Merge remote-tracking branch 'origin/dev'

landmarks.js

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+const fs = require("fs");
+const path = require("path");
+const { ArgumentParser } = require("argparse");
+const cv = require("@techstark/opencv-js");
+const yaml = require("js-yaml");
+const { Pose, POSE_LANDMARKS } = require("@mediapipe/pose");
+
+const logging = console;
+const warnings = console;
+
+class Landmarker {
+  static resizedHeight = 256;
+  static resizedWidth = 256;
+
+  constructor() {
+    this.args = this.parseArgs();
+    this.measurements = this.loadLandmarks();
+    if (!this.args.frontImage) {
+      throw new Error("Front image needs to be passed");
+    }
+    if (!this.args.sideImage) {
+      throw new Error("Side image needs to be passed");
+    }
+
+    this.frontImage = cv.imread(this.args.frontImage);
+    this.sideImage = cv.imread(this.args.sideImage);
+
+    this.frontImageResized = cv.resize(
+      this.frontImage,
+      new cv.Size(Landmarker.resizedWidth, Landmarker.resizedHeight),
+    );
+    this.sideImageResized = cv.resize(
+      this.sideImage,
+      new cv.Size(Landmarker.resizedWidth, Landmarker.resizedHeight),
+    );
+
+    this.distances = {};
+
+    this.personHeight = this.args.personHeight;
+    this.pixelHeight = this.args.pixelHeight;
+
+    this.pose = new Pose({
+      locateFile: (file) => {
+        return `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`;
+      },
+    });
+
+    this.landmarksIndices = [
+      POSE_LANDMARKS.LEFT_SHOULDER,
+      POSE_LANDMARKS.RIGHT_SHOULDER,
+      POSE_LANDMARKS.LEFT_ELBOW,
+      POSE_LANDMARKS.RIGHT_ELBOW,
+      POSE_LANDMARKS.LEFT_WRIST,
+      POSE_LANDMARKS.RIGHT_WRIST,
+      POSE_LANDMARKS.LEFT_HIP,
+      POSE_LANDMARKS.RIGHT_HIP,
+      POSE_LANDMARKS.LEFT_KNEE,
+      POSE_LANDMARKS.RIGHT_KNEE,
+      POSE_LANDMARKS.LEFT_ANKLE,
+      POSE_LANDMARKS.RIGHT_ANKLE,
+    ];
+  }
+
+  loadLandmarks() {
+    const file = fs.readFileSync(this.args.yamlFile, "utf8");
+    const landmarksData = yaml.load(file);
+    const measurements = {};
+    for (const measurement of landmarksData.measurements) {
+      measurements[measurement.name] = measurement.landmarks;
+    }
+    return measurements;
+  }
+
+  parseArgs() {
+    const parser = new ArgumentParser({
+      description: "Process images and calculate measurements",
+    });
+    parser.add_argument("--front", {
+      dest: "frontImage",
+      required: true,
+      help: "Path to the front image",
+    });
+    parser.add_argument("--side", {
+      dest: "sideImage",
+      required: true,
+      help: "Path to the side image",
+    });
+    parser.add_argument("--poseDetectionConfidence", {
+      dest: "poseDetectionConfidence",
+      default: 0.5,
+      type: "float",
+      help: "Confidence score for pose detection",
+    });
+    parser.add_argument("--poseTrackingConfidence", {
+      dest: "poseTrackingConfidence",
+      default: 0.5,
+      type: "float",
+      help: "Confidence score for pose tracking",
+    });
+    parser.add_argument("--personHeight", {
+      dest: "personHeight",
+      required: true,
+      type: "int",
+      help: "Person height in cm",
+    });
+    parser.add_argument("--pixelHeight", {
+      dest: "pixelHeight",
+      type: "int",
+      help: "Pixel height of person",
+    });
+    parser.add_argument("--measurement", {
+      dest: "measurement",
+      nargs: "+",
+      type: "str",
+      help: "Type of measurement",
+    });
+    parser.add_argument("--yamlFile", {
+      dest: "yamlFile",
+      required: true,
+      help: "Path to the YAML file containing landmarks",
+    });
+    return parser.parse_args();
+  }
+
+  async run() {
+    await this.pose.initialize();
+    const { frontResults, sideResults } = await this.processImages();
+
+    this.getCenterTopPoint(sideResults);
+
+    const table = [];
+    if (this.args.measurement) {
+      for (const m of this.args.measurement) {
+        if (!this.measurements[m]) {
+          throw new Error("Incorrect input (input not present in config.yml)");
+        } else {
+          const distance = this.calculateDistanceBetweenLandmarks(
+            frontResults,
+            m,
+          );
+          table.push([m, distance]);
+        }
+      }
+    } else {
+      for (const m in this.measurements) {
