更新: | 2017-05-09 |
---|---|
作者: | @voluntas |
バージョン: | 3.14 |
URL: | http://voluntas.github.io/ |
MQTT をググって調べた人向け
diff --git a/firmware/RCS620S/RCS620S.h b/firmware/RCS620S/RCS620S.h | |
index 9792859..f2a17c0 100644 | |
--- a/firmware/RCS620S/RCS620S.h | |
+++ b/firmware/RCS620S/RCS620S.h | |
@@ -16,6 +16,15 @@ | |
#define RCS620S_MAX_CARD_RESPONSE_LEN 254 | |
#define RCS620S_MAX_RW_RESPONSE_LEN 265 | |
+ | |
+/* |
更新: | 2017-05-09 |
---|---|
作者: | @voluntas |
バージョン: | 3.14 |
URL: | http://voluntas.github.io/ |
MQTT をググって調べた人向け
function exportSpreadsheet() { | |
//All requests must include id in the path and a format parameter | |
//https://docs.google.com/spreadsheets/d/{SpreadsheetId}/export | |
//FORMATS WITH NO ADDITIONAL OPTIONS | |
//format=xlsx //excel | |
//format=ods //Open Document Spreadsheet | |
//format=zip //html zipped | |
// Copyright 2015-2016 Espressif Systems (Shanghai) PTE LTD | |
// | |
// Licensed under the Apache License, Version 2.0 (the "License"); | |
// you may not use this file except in compliance with the License. | |
// You may obtain a copy of the License at | |
// http://www.apache.org/licenses/LICENSE-2.0 | |
// | |
// Unless required by applicable law or agreed to in writing, software | |
// distributed under the License is distributed on an "AS IS" BASIS, |
""" | |
Convert YouTube subtitles(vtt) to human readable text. | |
Download only subtitles from YouTube with youtube-dl: | |
youtube-dl --skip-download --convert-subs vtt <video_url> | |
Note that default subtitle format provided by YouTube is ass, which is hard | |
to process with simple regex. Luckily youtube-dl can convert ass to vtt, which | |
is easier to process. |
<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset='UTF-8'> | |
<meta http-equiv='X-UA-Compatible' content='IE=edge'> | |
<meta name='viewport' content='width=device-width, initial-scale=1'> | |
<style> | |
* {margin: 0} | |
</style> |
Combining TensorFlow for Poets and TensorFlow.js.
Retrain a MobileNet V1 or V2 model on your own dataset using the CPU only.
I'm using a MacBook Pro without Nvidia GPU.
MobileNets can be used for image classification. This guide shows the steps I took to retrain a MobileNet on a custom dataset, and how to convert and use the retrained model in the browser using TensorFlow.js. The total time to set up, retrain the model and use it in the browser can take less than 30 minutes (depending on the size of your dataset).
Example app - HTML/JS and a retrained MobileNet V1/V2 model.
import lcd | |
import utime | |
import sys | |
from machine import I2C | |
from Maix import GPIO | |
from fpioa_manager import * | |
i2c = I2C(I2C.I2C0, freq=400000, scl=28, sda=29) | |
# And a short delay to wait until the I2C port has finished activating. | |
utime.sleep_ms(100) |
// Copyright (c) 2019 aNoken | |
#include <M5StickC.h> | |
HardwareSerial serial_ext(2); | |
typedef struct { | |
uint32_t length; | |
uint8_t *buf; | |
} jpeg_data_t; | |
jpeg_data_t jpeg_data; |