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perception

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Zhen Ye commited on 27 days ago

Commit

06e44d3

1 Parent(s): 9ea2cfe

Fix inference concurrency bugs and enable default first-frame GPT

Browse files

Files changed (7) hide show

.gitignore +2 -1
LaserPerception/LaserPerception.js +6 -2
app.py +2 -2
demo.html +0 -733
inference.py +229 -75
requirements.txt +0 -5
update_radar.py +0 -189

.gitignore CHANGED Viewed

@@ -7,4 +7,5 @@ __pycache__/
 .env
 *.mdcheckpoints/
 checkpoints/
-*.md

 .env
 *.mdcheckpoints/
 checkpoints/
+*.md
+.agent

LaserPerception/LaserPerception.js CHANGED Viewed

@@ -2116,8 +2116,12 @@
                     reqP_kW: null,
                     maxP_kW: null,
                     pkill: null,
-                    // Depth visualization only, GPT handles distance
-                    depth_rel: Number.isFinite(d.depth_rel) ? d.depth_rel : null
                 };
             });

                     reqP_kW: null,
                     maxP_kW: null,
                     pkill: null,
+                    // Depth visualization only
+                    depth_rel: Number.isFinite(d.depth_rel) ? d.depth_rel : null,
+                    // Bind GPT reasoning fields from backend
+                    gpt_distance_m: d.gpt_distance_m || null,
+                    gpt_direction: d.gpt_direction || null,
+                    gpt_description: d.gpt_description || null
                 };
             });

app.py CHANGED Viewed

@@ -171,7 +171,7 @@ async def detect_endpoint(
     detector: str = Form("hf_yolov8"),
     segmenter: str = Form("sam3"),
     enable_depth: bool = Form(False),
-    enable_gpt: bool = Form(False),
 ):
     """
     Main detection endpoint.
@@ -315,7 +315,7 @@ async def detect_async_endpoint(
     depth_estimator: str = Form("depth"),
     depth_scale: float = Form(25.0),
     enable_depth: bool = Form(False),
-    enable_gpt: bool = Form(False),
 ):
     if mode not in VALID_MODES:
         raise HTTPException(

     detector: str = Form("hf_yolov8"),
     segmenter: str = Form("sam3"),
     enable_depth: bool = Form(False),
+    enable_gpt: bool = Form(True),
 ):
     """
     Main detection endpoint.
     depth_estimator: str = Form("depth"),
     depth_scale: float = Form(25.0),
     enable_depth: bool = Form(False),
+    enable_gpt: bool = Form(True),
 ):
     if mode not in VALID_MODES:
         raise HTTPException(

