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1. Mac Software For Orthographic Projections 2019
2. Mac Software For Orthographic Projection Map
3. Mac Software For Orthographic Projection
4. Mac Software For Orthographic Projection Definition

The Orthographic projection is a commonly used projection by cartographers because it closely resembles the world as we know it exists – a three-dimensional sphere (sorry “Flat Earthers”). However, when MAPublisher displays the world in the Orthographic projection, it will still draw any points, lines or polygons that fall beyond the perspective’s horizon. This can create a “hollow Earth” or see-through effect that would cause confusion to the map reader.

Orthographic projection is the method of drawing an object wherein all perspective is eliminated. A good example is a set of blueprint plans. There are usually several views of the object in the drawing, A birds eye view (called the plan view ) lo. Download Orthographic Projection and enjoy it on your iPhone, iPad, iPod touch or Mac OS X 10.13.0 or later. ‎Very useful application for technical school students and engineers. Learn or test your skills in the orthographic (orthogonal) projection through interactive way on your smartphone or tablet. Orthographic projection, common method of representing three-dimensional objects, usually by three two-dimensional drawings in each of which the object is viewed along parallel lines that are perpendicular to the plane of the drawing. For example, an orthographic projection of a house typically. Orthographic Projection,FAA Written (Knowledge) Test Preparation. Private Pilot through ATP and mechanic. For Windows PCs, Mac, iPhone/iPad, Android, PocketPC, and MP3 Audio. Up to date for and complete with all charts and figures and professional, illustrated explanations.

Further confusion may arise depending on the order of the artwork within the layers, as some features that should be on the far side of the Earth may appear on top of features on the near side of the Earth. In order to prevent these inaccuracies, we need to remove the data beyond the horizon before transforming the data to the Orthographic projection. The following workflow is one example of how to overcome this limitation. It can be easily adapted for any orthographic projection with a custom latitude of origin and/or central meridian.

Step 1: Plot Your Centre Point

Import your world dataset and ensure the MAP View is in WGS84 (or you can use the world.mif file from the MAPublisher Tutorial Data folder).

Next, decide where you want the final orthographic map to be focused on. In this example, our focal point is 70°N, 100°E. Create a point layer, and plot your centre point using the MAP Point Plotter tool.

Step 2: Project to Azimuthal Equidistant Projection

Reproject the map to Azimuthal Equidistant (with a sphere-based datum). To do so, go to the MAP Views panel > Edit Map View. Enable Perform Coordinate System Transformation and click the No Coordinate System Specified hyperlink.

Under the Projected>World category, find Lambert Azimuthal Equidistant (Sphere), meter and click the Copy Object button to begin editing a copy of the existing Lambert Azimuthal Equidistant (Sphere), meter coordinate system.

Side Note – Why start with the Azimuthal Equidistant projection?

The straight-line distance between the central point on the map to any other point is the same as the straight-line distance between the two points along the earth’s surface.

Furthermore, you must use the ‘Sphere’ version of the Lambert Azimuthal Equidistant projection, because the radius is equal in all directions. The other version of the projection uses an ellipsoid datum which would require more complex formulas to solve the buffer distance in Step 3.

Next On the Definition tab, set the Central Meridian to 100, and the Latitude of Origin to 70. You may rename the custom projection on the Identification tab if you wish to reuse it later. Click OK and apply the projection.

Step 3: Create the Horizon Line

Create the horizon line from your custom centre point. Next, add a buffer of 10,002.5 km (see note below) around your focal point.

Curious about where the buffer value comes from?

Given that we know:
Earth’s radius using Authalic Sphere is 6,371,007m
The formula for the circumference of a sphere = 2πr
The distance from the focal point to the horizon in the Azimuthal Equidistant projection = 1/4 of Earth’s circumference = C × 1/4

We can first determine the circumference by:
= 2πr
= 2 × π × 6,371,007
= 40,010,000 m

Now that we know Earth’s circumference, we can determine the distance to the horizon by:
= C × 1/4
= 40,010,000 × 1/4
= 10,002,500 m or 10,002.5 km

Step 4: Convert Your Custom Horizon Line

Convert the resulting circle with four Bezier curve points to polylines using MAPublisher’s path utilities tool Convert Beziers to polylines. This is your horizon line.

