We then connect these rooms by doors, select a room, and edit the speed modifier using the data calculated in the spreadsheet, as shown in Figure 6. We import the PyroSim model into Pathfinder and extract the floor, add the entrance and exit doors, then we divide the floor into rooms that correspond to the device locations we used to measure visibility. Typical calculation of speed factor using measured visibility Figure 5. In PyroSim, on the Analysis menu, click Simulation Parameters.Ĭlick the Output tab and set the Device write interval to a value that will result in a reasonable number of data points in the *.csv file (I used a 1 second interval for a 120 second simulation).Īfter the simulation, open the *.csv file and use the equation shown in Speed Slowing Factor to calculate the speed factor as a function of time based on the visibility.įigure 4 shows part of a spreadsheet that does this calculation.įigure 5 plots the speed factors. Visibility measurement devices output data to a *.csv file. Location of visibility measurement devices Perspective view of fire simulation Figure 3. The fire is a 200 MW fire that ramps to full value at 60 seconds. Occupants enter and then choose between two exits.Īt first, they use the nearest exit, but as the smoke slows their local velocity, they will choose to exit from the further exit.įigure 2 shows the geometry of fire simulation and Figure 3 shows the location of the devices that measure visibility. The fire model we will use for this example includes a room with a fire and a door that connects to a hallway. Speed factor as a function of visibility. The speed factor is tabulated and shown in the graph below. Their relation is given by Speed Slowing Factor: Speed Slowing Factor In the paper, they use this as absolute walking speed, but we will treat this as a factor that slows the speed of each occupant. 2018) gives a function of walking speed as a function of visibility. Walking Speed in Smoke: Representation in Life Safety Verifications ( Fridolf et al. The occupants will then slow their speed and adjust their paths using these speed reductions. ![]() The measurements will be located in the evacuation path.Ī local speed factor will be calculated as a function of time using the measured visibility at that location. To do this, we will measure visibility at different locations in the PyroSim/FDS model. We will use visibility to slow the walking speed of occupants. Walking Speed as a Function of Visibility This post provides an example of this process. In the interim, it is possible to run the fire simulation, monitor the reduced visibility due to smoke, and then manually use that information to slow and re-direct occupants in response to the fire. However, full coupling of the two simulations has not been completed. IntroductionĪ long-term development goal has been to fully couple the PyroSim/FDS fire results with Pathfinder evacuation.Ī first step toward this goal is the capability to display both fire and evacuation results in the same results viewer. You can follow along with this post by downloading the file smoke-effects.zip here. Created with software version: 2020.1 Table of Contents
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![]() Since an image file is included in the request, we will need to build a multipart request. ![]() Response_format (string) – must be url (default) or b64_jsonĪll of the default values work well for our purposes, so we’ll only need to supply the image and prompt fields when building our request. N (integer) – must be between 1 (default) and 10 Prompt (string) – required, max length 1000 characters Image (string) – required, must be square PNG < 4MB If you need help getting started with OpenAI API keys and other setup, please refer to our previous blog post.Īs described in the API reference, the image editing endpoint accepts 2-7 fields in the request body: Paint.xfermode = PorterDuffXfermode(_OUT)Ĭanvas.drawBitmap(imageBitmap, 0f, 0f, paint)įinally, once we’ve processed the masked image, and collected additional user input for the text prompt, we just need to build an OpenAI request to the image editing endpoint. Val maskBitmap = Bitmap.createBitmap(imageBitmap.width, imageBitmap.height, _8888) imageBitmap – reference to (cropped) original image bitmap customView – reference to custom view that tracks user input We can accomplish this with the help of the _OUT, which will delete pixels from the original image wherever the mask overlaps it. Once the user is satisfied with the mask they’ve drawn, then all that’s left to do is combine the original image with the mask drawing. To learn more about setting up custom views for drawing input, check out the documentation on custom drawing with Canvas and tracking touch input. We can do this by creating a simple custom view that overrides the onDraw and onTouchEvent methods to let the user color in the desired mask area. The next step is to let users apply a transparent mask to part of the captured image, which will allow OpenAI to fill in the masked area based on the text prompt. Request the necessary permissions –, , _AUDIO and _EXTERNAL_STORAGEĪdd a PreviewView so users can see a preview of their photo – this requires setting up a ProcessCameraProvider, a CameraSelector, and a Preview object with a SurfaceProviderĪdd a capture button and call takePhoto with the appropriate image file metadataįor more detailed implementation information, check out the documentation on image capture with CameraX. To capture images with CameraX, you must: CameraX is a Jetpack library designed to make camera app development easier, with lots of documentation, samples, and codelabs available for use. There are multiple ways you can do this in Android, but today we’ll be using CameraX. Our end goal will be to create a camera filter that lets users capture and edit their photos with OpenAI.īuild an OpenAI request to the image editing endpoint Now that we understand what the image editing endpoint requires for inputs, let’s try to build an Android app that targets this endpoint. Integrating image editing into an Android app To learn more, check out the API reference and use the editor portion of preview app. As with all the image APIs, the image input must be square and less than 4MB. The text prompt describes what the final edited image should look like, while the image provided should contain a masked transparent area to show which parts of the image can be edited. As mentioned above, this endpoint accepts two inputs: a text prompt and an image. In this blog post, we’ll be focusing specifically on the image edits endpoint. You can describe image style and content with lots of details, or provide more abstract concepts to see what the model will come up with! The preview app shows examples of prompts and generated images to give you an idea of where to start – here are some image prompts we tested out ourselves: If you’re curious about the output from the Images API, you can check out the DALL-E preview app and provide some of your own prompts. Variations: provide an existing image to generate random variations of the image.Edits: provide a text prompt and an existing image to generate an edited image.Generations: provide a text prompt to generate a new image.The Images API uses DALL-E models to interact with or generate images based on user prompts. Today, we’ll be focusing more on the Images API and how you can set up some interesting interactions in your Android apps, such as a camera filter with editing capabilities. ![]() Last week, we talked about the different API endpoint options and showed you some examples of how to use the Edits API. So far in this blog series, we’ve covered: We’re back with another blog post on using OpenAI in Android applications! ![]() Get through books, docs, articles, PDFs, emails - anything you read - faster. The Morning Tribe is a middle-grade action-adventure graphic novel that showcases young people protecting our critical environment and teaches that we are. 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Accept and go along with white supremacy and become “white” - or face the genocide, violence and discrimination visited on peoples who were Indigenous or of African descent.īut millions of people have said NO to that deal - choosing instead to organize and struggle against racism.A statue of Christopher Columbus is vandalized with the word ‘murderer.’ Nik Wheeler/Corbis via Getty Imagesįor many Indigenous peoples, Columbus Day is a controversial holiday. and offered to “ethnic immigrants” - who might be Slavic or Italian or other groups vilified as of color. Instead, that call was the sign of a new deal emerging in the U.S. 12 as the “discovery of America by Columbus,” in part because of “the divine care and guidance which has directed our history,” he was not taking a stand against xenophobia, white supremacy or colonization. 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