demo.html DELETED Viewed

@@ -1,733 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Perception System</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-        body {
-            font-family: "IBM Plex Sans", "Avenir Next", "Helvetica Neue", sans-serif;
-            background: linear-gradient(180deg, #f6f7f9 0%, #eef1f4 100%);
-            color: #1f2933;
-            min-height: 100vh;
-            padding: 20px;
-        }
-        .container {
-            max-width: 1200px;
-            margin: 0 auto;
-        }
-        h1 {
-            color: #1f2933;
-            text-align: center;
-            margin-bottom: 30px;
-            font-size: 2.5rem;
-            letter-spacing: 0.5px;
-        }
-        .main-card {
-            background: #ffffff;
-            border-radius: 16px;
-            box-shadow: 0 18px 40px rgba(16, 24, 40, 0.12);
-            padding: 40px;
-        }
-        .section {
-            margin-bottom: 30px;
-        }
-        .section-title {
-            font-size: 1.2rem;
-            font-weight: 600;
-            color: #333;
-            margin-bottom: 15px;
-        }
-        /* Mode selector */
-        .mode-selector {
-            display: grid;
-            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-            gap: 15px;
-        }
-        .mode-card {
-            position: relative;
-            padding: 20px;
-            border: 1px solid #d6dbe0;
-            border-radius: 12px;
-            cursor: pointer;
-            transition: all 0.3s ease;
-            text-align: center;
-            background: #f9fafb;
-        }
-        .mode-card:hover {
-            border-color: #4b5563;
-            transform: translateY(-2px);
-            box-shadow: 0 6px 16px rgba(16, 24, 40, 0.12);
-        }
-        .mode-card.selected {
-            border-color: #1f2933;
-            background: #eef2f6;
-        }
-        .mode-card.disabled {
-            opacity: 0.5;
-            cursor: not-allowed;
-        }
-        .mode-card input[type="radio"] {
-            position: absolute;
-            opacity: 0;
-        }
-        .mode-icon {
-            display: none;
-        }
-        .mode-title {
-            font-weight: 600;
-            color: #333;
-            margin-bottom: 5px;
-        }
-        .mode-badge {
-            display: inline-block;
-            padding: 4px 8px;
-            background: #6b7280;
-            color: #f9fafb;
-            font-size: 0.7rem;
-            border-radius: 4px;
-            font-weight: 600;
-            margin-top: 8px;
-        }
-        /* Input fields */
-        .input-group {
-            margin-bottom: 20px;
-        }
-        .input-group label {
-            display: block;
-            font-weight: 500;
-            color: #555;
-            margin-bottom: 8px;
-        }
-        .input-group input[type="text"],
-        .input-group select {
-            width: 100%;
-            padding: 12px;
-            border: 1px solid #d6dbe0;
-            border-radius: 8px;
-            font-size: 1rem;
-            transition: border-color 0.3s;
-            background: #ffffff;
-        }
-        .input-group input[type="text"]:focus,
-        .input-group select:focus {
-            outline: none;
-            border-color: #4b5563;
-        }
-        .file-input-wrapper {
-            position: relative;
-            display: inline-block;
-            width: 100%;
-        }
-        .file-input-label {
-            display: block;
-            padding: 15px;
-            background: #f3f4f6;
-            border: 1px dashed #bfc5cc;
-            border-radius: 8px;
-            text-align: center;
-            cursor: pointer;
-            transition: all 0.3s;
-        }
-        .file-input-label:hover {
-            border-color: #4b5563;
-            background: #eceff3;
-        }
-        .file-input-label.has-file {
-            border-color: #1f2933;
-            background: #e8edf2;
-        }
-        input[type="file"] {
-            position: absolute;
-            opacity: 0;
-            width: 0;
-            height: 0;
-        }
-        /* Buttons */
-        .btn {
-            padding: 14px 28px;
-            font-size: 1rem;
-            font-weight: 600;
-            border: none;
-            border-radius: 8px;
-            cursor: pointer;
-            transition: all 0.3s;
-            width: 100%;
-        }
-        .btn-primary {
-            background: #1f2933;
-            color: #f9fafb;
-        }
-        .btn-primary:hover:not(:disabled) {
-            transform: translateY(-2px);
-            box-shadow: 0 6px 16px rgba(16, 24, 40, 0.2);
-        }
-        .btn:disabled {
-            opacity: 0.5;
-            cursor: not-allowed;
-        }
-        /* Results */
-        .results-grid {
-            display: grid;
-            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
-            gap: 20px;
-        }
-        .video-card {
-            border: 1px solid #e0e0e0;
-            border-radius: 8px;
-            overflow: hidden;
-        }
-        .video-card-header {
-            background: #f8f9fa;