Step 5: Back to WGS84 and Editing the Horizon line

Import another copy of your world file. If necessary, transform it to WGS84 and drag your horizon line into this MAP View. You can also hide or remove the Lambert Azimuthal Equidistant MAP View.

Use Illustrator’s Direct Selection Tool to select and delete the horizontal portion of the horizon polygon.

Next, use Illustrator’s Scissors Tool to cut the polyline at the artboard edge

Move the section of the curve remaining outside the artboard inside and to the opposite edge.

Resize the artboard to match the extents of the WGS84 MAP View. Use Illustrator’s Pen Tool rejoin the two lines into one. Continue using the Pen Tool to close the polygon by following along the edge of the artboard (result should look similar to the image below). Export the polygon area in case you need to add additional data at a later date.

Step 6: Crop Beyond the Horizon

Use the Crop to Shape tool to remove the features outside of the polygon.

Step 7: Reproject the Remaining Data

After cropping, go to the MAP View and Perform Coordinate System Transformation. Choose the Orthographic, meter projection, click Copy Object to begin editing the coordinate system and put in the custom central meridian and latitude of origin you chose back at the beginning of the workflow. Once the custom Orthographic coordinate system is created, complete the MAP View transformation.

Step 8: Remove Lingering Polar Points

If there are any lingering polar points, you can delete them from the horizon line manually using the Direct Selection Tool.

You’ve successfully projected your data into a custom orthographic projection without any overlapping features from the other side of the Earth.

The best part is, all of the map features retain their attributes, so you are free to continue applying MAP Themes, generating labels, and any of the other design tools at your disposal to make your map project a work of art.

About the Author

Jeff Cable is the Quality Assurance Lead for Avenza’s desktop applications. While he primarily focuses on MAPublisher, Jeff’s formal education is in remote sensing. He hopes to utilize this knowledge in the near future to help develop Geographic Imager into a more powerful geospatial image processing suite.

A Word of Warning

To understand the content of this lesson, you need to be familiar with the concept of matrix, transforming points from one space to another, perspective projection (including how coordinates of 3D points on the canvas are computed) and with the rasterization algorithm. Read the previous lessons and the lesson on Geometry if you are note familiar with these concepts (see links above).

Projection Matrices: What Are They?

Figure 1: multiplying a point by the perspective projection matrices gives another point which is the projection of P onto the canvas.

What are projection matrices? They are nothing more than 4x4 matrices, which are designed so that when you multiply a 3D point in camera space by one of these matrices, you end up with a new point which is the projected version of the original 3D point onto the canvas. More precisely, multiplying a 3D point by a projection matrix allows you to find the 2D coordinates of this point onto the canvas in NDC space.

Remember from the previous lesson, in NDC space the 2D coordinates of a point on the canvas are contained in the range [-1, 1]. This is at least the convention that is generally used by graphics API such as Direct3D or OpenGL.

Another way of saying it is that, multiplying a 3D point in camera-space by a projection matrix, has the same effect than all the series of operations we have been using in the previous lessons to find the 2D coordinates of 3D points in NDC space (this includes the perspective divide step and a few remapping operations to go from screen space to NDC space). In other words, this rather long code snippet which we have been using in the previous lessons:

Can be replaced with a single point-matrix multiplication. Assuming (M_{proj}) is a projection matrix, we can write:

The first version involves five variables: (near), (t), (b), (l) and (r) which are the near clipping plane, the top, bottom, left and right screen coordinates respectively. Remember that the screen coordinates are also computed normally from the near clipping plane as well as the camera angle-of-view (which, if you use a physically-based camera model, is calculated from a whole series of parameters such as the film gate size, the focal length, etc.). This is great, because it reduces a rather complex process into a simple point-matrix multiplication operation. The whole point of this lesson is to explain what (M_{proj}) is. Though looking at the two code snippet above, should somehow give you some ideas about what we will need in order to build this matrix. It seems like if this matrix replaces:

• The perspective divide operation,
• As well as the remapping of the point from screen space to NDC space.

We will somehow have to pack in this matrix all the different variables that are part of these two steps. The near clipping plane as well as the screen coordinates. We will explain this in detail in the next chapters. Though before we get there, let's explain one important thing about projection matrices and points.

