Hello fellow programmers. I have to say I just started drawing figures on x3d and I'm really needing to constroy a pyramid for a project of mine. Yet nothing I search seems to help me as I cannot understand the logic beyond how the figures are drawn just from looking at code from other people.
I managed to draw a cone using some keywords i found like : "bottomRadius", "height", etc...
But have no idea how I could convert something like this to a pyramid, what keywords should I be aware of that could help me draw the base triangle isntead of a circle like the cone does with the keyword bottomRadius?
Use IndexedFaceSet's coord to define points in space that you can connect (create triangles) using the coordIndex.
e.g.:
Shape {
geometry IndexedFaceSet {
coord Coordinate {
point [
1 0 0,
0 1 0,
0 0 1,
0 0 0,
]}
coordIndex [
0,1,2,-1 #face1
0,1,3,-1 #face2
0,2,3,-1 #face3
1,2,3,-1 #face4
]
color Color {
color [ 1 0 0,0 1 0,0 0 1,1 0 1,]}
colorPerVertex TRUE
}
}
There is no fundamental shape of a pyramid. The only fundamental shapes are box, cone, cylinder, and sphere. You will need to use one of the detailed geometry shapes: IndexedFaceSet or TriangleSet. These can be coded by hand where you determine the coordinates of all of the verticies. You can also use a modeling tool (Blender is open source) to construct the geometry and export it as X3D.
Related
I have a dataset that consists of an (x,y) pair and v which describes a value at (x,y). The data needs to produce a figure that looks like:
This was created by using a surface plot, changing the eye and up values, and then turning the aspectratio on the z-axis to 0.01:
layout= {{
...
aspectmode: "manual",
aspectratio: {x: "3", y: "1", z: ".01"},
scene: {
...
zaxis: {
visible: false
}
}
}}
Notice that the x/y axes are still raised and awkwardly placed. I have two parts to my question:
Is there a better graph to show this data like this using Plotly? The end product needs to be the same, but the way I get there can change.
If this is the best way, how do I "lower" the x/y axes to make it look like a 2D plot?
The original reason I went the route of using a surface plot is because of Matlab. When building a surface plot and rotating it to one of the planes (x/y/z), it will essentially turn into a 2D figure.
After a good walk and looking at the documentation, using:
layout = {{
...
scene: {
...
xaxis: {
...
tickangle: 0
}
}
}}
Removed the '3D' effects. I also changed the aspectratio of z to be: .001
I am a beginner to deep learning and I am working with Keras built on top of Tensorflow. I am trying to using RGB images (540 x 360) resolution to predict bounding boxes.
My labels are binary (black/white) 2 dimensional np array of dimensions (540, 360) where all pixels are 0 except for the box edges which are a 1.
Like this:
[[0 0 0 0 0 0 ... 0]
[0 1 1 1 1 0 ... 0]
[0 1 0 0 1 0 ... 0]
[0 1 0 0 1 0 ... 0]
[0 1 1 1 1 0 ... 0]
[0 0 0 0 0 0 ... 0]]
There can be more than one bounding box in every picture. A typical image could look like this:
So, my input has the dimension (None, 540, 360, 3), output has dimensions (None, 540, 360) but if I add an internal array I can change the shape to (None, 540, 360, 1)
How would I define a CNN model such that my model could fit this criteria? How can I design a CNN with these inputs and outputs?
You have do differentiate between object detection and object segmentation. While both can be used for similar problems, the underlying CNN architectures look very different.
Object detection models use a CNN classification/regression architecure, where the output refers to the coordinates of the bounding boxes. It's common practice to use 4 values belonging to vertical center, horizontal center, width and height of each bounding box. Search for Faster R-CNN, SSD or YOLO to find popular object detection models for keras. In your case you would need to define a function that converts the current labels to the 4 coordinates I mentioned.
Object segmentation models commonly use an architecture referred to as encoder-decoder networks, where the original image is scaled down and compressed on the first half and then brought back to it's original resolution to predict a full image. Search for SegNet, U-Net or Tiramisu to find popular object segmentation models for keras. My own implementation of U-Net can be found here. In your case you would need to define a custom function, that fills all the 0s inside your bounding boxes with 1s. Understand that this solution will not predict bounding boxes as such, but segmentation maps showing regions of interest.
