顯示分類和區段資訊 - Amazon Lookout for Vision

本文為英文版的機器翻譯版本,如內容有任何歧義或不一致之處,概以英文版為準。

顯示分類和區段資訊

此範例顯示分析的影像並覆疊分析結果。如果回應包含異常遮罩,則遮色片會以相關異常類型的顏色顯示。

若要顯示影像分類和影像分割資訊
  1. 如果您尚未這麼做,請執行下列動作:

    1. 如果您尚未這樣做,請安裝並配置AWS CLI和AWS軟體開發套件。如需詳細資訊,請參閱步驟 4:設定 AWS CLI 和 AWS SDKs

    2. 訓練您的模型

    3. 開始您的模型

  2. 確保用戶呼叫DetectAnomalies可以存取您要使用的模型版本。如需詳細資訊,請參閱設定 SDK 權限

  3. 使用下面的代碼。

    Python

    下列範例程式碼會偵測您提供的映像中的異常。此範例採用下列命令列選項:

    • project— 您要使用的專案名稱。

    • version— 您要在專案中使用的模型版本。

    • image— 本機影像檔案 (JPEG 或 PNG 格式) 的路徑和檔案。

    # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to detect and show anomalies in an image using a trained Amazon Lookout for Vision model. The script displays the analysed image and overlays mask and analysis output. """ import argparse import logging import io import boto3 from PIL import Image, ImageDraw, ImageFont from botocore.exceptions import ClientError logger = logging.getLogger(__name__) class ShowAnomalies: """ Class to detect and show anomalies in an image analyzed by detect_anomalies. """ @staticmethod def draw_line(draw, text, fnt, y_coordinate, color): """ Draws a line of text on the supplied drawing surface. :param draw: The surface on which to draw the text. :param text: The text to draw in the drawing surface. :param fnt: The font for the text. :param y_coordinate: The y position for the text. :param color: The color for the text. :returns The y coordinate for the next line of text. """ text_width, text_height = draw.textsize(text, fnt) draw.rectangle([(10, y_coordinate), (text_width + 10, y_coordinate + text_height)], fill="black") draw.text((10, y_coordinate), text, fill=color, font=fnt) y_coordinate += text_height return y_coordinate @staticmethod def draw_analysis_text(image, analysis): """ Draws classification and segmentation info onto supplied image overlay analysis results on an image analyzed by detect_anomalies. :param analysis: The response from a call to detect_anomalies. :returns Nothing """ ## Calculate a reasonable font size based on image width. font_size = int(image.size[0]/32) fnt = ImageFont.truetype('/Library/Fonts/Tahoma.ttf', font_size) draw = ImageDraw.Draw(image) y_coordinate = 0 # Draw classification information. prediction = "Anomalous" if analysis["DetectAnomalyResult"]["IsAnomalous"] \ else "Normal" confidence = analysis["DetectAnomalyResult"]["Confidence"] found_anomalies = analysis["DetectAnomalyResult"]['Anomalies'] segmentation_info = False logger.info("Prediction: %s", format(prediction)) logger.info("Confidence: %s", format(confidence)) y_coordinate = 0 y_coordinate = ShowAnomalies.draw_line( draw, "Classification", fnt, y_coordinate, "white") y_coordinate = ShowAnomalies.draw_line( draw, f" Prediction: {prediction}", fnt, y_coordinate, "white") y_coordinate = ShowAnomalies.draw_line( draw, f" Confidence: {confidence:.2%}", fnt, y_coordinate, "white") # Draw segmentation information, if present. if (len(found_anomalies)) > 1: logger.info("Anomalies:") y_coordinate = ShowAnomalies.draw_line( draw, "Segmentation:", fnt, y_coordinate, "white") for i in range(1, len(found_anomalies)): # Only display info if more than 0% coverage found. percent_coverage = found_anomalies[i]['PixelAnomaly']['TotalPercentageArea'] if percent_coverage > 0: segmentation_info = True logger.info(" %s", found_anomalies[i]['Name']) logger.info(" Color: %s", found_anomalies[i]['PixelAnomaly']['Color']) logger.info(" Area: %s", percent_coverage) y_coordinate = ShowAnomalies.draw_line( draw, f" Anomaly: {found_anomalies[i]['Name']}. Area: {percent_coverage:.2%}", fnt, y_coordinate, found_anomalies[i]['PixelAnomaly']['Color']) if not segmentation_info: y_coordinate = ShowAnomalies.draw_line( draw, "No segmentation information found.", fnt, y_coordinate, "white") @staticmethod def show_anomaly_prediction(lookoutvision_client, project_name, model_version, photo): """ Detects anomalies in an image (jpg/png) by using your Amazon Lookout for Vision model. Displays the image and overlays prediction information text. :param lookoutvision_client: An Amazon Lookout for Vision Boto3 client. :param project_name: The name of 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 path and name of the image in which you want to detect anomalies. """ try: logger.info("Detecting anomalies in %s", photo) image = Image.open(photo) image_type = Image.MIME[image.format] # Check that image type is valid. if image_type not in ("image/jpeg", "image/png"): 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. image_bytes = io.BytesIO() image.save(image_bytes, format=image.format) image_bytes = image_bytes.getvalue() # Analyze the image. response = lookoutvision_client.detect_anomalies( ProjectName=project_name, ContentType=image_type, Body=image_bytes, ModelVersion=model_version ) # Overlay mask onto analyzed image. image_mask_bytes = response["DetectAnomalyResult"]["AnomalyMask"] image_mask = Image.open(io.BytesIO(image_mask_bytes)) final_img = Image.blend(image, image_mask, 0.5) \ if response["DetectAnomalyResult"]["IsAnomalous"] else image # Overlay analysis output on image. ShowAnomalies.draw_analysis_text(final_img, response) final_img.show() except ClientError as err: logger.info(format(err)) raise def add_arguments(parser): """ Adds command line arguments to the parser. :param parser: The command line parser. """ parser.add_argument( "project", help="The project containing the model that you want to use." ) parser.add_argument( "version", help="The version of the model that you want to use." ) parser.add_argument( "image", help="The file that you want to analyze. " "Supply a local file path.", ) def main(): """ Entrypoint for anomaly detection example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") session = boto3.Session( profile_name='lookoutvision-access') lookoutvision_client = session.client("lookoutvision") parser = argparse.ArgumentParser(usage=argparse.SUPPRESS) add_arguments(parser) args = parser.parse_args() # Analyze the image and show results. ShowAnomalies.show_anomaly_prediction( lookoutvision_client, args.project, args.version, args.image ) except ClientError as err: print("A service error occured: " + format(err.response["Error"]["Message"])) except FileNotFoundError as err: print("The supplied file couldn't be found: " + err.filename) except ValueError as err: print("A value error occured. " + format(err)) else: print("Successfully completed analysis.") if __name__ == "__main__": main()
    Java 2

    下列範例程式碼會偵測您提供的映像中的異常。此範例採用下列命令列選項:

