I am completely new to working with point cloud data. Right now I have a ".ply" file and its corresponding ".bmp" file. Both of them were generated from a TOF camera.
I am trying to get a ".jpg" file by superimposing the depth data on the BMP file, but i am ffailing miserably.
I have tried using the open3d library for this purpose. But it does not work on google colab. Therefore I am looking for a solution in the python-pcl library (or any other library)
how can I achieve this?
Related
I am working with the Ai4Boundaries dataset and while the data in the imagery folder is opening with Windows Photos + causing no issues in the python code I'm reading it into, the data in the mask folder will only open on ArcMap (as a black-blue gradient) and is causing errors in my code. (Both the imagery and the mask are in tiff format).
Here is a link to the
Imagery
Masks
When I simply try and open a mask using python with this code
plt.imshow(mpimg.imread('/content/AT_2989_ortholabel_1m_512.tif'))
the error I get is
UnidentifiedImageError: cannot identify image file '/content/AT_2989_ortholabel_1m_512.tif'
Any leads as to what the issue is and how I can resolve it?
I tried converting a mask to png and the png file is working fine on my code. But I'm working with around 7k+ images and don't know how to bulk convert.
I am expanding my limited Python knowledge by converting some MATLAB image analysis code to Python. I am following Image manipulation and processing using Numpy and Scipy. The code in Section 2.6.1 saves an image using both imageio.imsave and face.tofile, where type(face)=<class 'imageio.core.util.Array>'.
I am trying to understand why there are two ways to export an image. I tried web-searching tofile, but got numpy.ndarray.tofile. It's very sparse, and doesn't seem to be specific to images. I also looked for imageio.core.util.Array.tofile, but wasn't able to find anything.
Why are there two ways to export files? And why does imageio.core.util.Array.tofile seem to be un-findable online?
The difference is in what the two functions write in the file.
imageio.imsave() saves a conventional image, like a picture or photo, in JPEG/PNG format that can be viewed with an image viewer like GIMP, feh, eog, Photoshop or MSxPaint.
tofile() saves in a Numpy-compatible format that only Numpy (and a small number of other Python tools) use.
I am using a QGIS plugin of a friend written in Python, which reclassifies the pixels of a raster by setting points, these points span a polygon and all the pixels within the polygon will be converted or reclassified. So far, it works more or less fine if I use a normal raster image from my hard disk in the format of .img or .tiff. By reclassifying the pixels, all the changes will be automatically saved in the image on the disk.
In a next step, I want to store all my raster images in a PostGIS database and manipulate them with that tool. Unfortunately, the tool cannot convert the pixels of the image if I load them into QGIS from the database.
The tool does not produce any error message. It starts loading and then nothing happens.
So the question is: Do I need to adapt the saving method of the plugin or is it generally impossible to manipulate raster images in QGIS which are stored in a database, or do I need special rights to access the raster images raster data type?
I have the below PNG image and I am trying to identify which box is checked using Python.
I installed the OMR (optical mark recognition) package https://pypi.python.org/pypi/omr/0.0.7 but it wasn't any help and there wasn't any documentation about OMR.
So I need to know if there is any API or useful package I can use with Python.
Here is my image:
If you're not afraid of a little experimenting, the Python Imaging Library (PIL, download from http://www.pythonware.com/products/pil/ or your favorite repo. Manual: http://effbot.org/imagingbook/pil-index.htm) permits loading the PNG, and accessing it.
You can extract a section of the image (eg. the interior of a checkbox. See crop in the library), and sum the pixels in that sub-image (see point). Compare that with a threshold (say > 10 pixels = checked).
If the PNG comes from scanning forms, you may have to add some positional checking.
I have a local directory full of geotiff files which make up a map of the UK.
I'm using mapnik to render different images at various locations in the UK.
I'm wondering what is the best way to approach this?
I can create a single RasterSymbolizer then loop through the tiff directory and add each tiff as a seperate layer, then use mapniks zoom_to_box to render at the correct location.
But would this cause the rendering time to be unnecessarily slow? I have no information on how the tiles fit together (other than the data in each individual tiff of course).
I imagine there may be a way to setup some kind of vector file defining the tiff layout so I can quickly query that to find out which tile I need to render for a given bounding box?
You can either generate a big tiff file from the original tiffs with gdal_merge.py (you can find it in the python-gdal package on Debian or Ubuntu) or create a virtual file that mixes them all with gdal_merge-vrt. This second option saves space but probably is slower.