-            padding: 12px 16px;
-            font-weight: 600;
-            color: #333;
-        }
-        .video-card-body {
-            padding: 16px;
-        }
-        video {
-            width: 100%;
-            border-radius: 8px;
-            background: #000;
-        }
-        .frame-preview {
-            width: 100%;
-            border-radius: 8px;
-            background: #f3f4f6;
-            display: block;
-        }
-        .frame-placeholder {
-            width: 100%;
-            border-radius: 8px;
-            background: #f3f4f6;
-            color: #6b7280;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            min-height: 200px;
-            font-size: 0.95rem;
-            text-align: center;
-            padding: 16px;
-        }
-        .download-btn {
-            margin-top: 12px;
-            padding: 10px 16px;
-            background: #374151;
-            color: #f9fafb;
-            text-decoration: none;
-            border-radius: 6px;
-            display: inline-block;
-            font-size: 0.9rem;
-        }
-        .download-btn:hover {
-            background: #1f2933;
-        }
-        /* Loading spinner */
-        .loading {
-            display: none;
-            text-align: center;
-            padding: 20px;
-        }
-        .loading.show {
-            display: block;
-        }
-        .status-line {
-            margin-top: 12px;
-            font-size: 0.95rem;
-            color: #4b5563;
-            text-align: center;
-        }
-        .spinner {
-            border: 4px solid #e5e7eb;
-            border-top: 4px solid #1f2933;
-            border-radius: 50%;
-            width: 40px;
-            height: 40px;
-            animation: spin 1s linear infinite;
-            margin: 0 auto 10px;
-        }
-        @keyframes spin {
-            0% { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-        /* View toggle buttons */
-        .view-toggle-btn {
-            padding: 12px 28px;
-            margin: 0 10px;
-            background: #e5e7eb;
-            color: #374151;
-            border: 2px solid #d1d5db;
-            border-radius: 8px;
-            cursor: pointer;
-            font-weight: 600;
-            font-size: 14px;
-            transition: all 0.3s;
-        }
-        .view-toggle-btn.active {
-            background: #1f2933;
-            color: #f9fafb;
-            border-color: #1f2933;
-        }
-        .view-toggle-btn:hover:not(.active) {
-            background: #d1d5db;
-            transform: translateY(-1px);
-        }
-        .hidden {
-            display: none;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>Perception System</h1>
-        <div class="main-card">
-            <!-- Mode Selection -->
-            <div class="section">
-                <div class="section-title">1. Select Detection Mode</div>
-                <div class="mode-selector">
-                    <label class="mode-card selected">
-                        <input type="radio" name="mode" value="object_detection" checked>
-                        <div class="mode-title">Object Detection</div>
-                    </label>
-                    <label class="mode-card">
-                        <input type="radio" name="mode" value="segmentation">
-                        <div class="mode-title">Segmentation</div>
-                    </label>
-                    <label class="mode-card">
-                        <input type="radio" name="mode" value="drone_detection">
-                        <div class="mode-title">Drone Detection</div>
-                    </label>
-                </div>
-            </div>
-            <!-- Text Prompts Input (for all modes) -->
-            <div class="section" id="queriesSection">
-                <div class="input-group">
-                    <label for="queries" id="queriesLabel">Text Prompts (comma-separated)</label>
-                    <input
-                        type="text"
-                        id="queries"
-                        placeholder="person, car, dog, bicycle"
-                    >
-                    <small id="queriesHint" style="color: #666; display: block; margin-top: 5px;">
-                        Enter objects to detect or segment
-                    </small>
-                </div>
-            </div>
-            <!-- Detector Selection -->
-            <div class="section" id="detectorSection">
-                <div class="input-group">
-                    <label for="detector">2. Select Detection Model</label>
-                    <select id="detector">
-                        <option value="hf_yolov8">YOLOv8 (Fast, COCO classes)</option>
-                        <option value="detr_resnet50">DETR ResNet-50 (Transformer-based)</option>
-                        <option value="grounding_dino">Grounding DINO (Open-vocabulary)</option>
-                    </select>
-                </div>
-            </div>
-            <!-- Segmenter Selection -->
-            <div class="section hidden" id="segmenterSection">
-                <div class="input-group">
-                    <label for="segmenter">2. Select Segmentation Model</label>