What is right for you, depends on what precisely you want to achieve. For getting actual bounding boxes you want to perform an object detection. However, if you're interested in highlighting regions of interest that go beyond rectangle windows a segmentation may be a better fit. In theory, you can use your rectangle labels for a segmentation, where the network will learn to create better masks than the inaccurate segmentation of the ground truth, provided you have enough data.
This is a simple example of how to write intermediate layers to achieve the output. You can use this as a starter code.
def model_360x540(input_shape=(360, 540, 3),num_classes=1):
inputs = Input(shape=input_shape)
# 360x540x3
downblock0 = Conv2D(32, (3, 3), padding='same')(inputs)
# 360x540x32
downblock0 = BatchNormalization()(block0)
downblock0 = Activation('relu')(block0)
downblock0_pool = MaxPooling2D((2, 2), strides=(2, 2))(block0)
# 180x270x32
centerblock0 = Conv2D(1024, (3, 3), padding='same')(downblock0_pool)
#180x270x1024
centerblock0 = BatchNormalization()(center)
centerblock0 = Activation('relu')(center)
upblock0 = UpSampling2D((2, 2))(centerblock0)
# 180x270x32
upblock0 = concatenate([downblock0 , upblock0], axis=3)
upblock0 = Activation('relu')(upblock0)
upblock0 = Conv2D(32, (3, 3), padding='same')(upblock0)
# 360x540x32
upblock0 = BatchNormalization()(upblock0)
upblock0 = Activation('relu')(upblock0)
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(upblock0)
#360x540x1
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=RMSprop(lr=0.001), loss=bce_dice_loss, metrics=[dice_coeff])
return model
The downblock represents the block of layers which perform downsampling(MaxPooling2D).
The centerblock has no sampling layer.
The upblock represents the block of layers which perform up sampling(UpSampling2D).
So here you can see how (360,540,3) is being transformed to (360,540,1)
Basically, you can add such blocks of layers to create your model.
Also check out Holistically-Nested Edge Detection which will help you better with the edge detection task.
Hope this helps!
I have not worked with keras but I will provide a solution approach in more generalized way which can be used on any framework.
Here is full procedure.
Data preparation: I know your labels are edges of boxes which will also work but i will recommend that instead of edges you prepare dataset marking complete box like given in sample (I have marked for two boxes). Now your dataset have three classes (Box,Edges of box and background). Create two lists, Image and label.
Get a pre-trained model (RESNET-51 recommended) solver and train prototxt from here, Remove fc1000 layer and add de-convolution/up-sampling layers to match your input size. use paddding in first layer to make it square and crop in deconvolution layer to match input output dimensions.
Transfer weights from previously trained network (Original) and train your network.
Test your dataset and create bounding boxes using detected blobs.
I have a SCNPlane that I created in the SceneKit editor and I want 1 side of the plane to have a certain image and the other side of the plane to have another image. How do I do that in the Scenekit editor
So far what I've tried to do is adding 2 materials to the plane. I tried adding 2 materials and unchecking double-sided but that doesn't work.
Any help would be appreciated!
Per the SCNPlane docs:
The surface is one-sided. Its surface normal vectors point in the positive z-axis direction of its local coordinate space, so it is only visible from that direction by default. To render both sides of a plane, ether set the isDoubleSided property of its material to true or create two plane geometries and orient them back to back.
That implies a plane has only one material — isDoubleSided is a property of a material, letting that one material render on both sides of a surface, but there's nothing you can do to one material to turn it into two.
If you want a flat surface with two materials, you can arrange two planes back to back as the doc suggests. Make them both children of a containing node and you can then use that to move them together. Or you could perhaps make an SCNBox that's very thin in one dimension.
Very easy to do in 2022.
It's very easy and common to do this, you just add the rear as a child.
To be clear the node (and the rear you add) should both use the single-sided shader.
Obviously, the rear you add points in the other direction!
