Visualize depth image python

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

When I run it, I see a window which is all grey? I think it is wrong. I used this code from the OpenCV docs website. Can anyone help? PS: First I had some error which did not allow the output window to pop up. So, I added the two lines namely img1 and img 2 in my code.

You can display the result disparity using cv2. Notice the change of data type after normalizing the image. Prior to normalization disparity was of type int After normalization it is float32 mentioned within the function cv2.

Instead of using imshow use matplotlib to visualization as per the documentation. Also you can convert image into gray in the same line you read the image as follows.

How are we doing? Please help us improve Stack Overflow. Take our short survey. Learn more. Depth map shows everything grey! Asked 1 year, 10 months ago. Active 1 year, 10 months ago. Viewed 3k times. Ishara Madhawa 2, 4 4 gold badges 16 16 silver badges 33 33 bronze badges. Winbuntu Winbuntu 87 3 3 silver badges 12 12 bronze badges. In order to visualize the depth values better, rescale them. IsharaMadhawa and Dietrich Epp Thanks. I now modified the same code as on the OpenCV docs website.

I don't know why that corrected the error. But it did. Active Oldest Votes. Jeru Luke Jeru Luke Thanks : This is what I was looking for.

Thanks : It is right but check out Jeru Luke's answer too. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.PCA condenses information from a large set of variables into fewer variables by applying some sort of transformation onto them. The transformation is applied in such a way that linearly correlated variables get transformed into uncorrelated variables.

Correlation tells us that there is redundancy of information and if this redundancy can be reduced, then information can be compressed. For example, if there are two variables in the variable set which are highly correlated, then, we are not gaining any extra information by retaining both the variables because one can be nearly expressed as the linear combination of the other.

In such cases, PCA transfers the variance of the second variable onto the first variable by translation and rotation of original axes and projecting data onto new axes. The direction of projection is determined using eigenvalues and eigenvectors. So, the first few transformed features termed as Principal Components are rich in information, whereas the last features contain mostly noise with negligible information in them.

This transferability allows us to retain the first few principal components, thus reducing the number of variables significantly with minimal loss of information. This article focuses more on practical step-by-step PCA implementation on Image data rather than a theoretical explanation as there are tons of materials already available for that.

The image data has been chosen over tabular data so that the reader can better understand the working of PCA through image visualization. Technically, an image is a matrix of pixels whose brightness represents the reflectance of surface feature within that pixel.

The reflectance value ranges from 0 to for an 8-bit integer image. So the pixels with zero reflectance would appear as black, pixels with value appear as pure white and pixels with value in-between appear in a gray tone. Landsat TM satellite Images, captured over the coastal region of India, have been used in this tutorial. The images are resized to a smaller scale to reduce computational load on the CPU.

The image set consists of 7 band images captured across the blue, green, red, near-infrared NIR and mid-infrared MIR range of the electromagnetic spectrum. For readers who are interested in trying out steps on their own, kindly refer to this Github repository that contains Input datasets and the Ipython code used here. The first step is to import the required libraries and load data. To make accessibility and processing easier, the band images are stacked in a 3d numpy array of sizes x x 7 height x width x no of bands.

The color image shown below is a composite of Red, Green, and Blue RGB band images, reproducing the same view as it would have appeared to us. Get a glimpse of the scene. The image scene encompasses various surface features such as water, built-up area, forest, and farmland. Let us take a look at the reflectances of individual band images for different features and try to get some insight into the features in the band images.

If we observe the images, all bands have captured one or more surface features and also each feature is captured well in multiple bands. For example, farmlands are easily distinguishable from other surface features in both band 2 green and band 4 near-infrared image but not in others. So, there exists redundancy of information between the bands which means reflectances are somewhat correlated across bands.

This gives us the right opportunity to test PCA on them. Before applying PCA, we have to bring our data to a common format through standardization. The purpose of doing this is to make sure that variables are internally consistent with each other regardless of their type.

For example, if a dataset has two variables temperature measured in degrees Celsius and rainfall measured in cm. Since the variables range and units are different, it is not advisable to use dissimilar variables as they are, otherwise, variables differing in order of magnitude may introduce a model bias towards some variables.