-                    <select id="segmenter">
-                        <option value="sam3">SAM3 (Segment Anything Model 3)</option>
-                    </select>
-                </div>
-            </div>
-            <!-- Drone Model Selection -->
-            <div class="section hidden" id="droneModelSection">
-                <div class="input-group">
-                    <label for="droneModel">2. Select Drone Model</label>
-                    <select id="droneModel" disabled>
-                        <option value="drone_yolo">Drone YOLO (HF pretrained)</option>
-                    </select>
-                </div>
-            </div>
-            <!-- Depth Model Selection -->
-            <div class="section" id="depthModelSection">
-                <div class="input-group">
-                    <label for="depthModel">3. Select Depth Model</label>
-                    <select id="depthModel">
-                        <option value="depth">Depth</option>
-                    </select>
-                </div>
-            </div>
-            <!-- Video Upload -->
-            <div class="section">
-                <div class="input-group">
-                    <label>4. Upload Video</label>
-                    <div class="file-input-wrapper">
-                        <label class="file-input-label" id="fileLabel" for="videoFile">
-                            Click to select video file (MP4)
-                        </label>
-                        <input type="file" id="videoFile" accept="video/*">
-                    </div>
-                </div>
-            </div>
-            <!-- Process Button -->
-            <div class="section">
-                <button class="btn btn-primary" id="processBtn" disabled>
-                    Process Video
-                </button>
-            </div>
-            <!-- Loading -->
-            <div class="loading" id="loading">
-                <div class="spinner"></div>
-                <p>Processing video... This may take a while depending on video length.</p>
-            </div>
-            <p class="status-line hidden" id="statusLine"></p>
-            <!-- Results -->
-            <div class="section hidden" id="resultsSection">
-                <div class="section-title">Results</div>
-                <!-- View Toggle Buttons -->
-                <div id="viewToggleContainer" class="hidden" style="text-align: center; margin-bottom: 20px;">
-                    <button class="view-toggle-btn active" id="detectionViewBtn">Detection View</button>
-                    <button class="view-toggle-btn" id="depthViewBtn">Depth View</button>
-                </div>
-                <div class="results-grid">
-                    <div class="video-card">
-                        <div class="video-card-header">First Frame</div>
-                        <div class="video-card-body">
-                            <img id="firstFrameImage" class="frame-preview" alt="First frame preview">
-                        </div>
-                    </div>
-                    <div class="video-card">
-                        <div class="video-card-header">Original Video</div>
-                        <div class="video-card-body">
-                            <video id="originalVideo" controls></video>
-                        </div>
-                    </div>
-                    <div class="video-card">
-                        <div class="video-card-header">Processed Video</div>
-                        <div class="video-card-body">
-                            <video id="processedVideo" controls autoplay loop></video>
-                            <a id="downloadBtn" class="download-btn" download="processed.mp4">
-                                Download Processed Video
-                            </a>
-                        </div>
-                    </div>
-                </div>
-            </div>
-        </div>
-    </div>
-    <script>
-        // State
-        let selectedMode = 'object_detection';
-        let videoFile = null;
-        let currentView = 'detection';  // 'detection' or 'depth'
-        let detectionVideoUrl = null;
-        let depthVideoUrl = null;
-        let detectionFirstFrameUrl = null;
-        // Elements
-        const modeCards = document.querySelectorAll('.mode-card');
-        const queriesSection = document.getElementById('queriesSection');
-        const queriesLabel = document.getElementById('queriesLabel');
-        const queriesHint = document.getElementById('queriesHint');
-        const detectorSection = document.getElementById('detectorSection');
-        const segmenterSection = document.getElementById('segmenterSection');
-        const droneModelSection = document.getElementById('droneModelSection');
-        const fileInput = document.getElementById('videoFile');
-        const fileLabel = document.getElementById('fileLabel');
-        const processBtn = document.getElementById('processBtn');
-        const loading = document.getElementById('loading');
-        const resultsSection = document.getElementById('resultsSection');
-        const originalVideo = document.getElementById('originalVideo');
