


Method to realize intelligent recognition and label classification of pictures using PHP and Qiniu cloud storage interface
Method of using PHP and Qiniu cloud storage interface to realize intelligent recognition and label classification of images
In recent years, with the rapid development of artificial intelligence technology, the field of image recognition has also made significant progress. Using machine learning and deep learning algorithms, we can allow computers to intelligently understand and analyze image content, thereby achieving intelligent recognition and label classification of images. This article will introduce how to use PHP and Qiniu cloud storage interface to implement this function, and give corresponding code examples.
First, we need to create an account on Qiniu Cloud Storage (https://www.qiniu.com/) and obtain the AccessKey and SecretKey of the account to access the API of Qiniu Cloud Storage .
Next, we need to use the SDK provided by Qiniu Cloud Storage to operate images. In PHP, you can use the officially provided Qiniu Cloud Storage SDK (https://developer.qiniu.com/sdk/php) to interact with Qiniu Cloud Storage.
The following is a simple code example that demonstrates how to use PHP and Qiniu Cloud Storage interface to upload an image, and call Qiniu Cloud Storage's image recognition API to obtain the tag classification of the image:
<?php require 'autoload.php'; // 导入七牛云存储的PHP SDK use QiniuStorageUploadManager; use QiniuAuth; // 需要填写七牛云存储的AccessKey和SecretKey $accessKey = 'your-access-key'; $secretKey = 'your-secret-key'; // 初始化Auth对象 $auth = new Auth($accessKey, $secretKey); // 需要上传的图片文件路径 $filePath = './path/to/your/image.jpg'; // 生成上传Token $token = $auth->uploadToken('your-bucket-name'); // 初始化UploadManager对象 $uploadMgr = new UploadManager(); // 上传图片到七牛云存储 list($ret, $err) = $uploadMgr->putFile($token, null, $filePath); if ($err !== null) { // 上传图片失败 echo 'Upload failed:', $err->message(); } else { // 上传图片成功 $imageKey = $ret['key']; // 调用七牛云存储的图像识别API,获取图片的标签分类 $url = 'http://ai.qiniuapi.com/v1/ai/predict'; $data = ['url' => 'your-image-url', 'model' => 'img_tag']; $headers = [ 'Content-Type:application/json', 'Authorization:Qiniu ' . $auth->signRequest($url, null, 'POST', $headers, json_encode($data)) ]; $ch = curl_init(); curl_setopt($ch, CURLOPT_URL, $url); curl_setopt($ch, CURLOPT_POST, 1); curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1); curl_setopt($ch, CURLOPT_HTTPHEADER, $headers); curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data)); $response = curl_exec($ch); curl_close($ch); // 解析返回的结果 $result = json_decode($response, true); // 获取图片的标签分类 $labels = isset($result['result']) ? $result['result'] : []; // 输出图片的标签分类 echo 'Image labels:', implode(', ', $labels); }
In the above code, we first initialize an Auth object using AccessKey and SecretKey, and then generate an upload Token. Then, call the putFile method of the UploadManager object to upload the image file to Qiniu Cloud Storage, and get the upload result.
Next, we construct an HTTP request and call the image recognition API of Qiniu Cloud Storage. The API accepts an image URL and a model name as parameters and returns the label classification results of the image. We use the curl library to send HTTP requests. What needs to be noted is that the request header needs to be signed based on AccessKey and SecretKey.
Finally, we parse the return results of the API, obtain the label classification of the image, and output the results.
Through the above code examples, we can see that it is not complicated to use PHP and Qiniu cloud storage interface to realize intelligent recognition and label classification of images. By uploading images to Qiniu Cloud Storage and then calling Qiniu Cloud Storage's image recognition API, we can easily obtain the label classification information of the image, providing more possibilities for subsequent image processing and applications.
The above is the detailed content of Method to realize intelligent recognition and label classification of pictures using PHP and Qiniu cloud storage interface. For more information, please follow other related articles on the PHP Chinese website!

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