    • project— 您要使用的專案名稱。

    • version— 您要在專案中使用的模型版本。

    • image— 本機影像檔案 (JPEG 或 PNG 格式) 的路徑和檔案。

    /* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. SPDX-License-Identifier: Apache-2.0 */ package com.example.lookoutvision; import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider; import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.core.sync.RequestBody; import software.amazon.awssdk.services.lookoutvision.LookoutVisionClient; import software.amazon.awssdk.services.lookoutvision.model.Anomaly; import software.amazon.awssdk.services.lookoutvision.model.DetectAnomaliesRequest; import software.amazon.awssdk.services.lookoutvision.model.DetectAnomaliesResponse; import software.amazon.awssdk.services.lookoutvision.model.DetectAnomalyResult; import software.amazon.awssdk.services.lookoutvision.model.LookoutVisionException; import java.io.BufferedInputStream; import java.io.ByteArrayInputStream; import java.io.File; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.IOException; import java.io.InputStream; import java.net.URLConnection; import java.text.NumberFormat; import java.awt.*; import java.awt.font.LineMetrics; import java.awt.image.BufferedImage; import javax.imageio.ImageIO; import javax.swing.*; import java.util.logging.Level; import java.util.logging.Logger; // Finds anomalies on a supplied image. public class ShowAnomalies extends JPanel { /** * Finds and displays anomalies on a supplied image. */ private static final long serialVersionUID = 1L; private transient BufferedImage image; private transient BufferedImage maskImage; private transient Dimension dimension; public static final Logger logger = Logger.getLogger(ShowAnomalies.class.getName()); // Constructor. Finds anomalies in a local image file. public ShowAnomalies(LookoutVisionClient lfvClient, String projectName, String modelVersion, String photo) throws IOException, LookoutVisionException { logger.log(Level.INFO, "Processing local file: {0}", photo); maskImage = null; // Get image bytes and buffered image. InputStream sourceStream = new FileInputStream(new File(photo)); SdkBytes imageSDKBytes = SdkBytes.fromInputStream(sourceStream); byte[] imageBytes = imageSDKBytes.asByteArray(); ByteArrayInputStream inputStream = new ByteArrayInputStream(imageSDKBytes.asByteArray()); image = ImageIO.read(inputStream); // Get the image type. Can be image/jpeg or image/png. String contentType = getImageType(imageBytes); // Set the size of the window that shows the image. setWindowDimensions(); // 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(); if (result.anomalyMask() != null){ SdkBytes maskSDKBytes = result.anomalyMask(); ByteArrayInputStream maskInputStream = new ByteArrayInputStream(maskSDKBytes.asByteArray()); maskImage = ImageIO.read(maskInputStream); } drawImageInfo(result); } // Sets window dimensions to 1/2 screen size, unless image is smaller. public void setWindowDimensions() { dimension = java.awt.Toolkit.getDefaultToolkit().getScreenSize(); dimension.width = (int) dimension.getWidth() / 2; dimension.height = (int) dimension.getHeight() / 2; if (image.getWidth() < dimension.width || image.getHeight() < dimension.height) { dimension.width = image.getWidth(); dimension.height = image.getHeight(); } setPreferredSize(dimension); } private int drawLine(Graphics2D g2d, String line, FontMetrics metrics, int yPos, Color color) { /** * Draws a line of text at the spsecified y position and color. * confidence * * @param g2D The Graphics2D object for the image. * @param line The line of text to draw. * @param metrics The font information to use. * @param yPos The y position for the line of text. * * @return The yPos for the next line of text. */ int indent = 10; // Get text height, width, and descent. int textWidth = metrics.stringWidth(line); LineMetrics lm = metrics.getLineMetrics(line, g2d); int textHeight = (int) lm.getHeight(); int descent = (int) lm.getDescent(); int y2Pos = (yPos + textHeight) - descent; // Draw black rectangle. g2d.setColor(Color.BLACK); g2d.fillRect(indent, yPos, textWidth, textHeight); // Draw text. g2d.setColor(color); g2d.drawString(line, indent, y2Pos); yPos += textHeight; return yPos; } public void drawImageInfo(DetectAnomalyResult result) { /** * Draws the results from DetectAnomalies onto the output image. * * @param result The response from a call to * DetectAnomalies. * */ // Set up drawing. Graphics2D g2d = image.createGraphics(); if (result.anomalyMask() != null){ Composite composite = g2d.getComposite(); g2d.setComposite(AlphaComposite.SrcOver.derive(0.5f)); int x = (image.getWidth() - maskImage.getWidth()) / 2; int y = (image.getHeight() - maskImage.getHeight()) / 2; g2d.drawImage(maskImage, x, y, null); // Set composite for overlaying text. g2d.setComposite(composite); } //Calculate font size based on arbitary 32 pixel image width. int fontSize = (image.getWidth() / 32); g2d.setFont(new Font("Tahoma", Font.PLAIN, fontSize)); Font font = g2d.getFont(); FontMetrics metrics = g2d.getFontMetrics(font); // Get classification information. String prediction = "Prediction: Normal"; if (Boolean.TRUE.equals(result.isAnomalous())) { prediction = "Prediction: Anomalous"; } // Convert prediction to percentage. NumberFormat defaultFormat = NumberFormat.getPercentInstance(); defaultFormat.setMinimumFractionDigits(1); String confidence = String.format("Confidence: %s", defaultFormat.format(result.confidence())); // Draw classification information. int yPos = 0; yPos = drawLine(g2d, "Classification:", metrics, yPos, Color.WHITE); yPos = drawLine(g2d, prediction, metrics, yPos, Color.WHITE); yPos = drawLine(g2d, confidence, metrics, yPos, Color.WHITE); // Draw segmentation info. yPos = drawLine(g2d, "Segmentation:", metrics, yPos, Color.WHITE); // Ignore background label, so size must be > 1 if (result.anomalies().size() > 1) { for (Anomaly anomaly : result.anomalies()) { if (anomaly.name().equals("background")) continue; String label = String.format("Anomaly: %s. Area: %s", anomaly.name(), defaultFormat.format(anomaly.pixelAnomaly().totalPercentageArea())); Color anomalyColor = Color.decode((anomaly.pixelAnomaly().color())); yPos = drawLine(g2d, label, metrics, yPos, anomalyColor); } } else { drawLine(g2d, "None found.", metrics, yPos, Color.WHITE); } g2d.dispose(); } @Override public void paintComponent(Graphics g) /** * Draws the image and analysis results. * * @param g The Graphics context object for drawing. * DetectAnomalies. * */ { Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g. // Draw the image. g2d.drawImage(image, 0, 0, dimension.width, dimension.height, this); } // Gets the image mime type. Supported formats are image/jpeg and image/png. private String getImageType(byte[] image) throws IOException /** * Gets the file type of a supplied image. Raises an exception if the image * isn't compatible with with Amazon Lookout for Vision. * * @param image The image that you want to check. * * @return String The type of the image. */ { 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)); } public static void main(String[] args) throws Exception { String photo = null; String projectName = null; String modelVersion = null; final String USAGE = "\n" + "Usage:\n" + " DetectAnomalies <project> <version> <image> \n\n" + "Where:\n" + " project - The Lookout for Vision project.\n\n" + " version - The version of the model within the project.\n\n" + " image - The path and filename of a local image. \n\n"; try { if (args.length != 3) { System.out.println(USAGE); System.exit(1); } projectName = args[0]; modelVersion = args[1]; photo = args[2]; ShowAnomalies panel = null; // Get the Lookout for Vision client. LookoutVisionClient lfvClient = LookoutVisionClient.builder() .credentialsProvider(ProfileCredentialsProvider.create("lookoutvision-access")) .build(); // Create frame and panel. JFrame frame = new JFrame(photo); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); panel = new ShowAnomalies(lfvClient, projectName, modelVersion, photo); frame.setContentPane(panel); frame.pack(); frame.setVisible(true); } catch (LookoutVisionException lfvError) { logger.log(Level.SEVERE, "Lookout for Vision client error: {0}: {1}", new Object[] { lfvError.awsErrorDetails().errorCode(), lfvError.awsErrorDetails().errorMessage() }); System.out.println(String.format("lookout for vision client error: %s", lfvError.getMessage())); System.exit(1); } catch (FileNotFoundException fileError) { logger.log(Level.SEVERE, "Could not find file: {0}", fileError.getMessage()); System.out.println(String.format("Could not find file: %s", fileError.getMessage())); System.exit(1); } catch (IOException ioError) { logger.log(Level.SEVERE, "IO error {0}", ioError.getMessage()); System.out.println(String.format("IO error: %s", ioError.getMessage())); System.exit(1); } } }
  4. 如果您不打算繼續使用模型,停止您的模型