Here are the loaders, but I can't find an example of how to use the code on the internet. I have plenty of models as I'm a 3d modeler, but I don't know how to use the following link to import my 3ds models into javafx. Any help would be appreciated. Thanks.
http://www.interactivemesh.org/models/jfx3dimporter.html
Use the InteractiveMesh 3D Model Browser to load your model.
This will allow you to check that the 3D Model Importer and JavaFX 3D are capable of loading and rendering your 3ds model. This is a worthwhile check as both the 3D model importer and the JavaFX 3D API are currently early access releases which may have some issues or limitations displaying your particular models.
If the model browser application works with your models and you want to import the 3ds models into your own program, you could adapt a variation the answer to: How to create 3d shape from STL in JavaFX 8? As that answer deals with STL files, to import a 3ds file, substitute the TdsModelImporter for the STL importer. The rest of the test harness code remains the same (making appropriate adjustments for lighting, model scale, etc).
The interactive mesh model importer download includes api javadoc on usage of the TdsModelImporter for 3ds models.
For further questions, I advise you to contact InteractiveMesh directly.
To use the InteractiveMesh 3D Model Browser to load your 3Ds model.
ModelImporter tdsImporter = new TdsModelImporter();
tdsImporter.read(fileUrl);
Node[] tdsMesh = (Node[]) tdsImporter.getImport();
tdsImporter.close();
Related
I am trying to find a pre-trained model that will classify images based on if there is a human present in the photo or not.
You can use the models trained on the COCO dataset for this.
For example, for Pytorch you can have a look at the official documentation concerning the provided models here.
There are more variety of models if you give it a simple search both for Pytorch and other frameworks.
You can check out the COCO homepage if you need more information concerning the dataset and the tasks it supports.
You may also find These useful:
Detecting people using Yolo-OpenCV
Yolo object detection in pytorch
Another Yolo implementation in Pytorch
Similar question on ai.stackexchange
You can also utilize frameworks such as Detectorn2, mmdetection for these tasks.(Or Tensorflow's ObjectDetectionAPI , ect)
I am interested in creating a MF-USG model in flopy with a quadtree grid. I have created the disu using gridgen, but now I am stuck on how to assign properties and boundaries to the model, as the all examples of unstructured grids I can find are for MF6.
This is where I am at...
m = flopy.modflow.Modflow(model_name = model_name,version = "mfusg",structured=False,model_ws=model_ws)
m.dis = g.get_disu(m,nper = 1,perlen=1000,nstp = 100,tsmult=1.2,steady = False)
Can anyone point me to an example of building a MF-USG model with properties and BCs?
The Packages of MODFLOW-USG that flopy supports are DISU and SMS
See the Git-Hud FloPy Supported Packages page
I guess it will be hard if not impossible to find a fully MODFLOW-USG oriented example with flopy.
Changing to MODFLOW-6 might be a better alternative, specially now that it supports groundwater transport, subsidence and density driven flow sharing most of the theory behind.
I have been using the form recognizer service and form labeller tool, using the version 2 of the api, to train my models to read a set of forms. But i have the need to use more than one layout of the forms, not knowing which form (pdf) layout is being uploaded.
Is it as simple as labelling the different layouts within the same model. Or is there another way to identify which model is to be used with which form.?
any help greatly appreciated
This is a common request. for now, if the 2 forms styles are not that different, you could try to train one model and see if that model could correctly extract key/value. Another option is to train two different forms, you could write a simple classification program to decide which model to use.
Form Recognizer team is working on a feature to allow user just submit the document and it would pick the most appropriate model to analyze the document. Please stay tuned for our update.
thanks
I am working on a project that requires real-time conversion of images and videos to a 3d model using deep learning . Although I have found ways to get a voxel model, I feel polygon meshes give finer models. Is there any way I can do this using python's libraries . I would love to know about any previous works on this topic.
How to use the Inception V3 tensorflow module to train with our own requirement dataset images. Say for example I want to train the Inception V3 module with the different cool drinkcompany brands Pepsi, Sprite etc.. How it can be achieved..??
In the link https://github.com/tensorflow/models/tree/master/inception they have explained with the ImageNet. I am bit confused with that. Please explain the stuff.
I suggest you to check Transfer Learning. which consists in retrain only the last layers with new categories
How to Retrain Inception's Final Layer for New Categories
Baptiste's answer linking to the Tensorflow site is good. This is a very broad question and his link is a good start.
If you'd like something a little more step-by-step then the Tensorflow for Poets tutorial is basically the same but doesn't require the use of Bazel commands. It initially uses flowers but you can use whatever dataset you want.
There are many other examples and tutorials on the web. I found some more with a quick search including this page and this video.
Good Luck!