move images

RifeWang · Oct 29, 2024 · df67098 · df67098
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diff --git a/content/posts/arch/DDIA-partition-replication-redis-kafka-es.md b/content/posts/arch/DDIA-partition-replication-redis-kafka-es.md
@@ -6,7 +6,7 @@ description = "DDIA 分布式数据的分区与复制 - 基于 Redis、Kafka、E
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 tags = ["系统架构", "分布式"]
-categories = ["系统架构", "分布式"]
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diff --git a/content/posts/engineering/image-search-system.md b/content/posts/engineering/image-search-system.md
@@ -29,7 +29,7 @@ disableComments = true
 
 
 比如 Average Hash 算法的计算过程：
-![Average Hash](/images/image-average-hash.jpeg)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/image-average-hash.jpeg)
 
 1. Reduce size : 将原图压缩到 8 x 8 即 64 像素大小，忽略细节。
 2. Reduce color : 灰度处理得到 64 级灰度图像。
@@ -58,7 +58,7 @@ disableComments = true
 ### 传统特征
 在计算机视觉领域早期，创造了很多经典的手工设计的特征算法，比如 `SIFT`
 
-![](/images/image-sift.jpeg)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/image-sift.jpeg)
 
 如上图所示，通过 `SIFT` 算法提取出来的一系列的特征点。
 
@@ -71,7 +71,7 @@ disableComments = true
 ### CNN 特性
 人工智能兴起之后，基于 CNN 卷积神经网络提取图像特征越来越主流。
 
-![](/images/cnn.jpg)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/cnn.jpg)
 
 通过 CNN 提取出来的图像特征其实也是一个多维向量，比如使用 VGG16 模型提取特征可参考: https://keras.io/applications/#extract-features-with-vgg16
 

diff --git a/content/posts/engineering/image-search-total.md b/content/posts/engineering/image-search-total.md
@@ -33,13 +33,13 @@ disableComments = true
 
 比如：
 
-![image-pixel-example](/images/pixel-example.png)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/pixel-example.png)
 
 左图红色圈中的部分其实就是右图中一系列的像素点。
 
 再举例：
 
-![image-pixel2](/images/pixel2.png)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/pixel2.png)
 
 假设上图红色圈的部分是一幅图像，其中每一个独立的小方格就是一个像素点（简称像素），像素是最基本的信息单元，而这幅图像的大小就是 11 x 11 px 。
 
@@ -96,7 +96,7 @@ disableComments = true
 
 第一代搜图系统在特征提取上使用的是 `Perceptual hash` 即 `pHash` 算法，这个算法的基本原理是什么？
 
-![pHash](/images/phash.png)
+![](https://raw.githubusercontent.com/RifeWang/images/master/engineering/phash.png)
 
 如上图所示，`pHash` 算法就是对图像整体进行一系列变换最后构造 hash 值，而变换的过程可以理解为对图像进行不断的抽象，此时如果对另外一张相似内容的图像进行同样的整体抽象，那么其结果一定是非常接近的。
 

diff --git a/content/posts/influxdb/1.md b/content/posts/influxdb/1.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（一）"
 description = "时序数据库 InfluxDB（一）"
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diff --git a/content/posts/influxdb/2.md b/content/posts/influxdb/2.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（二）"
 description = "时序数据库 InfluxDB（二）"
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diff --git a/content/posts/influxdb/3.md b/content/posts/influxdb/3.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（三）"
 description = "时序数据库 InfluxDB（三）"
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diff --git a/content/posts/influxdb/4.md b/content/posts/influxdb/4.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（四）"
 description = "时序数据库 InfluxDB（四）"
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diff --git a/content/posts/influxdb/5.md b/content/posts/influxdb/5.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（五）"
 description = "时序数据库 InfluxDB（五）"
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diff --git a/content/posts/influxdb/6.md b/content/posts/influxdb/6.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（六）"
 description = "时序数据库 InfluxDB（六）"
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diff --git a/content/posts/influxdb/7.md b/content/posts/influxdb/7.md
@@ -5,8 +5,8 @@ title = "时序数据库 InfluxDB（七）"
 description = "时序数据库 InfluxDB（七）"
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diff --git a/content/posts/mysql/protocol-connection&replication.md b/content/posts/mysql/protocol-connection&replication.md
@@ -5,8 +5,8 @@ title = "解读 MySQL Client/Server Protocol: Connection & Replication"
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-tags = ["MySQL"]
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diff --git a/content/posts/mysql/sync-data-from-mysql.md b/content/posts/mysql/sync-data-from-mysql.md
@@ -5,8 +5,8 @@ title = "同步 MySQL 数据至 Elasticsearch/Redis/MQ 等的五种方式"
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-tags = ["MySQL"]
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diff --git a/static/images/cnn.jpg b/static/images/cnn.jpg
diff --git a/static/images/image-average-hash.jpeg b/static/images/image-average-hash.jpeg
diff --git a/static/images/image-sift.jpeg b/static/images/image-sift.jpeg
diff --git a/static/images/phash.png b/static/images/phash.png
diff --git a/static/images/pixel-example.png b/static/images/pixel-example.png
diff --git a/static/images/pixel2.png b/static/images/pixel2.png