Do note that they are indeed in "exactly the same place". Sometimes folks new to 3D mesh think the two meshes would need to be "a little apart", not so.
public var rear = SCNNode()
private var theRearPlane = SCNPlane()
private func addRear() {
addChildNode(rear)
rear.eulerAngles = SCNVector3(0, CGFloat.pi, 0)
theRearPlane. ... set width, height etc
theRearPlane.firstMaterial?.isDoubleSided = false
rear.geometry = theRearPlane
rear.geometry?.firstMaterial!.diffuse.contents = .. your rear image/etc
}
So ...
///Double-sided sprite
class SCNTwoSidedNode: SCNNode {
public var rear = SCNNode()
private var thePlane = SCNPlane()
override init() {
super.init()
thePlane. .. set size, etc
thePlane.firstMaterial?.isDoubleSided = false
thePlane.firstMaterial?.transparencyMode = .aOne
geometry = thePlane
addRear()
}
Consuming code can just refer to .rear , for example,
playerNode. ... the drawing of the Druid
playerNode.rear. ... Druid rules and abilities text
enemyNode. ... the drawing of the Mage
enemyNode.rear. ... Mage rules and abilities text
If you want to do this in the visual editor - very easy
It's trivial. Simply add the rear as a child. Rotate the child 180 degrees on Y.
It's that easy.
Make them both single-sided and put anything you want on the front and rear.
Simply move the main one (the front) normally and everything works.
How to get Circle radius in meters
May be this is existing question, but i am not getting proper result. I am trying to create Polygon in postgis with same radius & center getting from openlayers circle.
To get radius in meters I followed this.
Running example link.
var radiusInMeters = circleRadius * ol.proj.METERS_PER_UNIT['m'];
After getting center, radius (in meters) i am trying to generate Polygon(WKT) with postgis (server job) & drawing that feature in map like this.
select st_astext(st_buffer('POINT(79.25887485937808 17.036647682474722 0)'::geography, 365.70644956827164));
But both are not covering same area. Can any body please let me know where i am doing wrong.
Basically my input/output to/from Circle will be in meters only.
ol.geom.Circle might not represent a circle
OpenLayers Circle geometries are defined on the projected plane. This means that they are always circular on the map, but the area covered might not represent an actual circle on earth. The actual shape and size of the area covered by the circle will depend on the projection used.
This could be visualized by Tissot's indicatrix, which shows how circular areas on the globe are transformed when projected onto a plane. Using the projection EPSG:3857, this would look like:
The image is from OpenLayer 3's Tissot example and displays areas that all have a radius of 800 000 meters. If these circles were drawn as ol.geom.Circle with a radius of 800000 (using EPSG:3857), they would all be the same size on the map but the ones closer to the poles would represent a much smaller area of the globe.
This is true for most things with OpenLayers geometries. The radius, length or area of a geometry are all reported in the projected plane.
So if you have an ol.geom.Circle, getting the actual surface radius would depend on the projection and features location. For some projections (such as EPSG:4326), there would not be an accurate answer since the geometry might not even represent a circular area.
However, assuming you are using EPSG:3857 and not drawing extremely big circles or very close to the poles, the Circle will be a good representation of a circular area.
ol.proj.METERS_PER_UNIT
ol.proj.METERS_PER_UNIT is just a conversion table between meters and some other units. ol.proj.METERS_PER_UNIT['m'] will always return 1, since the unit 'm' is meters. EPSG:3857 uses meters as units, but as noted they are distorted towards the poles.
Solution (use after reading and understanding the above)
To get the actual on-the-ground radius of an ol.geom.Circle, you must find the distance between the center of the circle and a point on it's edge. This could be done using ol.Sphere:
var center = geometry.getCenter()
var radius = geometry.getRadius()
var edgeCoordinate = [center[0] + radius, center[1]];
var wgs84Sphere = new ol.Sphere(6378137);
var groundRadius = wgs84Sphere.haversineDistance(
ol.proj.transform(center, 'EPSG:3857', 'EPSG:4326'),
ol.proj.transform(edgeCoordinate, 'EPSG:3857', 'EPSG:4326')
);
More options
If you wish to add a geometry representing a circular area on the globe, you should consider using the method used in the Tissot example above. That is, defining a regular polygon with enough points to appear smooth. That would make it transferable between projections, and appears to be what you are doing server side. OpenLayers 3 enables this by ol.geom.Polygon.circular:
var circularPolygon = ol.geom.Polygon.circular(wgs84Sphere, center, radius, 64);
There is also ol.geom.Polygon.fromCircle, which takes an ol.geom.Circle and transforms it into a Polygon representing the same area.
