ImageSearch | 基于百度AI 的图片搜索、以图搜图、相似图查找

I have seen the below used for this same exact reason

In your code, you asked to put all titles in the same div indeed:

There seems to be nothing in your code that empties the HTML of resultContainer, and the way you modify it is by using append which only adds to it.

You can do a custom function, but i think the easiest way would be to just add a line that clears the HTML content of said container after you know you successfully fetched the data (like just before data.forEach((beer) => {).

I don't know if this line would work but just as an example something along resultContainer.innerHTML = "" should do fine to reset the list.

If you require more logic you may aswell do a custom function to call here for readability, but i don't think it is required by default.

use this:

Trio won't directly parallelize CPU-bound code like this. Being an "async framework" means that it just uses a single CPU thread, while parallelizing I/O and networking operations. If you insert some calls to await trio.sleep(0) then that will let Trio interleave the image searching with other tasks, but it won't make the image searching any faster.

What you might want to do, though, is use a separate thread. I guess your code is probably spending most of its time in opencv, and opencv probably drops the GIL? So using threads will probably let you run your code across multiple CPUs at once, while also letting other async tasks run at the same time. To manage threads like this, Trio lets you do await trio.to_thread.run_sync(some_sync_function, *args), which runs some_sync_function(*args) in a thread. If you run multiple calls like this simultaneously in a nursery, then you'll use multiple threads.

There is one major gotcha to watch out for with threads, though: once a trio.to_thread.run_sync call starts, it can't be cancelled, so timeouts etc. won't take effect until after the call finishes. To work around this you might want to make sure that individual calls don't block for too long.

Also, a side note on style: functions made for Trio usually don't take timeout= arguments like that, because if the user wants to add a timeout, they can write the with block themselves around your function just as easily as passing an argument. So this way you don't have to clutter up APIs with timeout arguments everywhere.

As per my experience, You have faced this issue because you're added large size images for all resolution 1X, 2X and 3X. You need to scale it down to a size more appropriate for the navigation bar.

You need to scale it down to a size more appropriate for the navigation bar. Please have look image sizes for UINavigationBar

For 1X image Size: 24X24

For 2X image Size: 48X48

For 3X image Size: 72X72

If you want to go with the same images then you need to do below changes in your code.

You just need to add UIImageView inside the UIView then everything works as expected.

edited 03/08/2020 to add pointers for visual search

Since you seem interested both in the tasks of object detection (input an image, and return bounding boxes with object classes) and visual search (input an image and return relevant images) let me give you pointers for both :)

For object detection you have 3 options:

• Using the managed service Amazon Rekognition Custom Labels. The key benefits of this service is that (1) it doesn't require writing ML code, as the service runs autoML internally to find the best model, (2) it is very flexible in terms of interaction (SDKs, console), data loading and annotation and (3) it can work even with small datasets (typically a few hundred images or less).
• Using SageMaker Object Detection model (documentation, demo). In this option, the model is also already written (SSD architecture with Resnet or VGG backbone) and you just need to choose or tune hyperparameters
• Using your own model on Amazon SageMaker. This could be your own code in docker, or code from an ML framework in a SageMaker ML Framework container. There are such containers for Pytorch, Tensorflow, MXNet, Chainer and Sklearn. In terms of model code, I recommend considering gluoncv, a compact python computer vision toolkit (based on mxnet backend) that comes with many state-of-the-art models and tutorials for object detection

The task of visual search requires more customization, since you need to provide the info of (1) what you define as search relevancy (eg is it visual similarity? or object complementarity? etc) and (2) the collection among which to search. If all you need is visual similarity, a popular option is to transform images into vectors with a pre-trained neural network and run kNN search between the query image and the collection of transformed images. There are 2 tutos showing how to build such systems on AWS here:

• Blog Post Visual Search on AWS (MXNet resnet embeddings + SageMaker kNN)
• Visual Search on MMS demo (MXNet resnet embeddings + HNSW kNN on AWS Fargate)

The Python implementation adds automatically the Bing version, you need to use

Your while loop is unreachable code.
The code execution stops when the first hotkey label is encountered. This is called the Auto-execute Section.

Move your hotkey definition to be at the very bottom.
(All hotkeys always defined by hotkeys labels get always created regardless of being in the auto-execute section or not)