First projection matrices are used to transform vertices or 3D points, not vectors. Using a projection matrix to transform vector doesn't make any sense. These matrices are used to project vertices of 3D objects onto the screen in order to create images of these objects that follow the rules of perspective. Remember from the lesson on geometry that a point is also a form of matrix. A 3D point can be defined as a [1x3] row vector matrix (1 row, 3 columns). Keep in mind that we use the row-major order convention on Scratchapixel. From the same lesson, we know that matrices can only be multiplied by each other if the number of columns of the left matrix equals the number of rows of the right matrix. In other words the matrices [mxn][nxk] can be multiplied by each other but the matrices [nxm][kxn] can't. Though if you multiply a 3D point with a 4x4 matrix, you get [1x3][4x4] and technically what this means is that this multiplication simply can't be done!

The trick to make this operation possible is to treat points not as [1x3] vectors but as [1x4] vectors. Then, you can multiply this [1x4] vector by a 4x4 matrix. Now as usual with matrix multiplication, the result of this operation is another [1x4] matrix. This [1x4] matrix or 4D points in a way are called in mathematics a points with homogeneous coordinates. A 4D point can't be used as 3D point unless its fourth coordinate is equal to 1. When this is the case, the first three coordinates of a 4D point can be used as the coordinates of a standard 3D Cartesian point. We will study this conversion process from Homogeneous to Cartesian in detail in the next chapter.

Whenever we multiply a point by a 4x4 matrix, points are always treated as 4D points, but for a reason we will explain in the next chapters, when you use 'conventional' 4x4 transformation matrices (the matrices we use the most often in CG to scale, translate or rotate objects for instance), this fourth coordinate doesn't need to be explicitly defined. But when a point is multiplied by a projection matrix, such as the perspective or orthographic projection matrices, this fourth coordinate needs to be dealt with explicitly. Which is why homogeneous coordinates are more often discussed within the context of projections than within the context of general transformations (even though projections are a form of transformation and even though you are also somehow using homogeneous coordinates when you deal with conventional transformation matrices. You only do so implicitly as we just explained).

This essentially means that, the one and only time you have to deal with 4D points in a renderer is when you work with projection matrices. The rest of the time, you will never have to deal with them at least explicitly.

Projection matrices are also generally only used by programs that implement the rasterization algorithm. In itself, this is not a problem at all, but in the algorithm, there is a process called clipping (we haven't talked about it at all in the lesson on rasterization) that happens while the point is being transformed by the projection matrix. You read correctly: clipping, which is a process we will describe in the next chapters, happens somewhere when the points are being transformed by the projection matrix. Not before, nor after. So in essence, the projection matrix is used indirectly to 'interleave' a process called clipping that is important in rendering (we will explain what clipping does in the next chapter). And this makes things even more confusing, because generally, when books get to the topic of projection matrices, they do also speak about clipping without really explaining why it is there, where it comes from and what relation it really has with the projection matrix (in fact it has none, it just happens that it is convenient to do it while the points are being transformed).

Mac Software For Orthographic Projections 2019

Where Are They Used and Why?

That is the real question. In fact in previous versions of the website, this lesson was not part of the basic rendering section. But we moved it because projection matrices is a very popular topic both on this website but also on specialized forums. They are still very confusing to many and if they are so popular, it must be for something. Not surprisingly, we also found the topic to be generally poorly documented, which is another one of these oddities, considering how important the subject matter is. Their popularity essentially comes from their use in real-time graphics APIs (such as OpenGL or Direct3D) which are themselves very popular due to their use in games and other common desktop graphics applications. What these APIs have in common is that they are used as an interface between your program and the GPU. Not surprisingly, and as something we already mentioned in the previous lesson, GPUs implement in their circuit the rasterization algorithm.

In old versions of the rendering pipeline used by GPUs (known as the fixed function pipeline), GPUs transformed points from camera to NDC space using a projection matrix. But the GPU didn't know how to build this matrix itself. As a programmer, it was your responsibility to build it and pass it on to the graphics card yourself. That essentially meant that you were required to know how to build the matrix in the first place.

Don't use this code though. It is only mentioned for reference and historical reasons. We are not supposed to use OpenGL that way anymore (these functionalities are now deprecated).

