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swift - 在 Swift 4 中本地保存图像并写入 api 的性能问题

问题描述

我们将图像转换为字节数组，然后将它们作为字符串保存在 JSON 文件中。这发生在文件系统本地。从文件系统读取这个 JSON 文件或将其写入 API 时，性能真的很差。对于大量图像，您必须等待 >10 秒，然后才能将其加载到 ViewController 中。有没有更好的方法或库（Swift4）来处理这个问题？我们已经将质量压缩到 0.2。有人告诉我们使用base64，这是要走的路吗？

我们像这样对图像进行编码：ImagesBytes : [[UInt8]]? 放入带有一些 TextField 数据的 JSON 文件。

这是代码

public var formImagesBytes : [[UInt8]]? = []所以这是在模型中作为一个领域。该模型是具有字段（字符串）和图像数组的表单，该数组是 UInt8 数组。这将首先在本地保存，同时像这样解码：

formImagesBytes = try container.decodeIfPresent([[UInt8]].self, forKey: .formImagesBytes)

然后我们对所有数据进行 JSON 编码，包括表单的值和图像字节数组。然后我们将它与 Filemanager 一起添加到设备上的本地存储中。我们可以打开这个文件，在这种情况下它包括表单数据和一个字节数组。

当从视图控制器的本地存储中加载数据时，我们再次读取此本地文件并将其解码为表单模型。但是处理这个文件需要太多时间。我希望很清楚。我的问题实际上不是关于代码，而是关于：是否有最佳实践或更好的方法将图像写入此 JSON 文件，以便让应用程序更好地执行。

示例输出：

{"Images":[[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,144,0,144,0,0,255,225,0,140,69,120,105,102,0,0,77,77,0,42,0,0,0,8,0,5,1,18,0,3,0,0,0,1,0,1,0,0,1,26,0,5,0,0,0,1,0,0,0,74,1,27,0,5,0,0,0,1,0,0,0,82,1,40,0,3,0,0,0,1,0,2,0,0,135,105,0,4,0,0,0,1,0,0,0,90,0,0,0,0,0,0,0,144,0,0,0,1,0,0,0,144,0,0,0,1,0,3,160,1,0,3,0,0,0,1,0,1,0,0,160,2,0,4,0,0,0,1,0,0,1,82,160,3,0,4,0,0,0,1,0,0,0,253,0,0,0,0,255,237,0,56,80,104,111,116,111,115,104,111,112,32,51,46,48,0,56,66,73,77,4,4,0,0,0,0,0,0,56,66,73,77,4,37,0,0,0,0,0,16,212,29,140,217,143,0,178,4,233,128,9,152,236,248,66,126,255,192,0,17,8,0,253,1,82,3,1,34,0,2,17,1,3,17,1,255,196,0,31,0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,2,3,4,5,6,7,8,9,10,11,255,196,0,181,16,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,125,1,2,3,0,4,17,5,18,33,49,65,6,19,81,97,7,34,113,20,50,129,145,161,8,35,66,177,193,21,82,209,240,36,51,98,114,130,9,10,22,23,24,25,26,37,38,39,40,41,42,52,53,54,55,56,57,58,67,68,69,70,71,72,73,74,83,84,85,86,87,88,89,90,99,100,101,102,103,104,105,106,115,116,117,118,119,120,121,122,131,132,133,134,135,136,137,138,146,147,148,149,150,151,152,153,154,162,163,164,165,166,167,168,169,170,178,179,180,181,182,183,184,185,186,194,195,196,197,198,199,200,201,202,210,211,212,213,214,215,216,217,218,225,226,227,228,229,230,231,232,233,234,241,242,243,244,245,246,247,248,249,250,255,196,0,31,1,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,2,3,4,5,6,7,8,9,10,11,255,196,0,181,17,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,119,0,1,2,3,17,4,5,33,49,6,18,65,81,7,97,113,19,34,50,129,8,20,66,145,161,177,193,9,35,51,82,240,21,98,114,209,10,22,36,52,225,37,241,23,24,25,26,38,39,40,41,42,53,54,55,56,57,58,67,68,69,70,71,72,73,74,83,84,85,86,87,88,89,90,99,100,101,102,103,104,105,106,115,116,117,118,119,120,121,122,130,131,132,133,134,135,136,137,138,146,147,148,149,150,151,152,153,154,162,163,164,165,166,167,168,169,170,178,179,180,181,182,183,184,185,186,194,195,196,197,198,199,200,201,202,210,211,212,213,214,215,216,217,218,226,227,228,229,230,231,232,233,234,242,243,244,245,246,247,248,249,250,255,219,0,67,0,28,28,28,28,28,28,48,28,28,48,68,48,48,48,68,92,68,68,68,68,92,116,92,92,92,92,92,116,140,116,116,116,116,116,116,140,140,140,140,140,140,140,140,168,168,168,168,168,168,196,196,196,196,196,220,220,220,220,220,220,220,220,220,220,255,219,0,67,1,34,36,36,56,52,56,96,52,52,96,230,156,128,156,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,230,255,221,0,4,0,22,255,218,0,12,3,1,0,2,17,3,17,0,63,0,223,162,138,40,16,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,1,255,208,223,162,138,40,16,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,1,255,209,223,162,138,40,16,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,1,255,210,223,162,138,40,16,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0,81,69,20,0]],

这是一个屏幕截图：

ByteArray 中图像的输出

标签： swift

解决方案

您发布的 JSON 解释了为什么解析需要这么长时间。解析这些巨大的数组可能需要一段时间。而不是将每个图像作为一个字节数组，它应该只是每个图像的一个 base64 编码字符串。像这样的东西：

public var formImagesBase64 : [String]?

以下是一些可能对您有所帮助的 Swift 代码：

// Encoding Image
let data = image.jpegData(compressionQuality: 1)
let b64string = data?.base64EncodedString()

// Decoding image
let decodedData = Data(base64Encoded: b64string!)
let decodedImage = UIImage(data: decodedData!)

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