Standardization is done by centering the variable by subtracting mean and then bringing them to a common scale by dividing standard deviation. Since the variables band images we are dealing with are similar and have the same range, standardization is not necessary but still, it is a good practice to apply.

Our variables which are image 2-d arrays need to be converted to 1-d vector to facilitate Matrix computation. Let us understand a little bit more about the axis transformation that happens within the PCA.Comment 0. Computers store images as a mosaic of tiny squares. This is like the ancient art form of tile mosaic, or the melting bead kits kids play with today.

The more and smaller tiles we use, the smoother or as we say less pixelated, the image will be. These sometimes get referred to as resolution of the images. Vector graphics are a somewhat different method of storing images that aims to avoid pixel related issues.

But even vector images, in the end, are displayed as a mosaic of pixels. The word pixel means a picture element. A simple way to describe each pixel is using a combination of three colors, namely Red, Green, Blue.

This is what we call an RGB image. In an RGB image, each pixel is represented by three 8 bit numbers associated with the values for Red, Green, Blue respectively. What is more interesting is to see that those tiny dots of little light are actually multiple tiny dots of little light of different colors, which are nothing but Red, Green, Blue channels.

The combination of those create images and basically what we see on screen every single day. Every photograph, in digital form, is made up of pixels. They are the smallest unit of information that makes up a picture. Usually round or square, they are typically arranged in a 2-dimensional grid. It then shows as white, and if all three colors are muted, or has the value of 0, the color shows as black. The combination of these three will, in turn, give us a specific shade of the pixel color.

Since each number is an 8-bit number, the values range from The combination of these three colors tends to the highest value among them.

visualize depth image python

Since each value can have different intensity or brightness value, it makes In any case the following should do the trick:.

This will transform depth image so that all values are between 0 and Then you just transform it into 8-bit and visualize it. Thanks for the reply. What can I do? I did not understand anything from what you said, what is the spin image? What is actually the thing that you want to do? If you can try to explain better and give some more information, I think it will be helpful.

I have to do this is in red.

Get image size (width, height) with Python, OpenCV, Pillow (PIL)

Regarding what you want to do, unfortunately I cannot help you on that. I need to go through the paper in order to understand what you want to do and at the moment I do not have the time. But if you can try to explain what exactly you want to do here on in another thread then there might be someone willing to help.

Asked: Object Identification in 2D image. A question about registration function in Opencv2. Missing depth attribute on images. First time here? Check out the FAQ!

Hi there! Please sign in help. Hi, someone can tell me how to display a depth image with opencv? Question Tools Follow. Related questions Object Identification in 2D image Depth image stitching shadow removal in image A question about registration function in Opencv2. Copyright OpenCV foundation Powered by Askbot version 0.

Please note: OpenCV answers requires javascript to work properly, please enable javascript in your browser, here is how. Ask Your Question.Graph theory and in particular the graph ADT abstract data-type is widely explored and implemented in the field of Computer Science and Mathematics.

One of the most popular areas of algorithm design within this space is the problem of checking for the existence or shortest path between two or more vertices in the graph. Properties such as edge weighting and direction are two such factors that the algorithm designer can take into consideration.

In this post I will be exploring two of the simpler available algorithms, Depth-First and Breath-First search to achieve the goals highlighted below:. So as to clearly discuss each algorithm I have crafted a connected graph with six vertices and six incident edges.

The resulting graph is undirected with no assigned edge weightings, as length will be evaluated based on the number of path edges traversed. There are two popular options for representing a graph, the first being an adjacency matrix effective with dense graphs and second an adjacency list effective with sparse graphs. I have opted to implement an adjacency list which stores each node in a dictionary along with a set containing their adjacent nodes. As the graph is undirected each edge is stored in both incident nodes adjacent sets.

This has been purposely included to provide the algorithms with the option to return multiple paths between two desired nodes.

The first algorithm I will be discussing is Depth-First search which as the name hints at, explores possible vertices from a supplied root down each branch before backtracking. This property allows the algorithm to be implemented succinctly in both iterative and recursive forms. Below is a listing of the actions performed upon each visit to a node. The implementation below uses the stack data-structure to build-up and return a set of vertices that are accessible within the subjects connected component.

The second implementation provides the same functionality as the first, however, this time we are using the more succinct recursive form. Due to a common Python gotcha with default parameter values being created only once, we are required to create a new visited set on each user invocation. Another Python language detail is that function variables are passed by reference, resulting in the visited mutable set not having to reassigned upon each recursive call.