In the more recent 'programmable' rendering pipeline, the process is slightly different. First, the projection matrices doesn't convert points from camera space to NDC space directly, but it converts them into some intermediate space called clip space (though this was the same in the old 'fixed function' pipeline but we just didn't mention it to avoid confusing you). Don't worry too much about it for now. But to make it short, let's say that in clip space, points have homogeneous coordinates (do you remember the 4D points we talked about earlier?). In the modern programmable GPU rendering pipeline, this point-matrix multiplication (the transformation of vertices by the projection matrix) takes place in what we call a vertex shader. A vertex shader is nothing else than a small program if you wish, whose job is to transform vertices making up the 3D objects of your scene from camera space to clip space. A simple vertex shader takes a vertex as an input variable, a projection matrix (which is also a member variable of the shader) and set a pre-defined global variable (called gl_Position in OpenGL) as the result of the input vertex multiplied by the projection matrix. Note that gl_Position and the input vertex are both declared as vec4, in other words as points with homogeneous coordinates. Here is an example of a basic vertex shader:

Figure 2: the vertex shader transforms vertices to clip space.

This vertex shader is executed by the GPU to process every vertex in the scene. In a typical program using the OpenGL or Direct3D API, you store the vertex and the connectivity data (how are these vertices connected to each other to form the mesh's triangles) into the GPU's memory, and the GPU then processes this data (the vertex data in this particular case) to transform them from whatever space they are in when you pass them on to the GPU, to clip space. The space the vertices are in when they are processed by the vertex shader totally depends on you.

• You can either transform them yourself from wold space to camera space before you load them into the GPU's memory. Which means that when the vertices will be processed in the vertex shader, the coordinates will already be defined in camera space.
• Or you can leave the vertices in world space before you load them to the GPU's memory. When the vertices will be processed in the vertex shader, beside the projection matrix (which is often denoted P) you will also need to pass on to the shader, the world-to-camera matrix which is often denoted M in the GPU world, where M stands for the 'model-view' matrix. This is a matrix that concatenates both the transform from object space to world space and world space to camera space. We didn't really speak about object space so far, but this is simply the space an object is in before a transformation is applied to it. World space is the space the object is in after a 4x4 object-to-world matrix has been applied to it. You will first need to transform the vertex from world to camera space and then apply the projection matrix. In pseudo code you would get something like this:
  uniform mat4 P; // projection matrix uniform mat4 M; // model-view matrix (object to wold space * world space to camera space) in vec3 position; void main() { gl_Position = P * M * vec4(position, 1.0); }
  Remember that OpenGL, uses column vector notation (Scratchapixel uses row vector convention). Thus the point that is being transformed appears on the right, and you need to read the transformation from right to left. The vertex is first transformed by M, the model-view matrix, then P, the projection matrix.
  Many programmers prefer the second option. But what they usually typically do, is to concatenate the world-to-camera and the projection matrix into a single matrix.
  uniform mat4 PM; // projection matrix * model-view matrix in vec3 position; void main() { gl_Position = PM * vec4(position, 1.0); } int main(...) { ... // we use row-vector matrices Matrix44f projectionMatrix(...); Matrix44f modelViewMatrix(...); Matrix44f PM = modelViewMatrix * projectionMatrix; // GL uses column-vector matrices, thus we need to transpose our matrix PM.transpose(); // look for a variable called PM in the vertex shader. Glint loc = glGetUniformLocation(p, 'PM'); // set this shader variable with the content of our PM matrix glUniformMatrix4fv(loc, 1, false, &PM.m[0][0]); ... render(); ... return 0; }

Don't worry too much if you are not familiar with any graphics API. Or maybe you are familiar with Direct3D but not with OpenGL, though the principles are exactly the same, so you should be able to find similarities easily. Regardless, just try to understand what the code is doing. In the main CPU program, we just set the projection matrix and the model view matrix (which combines the object-to-world and the work-to-camera transform), and multiply these two matrices together so that rather than passing two matrices to the vertex shader, only one is needed. The OpenGL API requires that we find the location of the variable we are looking for in a shader using the glGetUniformLocation() call.

