Digit Classifier Migration to Superthin Template

exercises/static/exercises/dl_digit_classifier_newmanager/entry_point/exercise.py

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+import os.path
+from typing import Callable
+
+from src.manager.libs.applications.compatibility.exercise_wrapper_ros2 import CompatibilityExerciseWrapperRos2
+
+
+class Exercise(CompatibilityExerciseWrapperRos2):
+    def __init__(self, circuit: str, update_callback: Callable):
+        current_path = os.path.dirname(__file__)
+
+        super(Exercise, self).__init__(exercise_command=f"{current_path}/../../python_template/ros2_humble/exercise.py 0.0.0.0",
+                                       gui_command=f"{current_path}/../../python_template/ros2_humble/gui.py 0.0.0.0 {circuit}",
+                                       update_callback=update_callback)

exercises/static/exercises/dl_digit_classifier_newmanager/python_template/ros2_humble/GUI.py

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+import json
+import os
+import rclpy
+import cv2
+import sys
+import base64
+import threading
+import time
+import numpy as np
+from datetime import datetime
+import websocket
+import subprocess
+import logging
+
+from hal_interfaces.general.odometry import OdometryNode
+
+
+# Graphical User Interface Class
+class GUI:
+    # Initialization function
+    # The actual initialization
+    def __init__(self, host):
+
+        self.payload = {'image': '', 'shape': []}
+
+        # ROS2 init
+        if not rclpy.ok():
+            rclpy.init(args=None)
+
+
+	# Image variables
+        self.image_to_be_shown = None
+        self.image_to_be_shown_updated = False
+        self.image_show_lock = threading.Lock()
+        self.host = host
+        self.client = None
+
+
+
+        self.ack = False
+        self.ack_lock = threading.Lock()
+
+        # Create the lap object
+        # TODO: maybe move this to HAL and have it be hybrid
+
+
+        self.client_thread = threading.Thread(target=self.run_websocket)
+        self.client_thread.start()
+
+    def run_websocket(self):
+        while True:
+            print("GUI WEBSOCKET CONNECTED")
+            self.client = websocket.WebSocketApp(self.host, on_message=self.on_message)
+            self.client.run_forever(ping_timeout=None, ping_interval=0)
+
+    # Function to prepare image payload
+    # Encodes the image as a JSON string and sends through the WS
+    def payloadImage(self):
+        with self.image_show_lock:
+            image_to_be_shown_updated = self.image_to_be_shown_updated
+            image_to_be_shown = self.image_to_be_shown
+
+        image = image_to_be_shown
+        payload = {'image': '', 'shape': ''}
+
+        if not image_to_be_shown_updated:
+            return payload
+
+        shape = image.shape
+        frame = cv2.imencode('.JPEG', image)[1]
+        encoded_image = base64.b64encode(frame)
+
+        payload['image'] = encoded_image.decode('utf-8')
+        payload['shape'] = shape
+        with self.image_show_lock:
+            self.image_to_be_shown_updated = False
+
+        return payload
+
+    # Function for student to call
+    def showImage(self, image):
+        with self.image_show_lock:
+            self.image_to_be_shown = image
+            self.image_to_be_shown_updated = True
+
+    # Update the gui
+    def update_gui(self):
+        # print("GUI update")
+        # Payload Image Message
+        payload = self.payloadImage()
+        self.payload["image"] = json.dumps(payload)
+
+
+        message = json.dumps(self.payload)
+        if self.client:
+            try:
+                self.client.send(message)
+                # print(message)
+            except Exception as e:
+                print(f"Error sending message: {e}")
+
+    def on_message(self, ws, message):
+        """Handles incoming messages from the websocket client."""
+        if message.startswith("#ack"):
+            # print("on message" + str(message))
+            self.set_acknowledge(True)
+
+    def get_acknowledge(self):
+        """Gets the acknowledge status."""
+        with self.ack_lock:
+            ack = self.ack
+
+        return ack
+
+    def set_acknowledge(self, value):
+        """Sets the acknowledge status."""
+        with self.ack_lock:
+            self.ack = value
+
+
+class ThreadGUI:
+    """Class to manage GUI updates and frequency measurements in separate threads."""
+
+    def __init__(self, gui):
+        """Initializes the ThreadGUI with a reference to the GUI instance."""
+        self.gui = gui
+        self.ideal_cycle = 80
+        self.real_time_factor = 0
+        self.frequency_message = {'brain': '', 'gui': ''}
+        self.iteration_counter = 0
+        self.running = True
+
+    def start(self):
+        """Starts the GUI, frequency measurement, and real-time factor threads."""
+        self.frequency_thread = threading.Thread(target=self.measure_and_send_frequency)
+        self.gui_thread = threading.Thread(target=self.run)   
+        self.frequency_thread.start()
+        self.gui_thread.start()
+        print("GUI Thread Started!")
+
+    def measure_and_send_frequency(self):
+        """Measures and sends the frequency of GUI updates and brain cycles."""
+        previous_time = datetime.now()
+        while self.running:
+            time.sleep(2)
+
+            current_time = datetime.now()
+            dt = current_time - previous_time
+            ms = (dt.days * 24 * 60 * 60 + dt.seconds) * 1000 + dt.microseconds / 1000.0
+            previous_time = current_time
+            measured_cycle = ms / self.iteration_counter if self.iteration_counter > 0 else 0
+            self.iteration_counter = 0
+            brain_frequency = round(1000 / measured_cycle, 1) if measured_cycle != 0 else 0
+            gui_frequency = round(1000 / self.ideal_cycle, 1)
+            self.frequency_message = {'brain': brain_frequency, 'gui': gui_frequency}
+            message = json.dumps(self.frequency_message)
+            if self.gui.client:
+                try:
+                    self.gui.client.send(message)
+                except Exception as e:
+                    print(f"Error sending frequency message: {e}")
+
+    def run(self):
+        """Main loop to update the GUI at regular intervals."""
+        while self.running:
+            start_time = datetime.now()
+
+            self.gui.update_gui()
+            self.iteration_counter += 1
+            finish_time = datetime.now()
+
+            dt = finish_time - start_time
+            ms = (dt.days * 24 * 60 * 60 + dt.seconds) * 1000 + dt.microseconds / 1000.0
+            sleep_time = max(0, (50 - ms) / 1000.0)
+            time.sleep(sleep_time)
+
+
+# Create a GUI interface
+host = "ws://127.0.0.1:2303"
+gui_interface = GUI(host)
+
+# Spin a thread to keep the interface updated
+thread_gui = ThreadGUI(gui_interface)
+thread_gui.start()
+
+def showImage(image):
+    gui_interface.showImage(image)
+

