Method of using PHP and Qiniu cloud storage interface to implement object detection and intelligent classification of pictures

Extracting useful information from pictures, such as object detection and intelligent classification, is an important task in the field of computer vision. Qiniu Cloud Storage, as a cloud service provider with powerful storage and computing capabilities, provides developers with convenient interfaces to achieve these tasks. In this article, we will introduce how to use PHP and Qiniu cloud storage interface to implement object detection and intelligent classification of pictures.

First, we need to create a Qiniu Cloud Storage account and obtain the access key (Access Key and Secret Key). These keys will be used for authentication and access control.

Next, we need to install the PHP SDK, which provides interactive functions with the Qiniu cloud storage interface. You can download the SDK from the Qiniu Cloud official website and extract it to your project directory.

In the code, we first need to introduce the SDK and configure the access key:

require_once 'path/to/sdk/autoload.php';
use QiniuAuth;
use QiniuStorageUploadManager;

// 需要填寫你的 Access Key 和 Secret Key
$accessKey = 'YOUR_ACCESS_KEY';
$secretKey = 'YOUR_SECRET_KEY';

// 構(gòu)建鑒權(quán)對(duì)象
$auth = new Auth($accessKey, $secretKey);

Next, we can use the SDK interface to upload images to Qiniu Cloud Storage and obtain the Image address:

// 生成上傳 Token
$bucket = 'YOUR_BUCKET_NAME';
$token = $auth->uploadToken($bucket);

// 要上傳文件的本地路徑
$filePath = 'path/to/image.jpg';

// 上傳到七牛云存儲(chǔ)
$uploadMgr = new UploadManager();
list($ret, $err) = $uploadMgr->putFile($token, null, $filePath);

// 獲取上傳后的圖片地址
$imageUrl = $ret['key'];

Now, we can use the Qiniu cloud storage interface for image processing. Qiniu Cloud Storage provides a wealth of image processing functions, including image scaling, cropping, rotation, etc. Here, we will focus on how to perform object detection and intelligent classification.

For object detection, Qiniu Cloud Storage provides AI-based image processing capabilities. We can use the interface of Qiniu Cloud Storage to send a request to perform object detection on the uploaded pictures and obtain the detection results:

// 構(gòu)建物體檢測(cè)的請(qǐng)求參數(shù)
$requestUrl = 'http://ai.qiniuapi.com/v1/image/realtime?detectType=3&returnLabels=1';

// 發(fā)送請(qǐng)求
$curl = curl_init();
curl_setopt($curl, CURLOPT_URL, $requestUrl);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($curl, CURLOPT_POST, 1);
curl_setopt($curl, CURLOPT_POSTFIELDS, json_encode(['url' => $imageUrl]));
curl_setopt($curl, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);
$result = curl_exec($curl);
curl_close($curl);

// 解析返回的結(jié)果
$resultArray = json_decode($result, true);
$labels = $resultArray['result'][0]['labels'];

// 打印物體標(biāo)簽
foreach ($labels as $label) {
    echo $label['name'] . "
";
}

For intelligent classification, Qiniu Cloud Storage can also intelligently classify pictures based on the image content. . We can use the Qiniu Cloud Storage interface to send requests and obtain classification results:

// 構(gòu)建智能分類的請(qǐng)求參數(shù)
$requestUrl = 'http://ai.qiniuapi.com/v1/image/censor?scenes=';
$scenes = ['pulp', 'terror', 'politician', 'ads', 'live', 'sensitive'];
$requestUrl .= implode(',', $scenes);

// 發(fā)送請(qǐng)求
$curl = curl_init();
curl_setopt($curl, CURLOPT_URL, $requestUrl);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($curl, CURLOPT_POST, 1);
curl_setopt($curl, CURLOPT_POSTFIELDS, json_encode(['url' => $imageUrl]));
curl_setopt($curl, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);
$result = curl_exec($curl);
curl_close($curl);

// 解析返回的結(jié)果
$resultArray = json_decode($result, true);
$censorResults = $resultArray['result'];

// 打印分類結(jié)果
foreach ($censorResults as $scene => $censorResult) {
    echo $scene . ': ' . ($censorResult['suggestion'] == 0 ? '正常' : '不確定') . "
";
}

Through the above code examples, we can use PHP and the Qiniu Cloud Storage interface to perform object detection and intelligent classification of images. You can adjust the code to suit your project based on actual needs. Hope this article is helpful to you!

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