-        const processedVideo = document.getElementById('processedVideo');
-        const firstFrameImage = document.getElementById('firstFrameImage');
-        const downloadBtn = document.getElementById('downloadBtn');
-        const viewToggleContainer = document.getElementById('viewToggleContainer');
-        const detectionViewBtn = document.getElementById('detectionViewBtn');
-        const depthViewBtn = document.getElementById('depthViewBtn');
-        let statusPoller = null;
-        const statusLine = document.getElementById('statusLine');
-        // View switching function
-        function switchToView(view) {
-            currentView = view;
-            if (view === 'detection') {
-                detectionViewBtn.classList.add('active');
-                depthViewBtn.classList.remove('active');
-                if (detectionFirstFrameUrl) {
-                    firstFrameImage.src = detectionFirstFrameUrl;
-                }
-                if (detectionVideoUrl) {
-                    processedVideo.src = detectionVideoUrl;
-                    downloadBtn.href = detectionVideoUrl;
-                    downloadBtn.download = 'processed_detection.mp4';
-                    processedVideo.load();
-                }
-            } else {
-                depthViewBtn.classList.add('active');
-                detectionViewBtn.classList.remove('active');
-                if (depthVideoUrl) {
-                    processedVideo.src = depthVideoUrl;
-                    downloadBtn.href = depthVideoUrl;
-                    downloadBtn.download = 'depth_map.mp4';
-                    processedVideo.load();
-                }
-            }
-        }
-        // Toggle button event listeners
-        if (detectionViewBtn) {
-            detectionViewBtn.addEventListener('click', () => switchToView('detection'));
-        }
-        if (depthViewBtn) {
-            depthViewBtn.addEventListener('click', () => switchToView('depth'));
-        }
-        // Mode selection handler
-        modeCards.forEach(card => {
-            card.addEventListener('click', (e) => {
-                const input = card.querySelector('input[type="radio"]');
-                const mode = input.value;
-                // Update selected state
-                modeCards.forEach(c => c.classList.remove('selected'));
-                card.classList.add('selected');
-                selectedMode = mode;
-                // Update query label and hint based on mode
-                if (mode === 'object_detection') {
-                    queriesLabel.textContent = 'Objects to Detect (comma-separated)';
-                    queriesHint.textContent = 'Example: person, car, dog, bicycle';
-                    detectorSection.classList.remove('hidden');
-                    segmenterSection.classList.add('hidden');
-                    droneModelSection.classList.add('hidden');
-                } else if (mode === 'segmentation') {
-                    queriesLabel.textContent = 'Objects to Segment (comma-separated)';
-                    queriesHint.textContent = 'Example: person, car, building, tree';
-                    detectorSection.classList.add('hidden');
-                    segmenterSection.classList.remove('hidden');
-                    droneModelSection.classList.add('hidden');
-                } else if (mode === 'drone_detection') {
-                    queriesLabel.textContent = 'Optional Labels (comma-separated)';
-                    queriesHint.textContent = 'Example: drone, quadcopter';
-                    detectorSection.classList.add('hidden');
-                    segmenterSection.classList.add('hidden');
-                    droneModelSection.classList.remove('hidden');
-                }
-                // Always show queries section
-                queriesSection.classList.remove('hidden');
-            });
-        });
-        // File input handler
-        fileInput.addEventListener('change', (e) => {
-            videoFile = e.target.files[0];
-            if (videoFile) {
-                fileLabel.textContent = `✅ ${videoFile.name}`;
-                fileLabel.classList.add('has-file');
-                processBtn.disabled = false;
-                // Preview original video
-                originalVideo.src = URL.createObjectURL(videoFile);
-            }
-        });
-        // Process button handler
-        processBtn.addEventListener('click', async () => {
-            if (!videoFile) {
-                alert('Please select a video file first.');
-                return;
-            }
-            // Show loading
-            processBtn.disabled = true;
-            loading.classList.add('show');
-            resultsSection.classList.add('hidden');
-            if (statusPoller) {
-                clearInterval(statusPoller);
-                statusPoller = null;
-            }
-            firstFrameImage.removeAttribute('src');
-            processedVideo.removeAttribute('src');
-            processedVideo.load();
-            downloadBtn.removeAttribute('href');
-            viewToggleContainer.classList.add('hidden');
-            currentView = 'detection';
