_dense flag should be false.

Comment by LeeAClift on 2021-02-26:
Hi Ranjit, sorry for the delay in replying. Thank you for the links you sent, they were very informative.

I have just checked if my point cloud is dense or not, it is not dense, and the flag is set to false.

I reran my code guaranteeing that the selected pixel would have a pointcloud value (making sure it was hitting an object etc), it still seems that I am getting NaN from my pointcloud.

I will try running the code you sent yesterday in its entirety, and report back.

Thank you once again for how much you are helping me :)

Edit: I think the code you have provided is a clear way of completing my task, but I am just struggling to implement it myself. I will make the question as answered, and look at spending some time rewriting my existing code to fit around what you have suggested :)

Comment by Ranjit Kathiriya on 2021-03-01:
Hi LeeAclift,

Just tell me at which point you are stick will try to help as much I can. Enjoy your coding!!

I would suggest that after you get values from depth channels and everything gets working. Then after trying to move it to point cloud because it gives good results as compare to depth channels.

Comment by LeeAClift on 2021-03-02:
Hi Ranjit, Thanks for your continuous support through this. Between your suggestions and further research, I have created a solution which I have detailed in my initial question. Thanks once again :)

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