How are the depth maps for these images implemented?Thank you!
The paper is "Single Image Haze Removal Using Dark Channel Prior".
How are the depth maps for these images implemented?Thank you!
Related
I'm trying to detect the bottles is attached image with opencv.
mainly position and orientation is what i'm aiming for.
I tried contour detection and Canny, but this gives me mainly very unclear images and cant see where to distinguish the bottles.
I think if i can detect following edges i can determine the orientation based on their distance
Can someone help me out with any ideas?
Thanks!
I have a dataset containing RGB images and their Depth images before alignment:
RGB images - (480, 640, 3)
Depth images - (480, 640)
The camera used is RealSense depth d435i.
Is there a way of aligning the Depths along the RGB images of this dataset?
I prefer python but if there is a simple way in a different language it's all the same for me.
FYI - I know how align the images during capturing using realsense package in python but the question above is on saved dataset, after capturing the images.
Thank You.
paper "Fast Obstacle Detection Using U-Disparity Maps with Stereo Vision"
reference paper: "Fast Obstacle Detection Using U-Disparity Maps with Stereo Vision"
I want to ask can opencv do this reslut?
How?
Thank you
Thanks for #Liew Xun's comment.
Input are left image and right image from two camera.(stereo camera)
I want to use opencv's cv2.StereoBM to do the result like the paper's image.
But,unfortunately i failed. I can't make the result like this paper's image.
So i am here to ask does anybody have the same experience do disparity map.
Can you give me a help or give me some advice?
What you are asking is a mobile robotics visual perception related question.
I guess this author made a trick on you as it is not called U disparity
The stereo depth can be computed by opencv stereo matching. If you fail, there could be many reason. you need to post what you have. you can follow this link to get depth map. https://docs.opencv.org/master/dd/d53/tutorial_py_depthmap.html
This right image is done by concatenating depth observation along the vertical plane. It is called V disparity. For normal road scene, most of the background road should be on the same straight line spam diagonally. Anything not on this line can be considered as an obstacle above or below the road surface. When the sensing distance is far, there might be deformation for the line because of stereo uncertainty. This algorithm is designed to detect cases such as negative obstacle(drains, potholes) and positive obstacle( cars humans）
you can find a sample implementation here
https://github.com/thinkbell/v_disparity
If you are Phd candidate, you need to read a lot more paper to know whether some paper is lying on their contribution rather than post question here and hope someone can tell you the answer.
Regards
Dr Yuan Shenghai
I am using Object segmentation dataset having following information:
Introduced: IROS 2012
Device: Kinect v1
Description: 111 RGBD images of stacked and occluding objects on table.
Labelling: Per-pixel segmentation into objects.
link for the page: http://www.acin.tuwien.ac.at/?id=289
I am trying to use the depth map provided by the dataset. However, it seems the depth map is completely black.
Original image for the above depth map
I tried to do some preprocessing and normalised the image so that the depth map could be visualised in the form of a gray image.
img_depth = cv2.imread("depth_map.png",-1) #depth_map.png has uint16 data type
depth_array = np.array(img_depth, dtype=np.float32)
frame = cv2.normalize(depth_array, depth_array, 0, 1, cv2.NORM_MINMAX)
cv2.imwrite('capture_depth.png',frame*255)
The result of doing this preprocessing is:
In one of the posts in stackoverflow, i read that these black patches are the regions where the depth map was not defined.
If i have to use this depth map, what is the best possible way to fill these undefined regions? (I am thinking of filling these regions with K-nearest neighbour but feel there could be better ways for this).
Are there any RGB-D datasets that do not have such problems or these kind of problems always exists? what are the best possible way to tackle such problems?
Thanks in Advance!
Pretty much every 3d imaging technology will produce data with invalid or missing points. Lack of texture, too steep slopes, obscuration, transparency, reflections,... you name it.
There is no magic solution to filling these holes. You'll need some sort of interpolation or you maybe replace missing points based on some model.
The internet is full of methods for filling holes. Most techniques for intensity images can be successsfully applied to depth images.
It will depend on your application, your requirements and what you know about your objects.
Data quality in 3d is a question of time, money and the right combination of object and technology.
Areas that absorb or scatter the Kinect IR (like glossy surfaces or sharp edges) are filled with zero pixel value (indicating non-calculated depth). A method to approximately fill the non-captured data around these areas is by using the statistical median of a 5x5 window. This method works just fine for Kinect depth images. An example implementation can be seen for Matlab and C# in the links.
I am working on stereo camera depth estimation. However, for particular purpose, I need to use two camera with different FOV and focal length. After a lot of Google research, I know that I can still calibrate the two camera and rectify them to generate disparity map, but I have no idea how to convert disparity map to depth because their focal length is different, which does not satisfy the model in
http://docs.opencv.org/trunk/dd/d53/tutorial_py_depthmap.html
Does anyone have solution?
Thanks!
Have you considered using cv::triangulate() to obtain the (x,y,z) 3D world coordinates ?
I believe your end goal is to obtain a depth map.