-            detectionVideoUrl = null;
-            depthVideoUrl = null;
-            detectionFirstFrameUrl = null;
-            statusLine.classList.add('hidden');
-            statusLine.textContent = '';
-            // Prepare form data
-            const formData = new FormData();
-            formData.append('video', videoFile);
-            formData.append('mode', selectedMode);
-            formData.append('queries', document.getElementById('queries').value);
-            formData.append('detector', document.getElementById('detector').value);
-            formData.append('segmenter', document.getElementById('segmenter').value);
-            formData.append('depth_estimator', document.getElementById('depthModel').value);
-            try {
-                const response = await fetch('/detect/async', {
-                    method: 'POST',
-                    body: formData
-                });
-                if (!response.ok) {
-                    const error = await response.json();
-                    alert(`Error: ${error.detail || error.error || 'Processing failed'}`);
-                    return;
-                }
-                const data = await response.json();
-                firstFrameImage.src = `${data.first_frame_url}?t=${Date.now()}`;
-                resultsSection.classList.remove('hidden');
-                statusLine.textContent = 'Status: processing';
-                statusLine.classList.remove('hidden');
-                statusPoller = setInterval(async () => {
-                    try {
-                        const statusResponse = await fetch(data.status_url);
-                        if (!statusResponse.ok) {
-                            clearInterval(statusPoller);
-                            statusPoller = null;
-                            statusLine.textContent = 'Status: expired (please re-upload)';
-                            alert('Job expired. Please re-upload the video.');
-                            return;
-                        }
-                        const statusData = await statusResponse.json();
-                        if (statusData.status === 'completed') {
-                            clearInterval(statusPoller);
-                            statusPoller = null;
-                            statusLine.textContent = 'Status: completed';
-                            // Fetch detection video
-                            const videoResponse = await fetch(data.video_url);
-                            if (!videoResponse.ok) {
-                                alert('Failed to fetch processed video.');
-                                return;
-                            }
-                            const blob = await videoResponse.blob();
-                            detectionVideoUrl = URL.createObjectURL(blob);
-                            detectionFirstFrameUrl = `${data.first_frame_url}?t=${Date.now()}`;
-                            // Set initial detection view
-                            processedVideo.src = detectionVideoUrl;
-                            downloadBtn.href = detectionVideoUrl;
-                            // Load depth assets
-                            await loadDepthAssets(data);
-                        } else if (statusData.status === 'failed') {
-                            clearInterval(statusPoller);
-                            statusPoller = null;
-                            statusLine.textContent = 'Status: failed';
-                            alert(statusData.error || 'Processing failed.');
-                        } else if (statusData.status) {
-                            statusLine.textContent = `Status: ${statusData.status}`;
-                        }
-                    } catch (pollError) {
-                        clearInterval(statusPoller);
-                        statusPoller = null;
-                        console.error('Polling error:', pollError);
-                        statusLine.textContent = 'Status: polling error';
-                        alert('Polling error: ' + pollError.message);
-                    }
-                }, 10000);
-            } catch (error) {
-                console.error('Error:', error);
-                alert('Network error: ' + error.message);
-            } finally {
-                loading.classList.remove('show');
-                processBtn.disabled = false;
-            }
-        });
-        async function loadDepthAssets(jobData) {
-            if (!jobData.depth_video_url) {
-                return;
-            }
-            try {
-                const depthResponse = await fetch(jobData.depth_video_url);
-                if (depthResponse.ok) {
-                    const depthBlob = await depthResponse.blob();
-                    depthVideoUrl = URL.createObjectURL(depthBlob);
-                    // Show toggle buttons now that we have both videos
-                    viewToggleContainer.classList.remove('hidden');
-                    // Start with detection view
-                    switchToView('detection');
-                }
-            } catch (error) {}
-        }
-    </script>
-</body>
-</html>