+        const distance = this.calculateDistanceBetweenLandmarks(
+          frontResults,
+          m,
+        );
+        table.push([m, distance]);
+      }
+    }
+
+    console.table(table);
+
+    this.pose.close();
+  }
+
+  async processImages() {
+    const frontResults = await this.pose.estimatePoses(this.frontImageResized);
+    const sideResults = await this.pose.estimatePoses(this.sideImageResized);
+
+    this.sideImageKeypoints = this.sideImageResized.clone();
+    this.frontImageKeypoints = this.frontImageResized.clone();
+
+    if (frontResults[0].landmarks) {
+      this.drawLandmarks(
+        this.frontImageKeypoints,
+        frontResults[0].landmarks,
+        this.landmarksIndices,
+      );
+    }
+    if (sideResults[0].landmarks) {
+      this.drawLandmarks(
+        this.sideImageKeypoints,
+        sideResults[0].landmarks,
+        this.landmarksIndices,
+      );
+    }
+    return {
+      frontResults: frontResults[0],
+      sideResults: sideResults[0],
+    };
+  }
+
+  pixelToMetricRatio() {
+    const pixelToMetricRatio = this.personHeight / this.pixelHeight;
+    logging.debug("pixelToMetricRatio %s", pixelToMetricRatio);
+    return pixelToMetricRatio;
+  }
+
+  drawLandmarks(image, landmarks, indices) {
+    for (const idx of indices) {
+      const landmark = landmarks[idx];
+      const h = image.rows;
+      const w = image.cols;
+      const cx = Math.round(landmark.x * w);
+      const cy = Math.round(landmark.y * h);
+      this.circle(image, cx, cy);
+    }
+  }
+
+  circle(image, cx, cy) {
+    cv.circle(image, new cv.Point(cx, cy), 2, new cv.Scalar(255, 0, 0), -1);
+  }
+
+  calculateDistanceBetweenLandmarks(frontResults, measurementName) {
+    if (!frontResults.landmarks) {
+      return;
+    }
+
+    const landmarks = frontResults.landmarks;
+    const landmarkNames = this.measurements[measurementName];
+
+    let totalDistance = 0;
+    for (let i = 0; i < landmarkNames.length - 1; i++) {
+      const current = landmarks[landmarkNames[i]];
+      const next = landmarks[landmarkNames[i + 1]];
+      const pixelDistance = this.euclideanDistance(
+        current.x * Landmarker.resizedWidth,
+        current.y * Landmarker.resizedHeight,
+        next.x * Landmarker.resizedWidth,
+        next.y * Landmarker.resizedHeight,
+      );
+      const realDistance = pixelDistance * this.pixelToMetricRatio();
+      totalDistance += realDistance;
+    }
+    return totalDistance;
+  }
+
+  euclideanDistance(x1, y1, x2, y2) {
+    return Math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2);
+  }
+
+  getCenterTopPoint(sideResults) {
+    const grayImage = cv.cvtColor(this.sideImageKeypoints, cv.COLOR_BGR2GRAY);
+    const blurredImage = cv.GaussianBlur(grayImage, new cv.Size(5, 5), 0);
+    const roi = blurredImage.roi(
+      new cv.Rect(
+        0,
+        0,
+        this.sideImageResized.cols,
+        Math.floor(this.sideImageResized.rows / 2),
+      ),
+    );
+    this.edges = cv.Canny(roi, 50, 150);
+    const contours = this.edges.findContours(
+      cv.RETR_EXTERNAL,
+      cv.CHAIN_APPROX_SIMPLE,
+    );
+
+    let xt, yt;
+    this.topmostPoint = null;
+
+    for (const contour of contours) {
+      const [xt, yt] = contour.minEnclosingCircle();
+      if (this.topmostPoint === null || yt < this.topmostPoint[1]) {
+        this.topmostPoint = [xt, yt];
+      }
+    }
+
+    const { x, y } = sideResults.landmarks[POSE_LANDMARKS.NOSE];
+    const centerPoint = [
+      x * Landmarker.resizedWidth,
+      y * Landmarker.resizedHeight,
+    ];
+    this.pixelHeight = Math.abs(centerPoint[1] - this.topmostPoint[1]);
+
+    cv.circle(
+      this.sideImageKeypoints,
+      new cv.Point(centerPoint[0], centerPoint[1]),
+      2,
+      new cv.Scalar(255, 0, 0),
+      -1,
+    );
+    cv.circle(
+      this.sideImageKeypoints,
+      new cv.Point(this.topmostPoint[0], this.topmostPoint[1]),
+      2,
+      new cv.Scalar(255, 0, 0),
+      -1,
+    );
+  }
+}
+
+const landmarker = new Landmarker();
+landmarker.run().catch((error) => {
+  console.error(error);
+});

pyproject.toml

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+[tool.black]
+line-length = 120
+

requirements.txt

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+mediapipe==0.10.13
+tabulate==0.9.0
+opencv-python-headless==4.10.0.84
+pyyaml==6.0.1