Détecter des anomalies dans une image

Cette commande de l'AWS CLI affiche la sortie JSON pour l'opération DetectAnomalies de l'interface de ligne de commande (CLI). Modifiez les valeurs des paramètres d'entrée suivants :

aws lookoutvision detect-anomalies  --project-name project name\
    --model-version model version\
    --content-type content type\
    --body file name \
    --profile lookoutvision-access

Pour l'exemple de code complet, voirGitHub.

    def detect_anomalies(lookoutvision_client, project_name, model_version, photo):
        """
        Calls DetectAnomalies using the supplied project, model version, and image.
        :param lookoutvision_client: A Lookout for Vision Boto3 client.
        :param project: The project that contains the model that you want to use.
        :param model_version: The version of the model that you want to use.
        :param photo: The photo that you want to analyze.
        :return: The DetectAnomalyResult object that contains the analysis results.
        """

        image_type = imghdr.what(photo)
        if image_type == "jpeg":
            content_type = "image/jpeg"
        elif image_type == "png":
            content_type = "image/png"
        else:
            logger.info("Invalid image type for %s", photo)
            raise ValueError(
                f"Invalid file format. Supply a jpeg or png format file: {photo}")


        # Get images bytes for call to detect_anomalies
        with open(photo, "rb") as image:
            response = lookoutvision_client.detect_anomalies(
                ProjectName=project_name,
                ContentType=content_type,
                Body=image.read(),
                ModelVersion=model_version)

        return response['DetectAnomalyResult']

   public static DetectAnomalyResult detectAnomalies(LookoutVisionClient lfvClient, String projectName,
            String modelVersion,
            String photo) throws IOException, LookoutVisionException {
        /**
         * Creates an Amazon Lookout for Vision dataset from a manifest file.
         * Returns after Lookout for Vision creates the dataset.
         * 
         * @param lfvClient    An Amazon Lookout for Vision client.
         * @param projectName  The name of the project in which you want to create a
         *                     dataset.
         * @param modelVersion The version of the model that you want to use.
         *
         * @param photo        The photo that you want to analyze.
         * 
         * @return DetectAnomalyResult The analysis result from DetectAnomalies.
         */

        logger.log(Level.INFO, "Processing local file: {0}", photo);

        // Get image bytes.

        InputStream sourceStream = new FileInputStream(new File(photo));
        SdkBytes imageSDKBytes = SdkBytes.fromInputStream(sourceStream);
        byte[] imageBytes = imageSDKBytes.asByteArray();

        // Get the image type. Can be image/jpeg or image/png.
        String contentType = getImageType(imageBytes);

        // Detect anomalies in the supplied image.
        DetectAnomaliesRequest request = DetectAnomaliesRequest.builder().projectName(projectName)
                .modelVersion(modelVersion).contentType(contentType).build();

        DetectAnomaliesResponse response = lfvClient.detectAnomalies(request,
                RequestBody.fromBytes(imageBytes));

        /*
         * Tip: You can also use the following to analyze a local file.
         * Path path = Paths.get(photo);
         * DetectAnomaliesResponse response = lfvClient.detectAnomalies(request, path);
         */
        DetectAnomalyResult result = response.detectAnomalyResult();

        String prediction = "Prediction: Normal";

        if (Boolean.TRUE.equals(result.isAnomalous())) {
            prediction = "Prediction: Anomalous";
        }

        // Convert confidence to percentage.
        NumberFormat defaultFormat = NumberFormat.getPercentInstance();
        defaultFormat.setMinimumFractionDigits(1);
        String confidence = String.format("Confidence: %s", defaultFormat.format(result.confidence()));

        // Log classification result.
        String photoPath = "File: " + photo;
        String[] imageLines = { photoPath, prediction, confidence };
        logger.log(Level.INFO, "Image: {0}\nAnomalous: {1}\nConfidence {2}", imageLines);

        return result;

    }

    // Gets the image mime type. Supported formats are image/jpeg and image/png.
    private static String getImageType(byte[] image) throws IOException {

        InputStream is = new BufferedInputStream(new ByteArrayInputStream(image));
        String mimeType = URLConnection.guessContentTypeFromStream(is);

        logger.log(Level.INFO, "Image type: {0}", mimeType);

        if (mimeType.equals("image/jpeg") || mimeType.equals("image/png")) {
            return mimeType;
        }
        // Not a supported file type.
        logger.log(Level.SEVERE, "Unsupported image type: {0}", mimeType);
        throw new IOException(String.format("Wrong image type. %s format isn't supported.", mimeType));
    }