exercises/static/exercises/dl_digit_classifier_newmanager/python_template/ros2_humble/HAL.py

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+import rclpy
+from rclpy.node import Node
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge
+import threading
+import cv2
+
+current_frame = None  # Global variable to store the frame
+
+class WebcamSubscriber(Node):
+    def __init__(self):
+        super().__init__('webcam_subscriber')
+        self.subscription = self.create_subscription(
+            Image,
+            '/image_raw',
+            self.listener_callback,
+            10)
+        self.subscription  # prevent unused variable warning
+        self.bridge = CvBridge()
+
+    def listener_callback(self, msg):
+        global current_frame
+        self.get_logger().info('Receiving video frame')
+        current_frame = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+
+def run_webcam_node():
+
+    webcam_subscriber = WebcamSubscriber()
+
+    rclpy.spin(webcam_subscriber)
+    webcam_subscriber.destroy_node()
+
+
+# Start the ROS2 node in a separate thread
+thread = threading.Thread(target=run_webcam_node)
+thread.start()
+
+def getImage():
+    global current_frame
+    return current_frame
+

exercises/static/exercises/dl_digit_classifier_newmanager/python_template/ros2_humble/README.md

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+[Exercise Documentation Website](https://jderobot.github.io/RoboticsAcademy/exercises/ComputerVision/dl_digit_classifier)

exercises/static/exercises/dl_digit_classifier_newmanager/python_template/ros2_humble/demo_code/academy.py