We are able to tweak both of the previous implementations to return all possible paths between a start and goal vertex. The implementation below uses the stack data-structure again to iteratively solve the problem, yielding each possible path when we locate the goal. Using a generator allows the user to only compute the desired amount of alternative paths. Unfortunately the version of Pygments installed on the server at this time does not include the updated keyword combination.

An alternative algorithm called Breath-First search provides us with the ability to return the same results as DFS but with the added guarantee to return the shortest-path first. This algorithm is a little more tricky to implement in a recursive manner instead using the queue data-structure, as such I will only being documenting the iterative approach.

The actions performed per each explored vertex are the same as the depth-first implementation, however, replacing the stack with a queue will instead explore the breadth of a vertex depth before moving on. This behavior guarantees that the first path located is one of the shortest-paths present, based on number of edges being the cost factor.

Similar to the iterative DFS implementation the only alteration required is to remove the next item from the beginning of the list structure instead of the stacks last.

This implementation can again be altered slightly to instead return all possible paths between two vertices, the first of which being one of the shortest such path. As we are using a generator this in theory should provide similar performance results as just breaking out and returning the first matching path in the BFS implementation. In this post I will be exploring two of the simpler available algorithms, Depth-First and Breath-First search to achieve the goals highlighted below: Find all vertices in a subject vertices connected component.

Return all available paths between two vertices. And in the case of BFS, return the shortest path length measured by number of path edges. The Graph So as to clearly discuss each algorithm I have crafted a connected graph with six vertices and six incident edges.Here, the method of acquiring the image size width, height will be described.

In OpenCV, the image size width, height can be obtained as a tuple with the attribute shape of ndarray and the attribute size of PIL. Image in Pillow PIL. Note that the order of width and height is different.

The size width, height of the image can be acquired from the attribute shape indicating the shape of ndarray. Not limited to OpenCV, the size of the image represented by ndarraysuch as when an image file is read by Pillow and converted to ndarrayis obtained by shape.

In the case of a color image, it is a 3D ndarray of row height x column width x color 3.

visualize depth image python

An example where the number of colors number of channels is not used is as follows. If you want to get tuples in the order of width, heightyou can use slice like the following example.

When setting the size to cv2. For grayscale monochrome images, it is a 2D ndarray of rows height x columns width. If you want to assign width and height to variables, you can apply the following to either color or grayscale images:. If you want to get a width, height tuple, you can use slice.

The image can be either color or grayscale if it is written as follows. Image object obtained by reading an image with Pillow PIL has attributes sizewidthand height. The width and height can also be acquired with the attributes width and height. Here, the following contents will be described.

OpenCV: Get image size width, height with ndarray.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Blender Stack Exchange is a question and answer site for people who use Blender to create 3D graphics, animations, or games. It only takes a minute to sign up. I set up the nodes, forward the z-data to the output node, render and save the pixels as an array.

Basic Image Data Analysis Using Python: Part 1

Further, I use the dimensions of the image to setup a ndarray with numpy depth wich I save to an. Somewhere in this part I make a mistake since following code from commandline generates a somehow weird looking plot shown below as well. So I hope somebody can give me a hint on where to look exactly for the error, and also I would be very thankfull for coding and style tips.

I also found similar posts, but no one where the obtained data is visualized. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Visualizing depth image after accessing render results Ask Question. Asked 3 years, 4 months ago.

Decision tree visual example

Active 1 year, 1 month ago. Viewed 1k times. Amir 2, 1 1 gold badge 13 13 silver badges 38 38 bronze badges. Meaning that the first row is done properly, the second one is shifted to the left by one entry, the second row is shifted to the left by two entries, This happens already in the code.

Active Oldest Votes. Teris Teris 4 4 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook.

Sign up using Email and Password. Post as a guest Name.

visualize depth image python

Email Required, but never shown. The Overflow Blog. Socializing with co-workers while social distancing. Podcast Programming tutorials can be a real drag. Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Related 1. Hot Network Questions. Question feed.

Blender Stack Exchange works best with JavaScript enabled.


thoughts on “Visualize depth image python

Leave a Reply

Your email address will not be published. Required fields are marked *