#Arraysync flickr photos code
#Arraysync flickr photos Offline

#Arraysync flickr photos code

If you want to verify yourself, I recommend using breakpoint debugging method, so that the input and output data of each line of code can be seen at a glance. Then normalize these data, calculate the coordinates of the upper left corner and the lower right corner of the prediction frame, and then do inverse normalization to change back to the original value range, and then do line splicing to output the results. Here, note that the original point of the central coordinate of the prediction frame is each anchor point, so boxStarts intercepts the orange area, while boxsizes intercepts the green area, centers is the center coordinate of the prediction box whose origin is the upper left corner of the picture. For each anchor point, there are 17 prediction attributes, and we only use the first five. The size of the table is 896 * 17, which means that there are 896 anchor points in total.

,īoxOutputs and anchor are two-dimensional tensors, which are actually two-dimensional arrays, that is, a table. Let's analyze the following functions function decodeBounds(īoxOutputs: tf.Tensor2D, anchors: tf.Tensor2D,Ĭonst boxStarts = tf.slice(boxOutputs,, ) Ĭonst centers = tf.add(boxStarts, anchors) Ĭonst boxSizes = tf.slice(boxOutputs,, ) Ĭonst boxSizesNormalized = tf.div(boxSizes, inputSize) Ĭonst centersNormalized = tf.div(centers, inputSize) Ĭonst halfBoxSize = tf.div(boxSizesNormalized, 2) Ĭonst starts = tf.sub(centersNormalized, halfBoxSize) Ĭonst ends = tf.add(centersNormalized, halfBoxSize) Ĭonst startNormalized = tf.mul(starts, inputSize) Ĭonst endNormalized = tf.mul(ends, inputSize) Modification and optimization of decodebonds function The implementation is simple, so I won't repeat it. off-line recognitionįor wechat applet, tensorflow JS provides two schemes of local storage model: local storage caching and saving as user files, and official detailed tutorials Here, I use the localStorage cache scheme here, because the loading of the model is in index TS, so it's in index Ts can be modified accordingly. In this way, the pre operation of improving the api is completed. Therefore, next, we need to add the relevant environment of typescript in the created face detect project.įor existing JavaScript applet projects, there are many ways to add typescript environment on the Internet course Because I use GitHub managed code, you can check Historical changes, know what changes I made.Īt the same time, in order to facilitate debugging, I made some modifications on the basis of the business: takephoto page in the previous chapter (I wrote down the image path, so I don't need to choose the image every time), and named it myapi. The interface can be run only after tsc converts the typescript code into JavaScript code. Typescript is a super class of JavaScript. OK! Next, let's improve the code one by one Improve the pre operation of apiįirst of all, the api source code is written in typescript. Improve performance, improve prediction speed and reduce memory consumption.

The input is processed by intercepting square to improve the prediction accuracy of face prediction frame and key points.Put all the information about tensorflow The operation of tfjs is put into the api, and the external call should not involve any operation on tfjs at all, so as to improve the code decoupling.For example, it is meaningless to predict the input of face input and receive HTMLVideoElement, HTMLImageElement, HTMLCanvasElement and other types, because wechat applet can't get them Some input parameters are meaningless.You can package the image preprocessing into a function, and then package the processing of the predicted results into a function, so as to improve the readability of the code

#Arraysync flickr photos Offline

It should be designed to save the data to the local after pulling it for the first time, and then it can be recognized offline Every time the program starts and api is called, the model weight and structure data should be pulled from the Internet.Improve the project business, realize real-time face detection and real-time calibration of prediction frame and key pointsįirst, we need to determine the direction of improvement:.Based on the shortcomings of the blazeface api in the previous chapter, improve the api code to make it more suitable for the front-end development of wechat applet.Then I made a simple selection of images for face recognition and calibration of face prediction frame and key points. In the last chapter, I made a detailed interpretation of the blazeface api and pointed out the shortcomings and improvement direction of the api code.