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+import GUI
+import HAL
+import base64
+from datetime import datetime
+import json
+import sys
+import time
+import cv2
+import numpy as np
+import onnxruntime
+
+roi_scale = 0.75
+input_size = (28, 28)
+
+# Receive model
+raw_dl_model = '/workspace/code/demo_model/mnist_cnn.onnx'
+
+# Load ONNX model
+try:
+    ort_session = onnxruntime.InferenceSession(raw_dl_model)
+except Exception:
+    exc_type, exc_value, exc_traceback = sys.exc_info()
+    print(str(exc_value))
+    print("ERROR: Model couldn't be loaded")
+
+previous_pred = 0
+previous_established_pred = "-"
+count_same_digit = 0
+
+while True:
+
+    # Get input webcam image
+    image = HAL.getImage()
+    if image is not None:
+        input_image_gray = np.mean(image, axis=2).astype(np.uint8)
+
+        # Get original image and ROI dimensions
+        h_in, w_in = image.shape[:2]
+        min_dim_in = min(h_in, w_in)
+        h_roi, w_roi = (int(min_dim_in * roi_scale), int(min_dim_in * roi_scale))
+        h_border, w_border = (int((h_in - h_roi) / 2.), int((w_in - w_roi) / 2.))
+
+        # Extract ROI and convert to tensor format required by the model
+        roi = input_image_gray[h_border:h_border + h_roi, w_border:w_border + w_roi]
+        roi_norm = (roi - np.mean(roi)) / np.std(roi)
+        roi_resized = cv2.resize(roi_norm, input_size)
+        input_tensor = roi_resized.reshape((1, 1, input_size[0], input_size[1])).astype(np.float32)
+
+        # Inference
+        ort_inputs = {ort_session.get_inputs()[0].name: input_tensor}
+        output = ort_session.run(None, ort_inputs)[0]
+        pred = int(np.argmax(output, axis=1))  # get the index of the max log-probability
+
+        # Show region used as ROI
+        cv2.rectangle(image, pt2=(w_border, h_border), pt1=(w_border + w_roi, h_border + h_roi), color=(255, 0, 0), thickness=3)
+
+        # Show FPS count
+        cv2.putText(image, "Pred: {}".format(int(pred)), (7, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+        # Send result
+        GUI.showImage(image)
+
+

exercises/static/exercises/dl_digit_classifier_newmanager/react-components/DigClassRR.js

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+import * as React from "react";
+import {Fragment} from "react";
+
+import "./css/DigClassMain.css";
+
+const DigClassMain = (props) => {
+  return (
+    <Fragment>
+    {props.children}
+    </Fragment>
+  );
+};
+
+export default DigClassMain;

exercises/static/exercises/dl_digit_classifier_newmanager/react-components/DisplayFeed.js

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+import * as React from "react";
+import { Box } from "@mui/material";
+import "./css/GUICanvas.css";
+import { drawImage } from "./helpers/showImages";
+
+
+const DisplayFeed = (props) => {
+  const [image, setImage] = React.useState(null)
+  const canvasRef = React.useRef(null)
+
+  React.useEffect(() => {
+    console.log("TestShowScreen subscribing to ['update'] events");
+    const callback = (message) => {
+      if(message.data.update.image){
+        console.log('image')
+        const image = JSON.parse(message.data.update.image)
+        if(image.image){
+          drawImage(message.data.update)
+        } 
+      }
+    };
+
+    window.RoboticsExerciseComponents.commsManager.subscribe(
+      [window.RoboticsExerciseComponents.commsManager.events.UPDATE],
+      callback
+    );
+
+    return () => {
+      console.log("TestShowScreen unsubscribing from ['state-changed'] events");
+      window.RoboticsExerciseComponents.commsManager.unsubscribe(
+        [window.RoboticsExerciseComponents.commsManager.events.UPDATE],
+        callback
+      );
+    };
+  }, []);
+
+  return (
+    <Box sx={{ height: "100%" }}>
+      <canvas
+        ref={canvasRef}
+        className={"exercise-canvas"}
+        id="canvas"
+      ></canvas>
+    </Box>
+  );
+};
+
+DisplayFeed.defaultProps = {
+  width: 800,
+  height: 600,
+};
+
+export default DisplayFeed