My answer is a complement of the great answer by Alvin.
Imagine you want to draw a circle of a given radius (in meters) around a point feature. In my particular case, a 200m circle around a moving vehicle.
If this circle has a small diameter (< some kilometers), you can ignore earth roudness. Then, you can use the marker "Circle" in the style function of your point feature.
Here is my style function :
private pointStyle(feature: Feature, resolution: number): Array<Style> {
const viewProjection = map.getView().getProjection();
const coordsInViewProjection = (<Point>(feature.getGeometry())).getCoordinates();
const longLat = toLonLat(coordsInViewProjection, viewProjection);
const latitude_rad = longLat[1] * Math.PI / 180.;
const circle = new Style({
image: new CircleStyle({
stroke: new Stroke({color: '#7c8692'});,
radius: this._circleRadius_m / (resolution / viewProjection.getMetersPerUnit() * Math.cos(latitude_rad)),
}),
});
return [circle];
}
The trick is to scale the radius by the latitude cosine. This will "locally" disable the distortion effect we can observe in the Tissot Example.
I have plotted a contour map but i need to make some improvements. This is the structure of the data that are used:
str(lon_sst)
# num [1:360(1d)] -179.5 -178.5 -177.5 -176.5 -175.5 ...
str(lat_sst)
# num [1:180(1d)] -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 -83.5 -82.5 -81.5 -80.5 ...
dim(cor_Houlgrave_SF_SST_JJA_try)
# [1] 360 180
require(maps)
maps::map(database="world", fill=TRUE, col="light blue")
maps::map.axes()
contour(x=lon_sst, y=lat_sst, z=cor_Houlgrave_SF_SST_JJA_try[c(181:360, 1:180),],
zlim=c(-1,1), add=TRUE)
par(ask=TRUE)
filled.contour(x = lon_sst, y=lat_sst,
z=cor_Houlgrave_SF_SST_JJA_try[c(181:360, 1:180),],
zlim=c(-1,1), color.palette=heat.colors)
Because most of the correlations are close to 0, it is very hard to see the big ones.
Can i make it easier to see, or can i change the resolution so i can zoom it in? At the moment the contours are too tightly spaced so I can't see what the contour levels were.
Where can i see the increment, i set my range as (-1,1), i don't know how to set the interval manually.
Can someone tell me how to plot a specific region of the map, like longitude from 100 to 160 and latitude from -50 to -80? I have tried to replace lon_sst and lat_sst, but it has a dimension error. Thanks.
To answer 1 and 3 which appear to be the same request, try:
maps::map(database="world", fill=TRUE, col="light blue",
ylim=c(-80, -50), xlim=c(100,160) )
To address 2: You have a much smaller range than [-1,1]. The labels on those contour lines are numbers like .06, -.02 and .02. The contour function will accept either an 'nlevels' or a 'levels' argument. Once you have a blown up section you can use that to adjust the z-resolution of contours.
contourplot in the lattice package can also produce these types of contour plots, and makes it easy to both contour lines and fill colours. This may or may not suit your needs, but by filling contour intervals, you can do away with the text labels, which can get a little crowded if you want to have high resolution contours.
I don't have your sea surface temperature data, so the following figure uses dummy data, but you should get something similar. See ?contourplot and ?panel.levelplot for possible arguments.
For your desired small scale plot, overlaying the world map plot is probably inappropriate, especially considering that the area of interest is in the ocean.
library(lattice)
contourplot(cor_Houlgrave_SF_SST_JJA_try, region=TRUE, at=seq(-1, 1, 0.25),
labels=FALSE, row.values=lon_sst, column.values=lat_sst,
xlim=c(100, 160), ylim=c(-80, -50), xlab='longitude', ylab='latitude')
Here, the at argument controls the position at values at which contour lines will be calculated and plotted (and hence the number of breaks in the colour ramp). In my example, contour lines are provided at -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75 and 1 (with -1 being the background). Changing to at=seq(-1, 1, 0.5), for example, would produce contour lines at -0.5, 0, 0.5, and 1.