This process is central to the way objects are rendered on GPUs. Though this lesson is not an introduction to the GPU rendering pipeline, thus we don't want to get into too much detail at this point, but hopefully these explanations are enough for you to at least understand the concept. And regardless of whether you use the old or the new rendering pipeline, you are still somehow required to build the projection matrix yourself. And this is why projection matrices in general are so important and consequently why they are so popular on CG forums.

Now, note that in the new rendering pipeline, using a projection matrix is actually not absolutely required. All that is required is that you transform vertices from whatever space they are in, to clip space. Whether you use a matrix for this, or just write some code that does the same thing than what the matrix does, is not important as long as the result is the same. For example you could very much write:

Hopefully you understand what we mean when we say you can either use the matrix or just write some code that sets gl_Position like if you had used a projection matrix. Obviously, this wasn't possible in the fixed-function pipeline (because the concept of vertex shader didn't exist back then) but the point we are trying to make here, is that it is not strictly required any more to use projection matrices if you do not wish so. The advantage of being able to replace it with some other code, is that it gives you more flexibility on the way vertices are transformed. Though, in 99% of the cases you will be more likely to use a standard projection than not and even if you do not wish to use the matrix, you will still need to understand how the matrix is built in order to replace the point-matrix multiplication with some equivalent code (and transform the vertex coordinates one by one).

Figure 3: passing on the projection matrix and the vertex data from the CPU to the GPU pipeline.

All you need to remember really from this chapter is that:

• GPUs' rendering pipeline is based on the rasterization algorithm, in which vertices are typically transformed from camera space to clip space using projection matrices. GPUs are widespread and since projection matrices play a central role in the way images are produced on the GPU, they have also become an important topic of discussion and interest. Another way of saying this, is that if GPUs didn't exist or if they were based on the ray-tracing algorithm instead, we would probably don't care about projection matrices at all.
• In the programmable rendering pipeline, vertices are transformed to clip space in the vertex shader. The projection matrix is generally a member variable of the vertex shader. The matrix is set on the CPU before the shader is used (you can also pass to the shader all the variables that it needs to build the matrix itself, though this approach is not the most efficient).

All we need to now, is to learn how this matrix is built. And what clip space is.

Orthographic and Perspective Projection Matrix

Finally and to conclude this chapter, you may have noticed that the lesson is called 'The Perspective and Orthographic Projection Matrix', and you may wonder what the difference is between the two. First, let's say that they are both projections matrices. In other words, their function is to somehow project 3D points onto a 2D surface. We are already familiar with the perspective projection process. When the angle of view becomes very small (in theory when it tends to 0) then the four lines defining the frustum's pyramid get parallel to each other. In this particular configuration, there is no more foreshortening effect. In other words, an object size in the image stays constant regardless of its distance to camera. This is what we call an orthographic projection. Visually, we find an orthographic projection obviously less natural than a perspective projection, however this form of projection is nonetheless useful in some cases. Sim City is also an example of game that was rendered with an orthographic projection (image above). This gives the game a unique look.

Projection Matrix and Ray-Tracing

As we mentioned a few times already, in ray tracing, points don't need to be projected to the screen. Instead, rays emitted from the image plane are tested for intersections with the objects from the scene. Thus, projection matrices are not used in ray-tracing.

Mac Software For Orthographic Projection Map

What's Next?

Mac Software For Orthographic Projection

We found that the easiest way to learn about projection matrices and all the things we just talked about, is to start with learning how to build a very basic one. Once you understand what these homogeneous coordinates are, it will also become simpler to speak about clipping and other concepts which indirectly relate to projection matrices.

Mac Software For Orthographic Projection Definition

The simple perspective projection matrix that we will build in chapter three, won't be as sophisticated as the perspective projection matrix used in OpenGL or Direct3D (which we will also study in this lesson). As mentioned, the goal of chapter three is just to explain the principle behind projection matrices. In other words, how they work. We said that a projection matrix remaps vertices from whatever space they were in to clip space. Though, for the next two chapters, we will get back to our basis definition of the projection matrix which is that it converts points from camera space to NDC space. We will study the more generic case or more complex case in which points are actually converted to clip space in chapter four (in which we will explain how the OpenGL matrix works). For now, don't worry about clip space and keep this definition in mind: 'projection matrices convert vertices from camera space to NDC space' (though keep in mind that this definition is only temporary).