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1. FAST.AI TABULAR DATA PASS NP.ARRAY HOW TO
2. FAST.AI TABULAR DATA PASS NP.ARRAY PATCH
3. FAST.AI TABULAR DATA PASS NP.ARRAY CODE

Return StreamingResponse(iterfile(), media_type='image/png') Instead, you should use use FileResponse), file-like objects, such as those created by open(), are normal iterators thus, you can return them directly in a StreamingResponse, as described in the documentation and as shown below (if you find yield from f being rather slow when using StreamingResponse, please have a look at this answer for solutions): get_image(): It doesn't generally apply to serving images.Įven if you would like to stream an image file that is saved on disk (which you should rather not, unless it is a rather large file that can't fit into memory. That canĪpply to stuff like serving the results of slow database queries, but

Your output ahead of time, and you don't want to wait to collect itĪll to find out before you start sending it to the client. As described in this answer:Ĭhunked transfer encoding makes sense when you don't know the size of

StreamingResponse streams by iterating over the chunks provided by your iter() function (if Content-Length is not set in the headers-unlike StreamingResponse, other Response classes set that header for you, so that the browser will know where the data ends). If you would like to display (or download) the image using a JavaScript interface, such as Fetch API or Axios, have a look at the answers here and here.Īs for the StreamingResponse, if the numpy array is fully loaded into memory from the beginning, StreamingResponse is not necessary at all. # save image to an in-memory bytes buffer Even though the buffered stream is garbage collected when goes out of scope, it is generally better to call close() or use the with statement, as shown here and below. Next, write the image to a buffered stream, i.e., BytesIO, and use the getvalue() method to get the entire contents of the buffer. However, if the same image is going to be served multiple times, one could load the image only once at startup and store it on the app instance, as described in this answer). You can load an image from disk using Image.open, or use omarray to load an in-memory image ( Note: For demo purposes, when the case is loading the image from disk, the below demonstrates that operation inside the route.

FAST.AI TABULAR DATA PASS NP.ARRAY PATCH

# Function to create a sample RGB imageĪrr = np.zeros((h, w, 3), dtype=np.uint8)Īrr = # red patch in upper left

FAST.AI TABULAR DATA PASS NP.ARRAY CODE

For the purposes of this demo, the below code is used to create the in-memory sample image (numpy array), which is based on this answer.

FAST.AI TABULAR DATA PASS NP.ARRAY HOW TO

The below examples show how to convert an image loaded from disk, or an in-memory image (in the form of numpy array), into bytes (using either PIL or OpenCV libraries) and return them using a custom Response.