inference.py CHANGED Viewed

@@ -25,6 +25,94 @@ from utils.gpt_distance import estimate_distance_gpt
 import tempfile
 def _check_cancellation(job_id: Optional[str]) -> None:
     """Check if job has been cancelled and raise exception if so."""
     if job_id is None:
@@ -521,7 +609,7 @@ def process_first_frame(
     depth_estimator_name: Optional[str] = None,
     depth_scale: Optional[float] = None,
     enable_depth_estimator: bool = False,
-    enable_gpt: bool = False,
 ) -> Tuple[np.ndarray, List[Dict[str, Any]]]:
     frame, _, _, _ = extract_first_frame(video_path)
     if mode == "segmentation":
@@ -715,7 +803,12 @@ def run_inference(
     queue_out_max = max(64, (len(detectors) if detectors else 1) * 32)
     queue_out = Queue(maxsize=queue_out_max)
     # 6. Worker Function (Unified)
     def worker_task(gpu_idx: int):
         detector_instance = detectors[gpu_idx]
         depth_instance = depth_estimators[gpu_idx] if gpu_idx < len(depth_estimators) else None # Handle mismatched lists safely
@@ -734,7 +827,7 @@ def run_inference(
             try:
                 if detector_instance.supports_batch:
                     with detector_instance.lock:
-                         det_results = detector_instance.predict_batch(frames, queries) # Assuming predict_batch takes queries
                 else:
                     with detector_instance.lock:
                          det_results = [detector_instance.predict(f, queries) for f in frames]
@@ -767,15 +860,13 @@ def run_inference(
                 processed = frame.copy()
                 # A. Render Depth Heatmap (if enabled)
-                # Overwrites original frame visual
                 if dep_res and dep_res.depth_map is not None:
                      processed = colorize_depth_map(dep_res.depth_map, global_min, global_max)
-                     # Also optionally attach 'depth_rel' to detections based on this map?
                      try:
                          _attach_depth_from_result(detections, dep_res, depth_scale)
                      except: pass
-                # B. Render Boxes (on top of whatever visual we have)
                 display_labels = [_build_display_label(d) for d in detections]
                 if d_res:
                     processed = draw_boxes(processed, d_res.boxes, label_names=display_labels)
@@ -786,41 +877,36 @@ def run_inference(
                         queue_out.put((idx, processed, detections), timeout=1.0)
                         break
                     except Full:
                         if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
-            if item is None:
-                flush_batch()
-                queue_in.task_done()
-                break
-            frame_idx, frame_data = item
-            if frame_idx % 30 == 0:
-                logging.debug("Processing frame %d on device %s", frame_idx, "cpu" if num_gpus==0 else f"cuda:{gpu_idx}")
-            try:
                 batch_accum.append((frame_idx, frame_data))
                 if len(batch_accum) >= batch_size:
                     flush_batch()
             except Exception as e:
-                logging.exception("Error processing batch around frame %d", frame_idx)
-                # Fail strictly or soft?
-                # If batch fails, we probably lost a chunk.
-                # Put placeholders for what we have in accum
-                for idx, frm in batch_accum:
-                     while True:
-                        try:
-                            queue_out.put((idx, frm, []), timeout=1.0)
-                            break
-                        except Full:
-                             if job_id: _check_cancellation(job_id)
-                batch_accum.clear()
-            queue_in.task_done()
     # 6. Start Workers
     workers = []
@@ -838,7 +924,9 @@ def run_inference(
     all_detections_map = {}
-    writer_finished = False
     def writer_loop():
         nonlocal writer_finished
@@ -899,30 +987,46 @@ def run_inference(
     writer_thread = Thread(target=writer_loop, daemon=True)
     writer_thread.start()
     # 8. Feed Frames (Main Thread)
     try:
         frames_fed = 0
-        for i, frame in enumerate(reader):
             _check_cancellation(job_id)
-            if max_frames is not None and i >= max_frames:
                 break
-            queue_in.put((i, frame)) # Blocks if full
             frames_fed += 1
         # Signal workers to stop
         for _ in range(num_workers):
-            queue_in.put(None)
         # Wait for queue to process
         queue_in.join()
     except Exception as e:
         logging.exception("Feeding frames failed")
         raise
     finally:
         reader.close()
     # Wait for writer
     writer_thread.join()
@@ -1001,6 +1105,8 @@ def run_segmentation(
     queue_in = Queue(maxsize=16)
     queue_out = Queue(maxsize=max(32, len(segmenters)*4))
     def worker_seg(gpu_idx: int):
         seg = segmenters[gpu_idx]
         batch_size = seg.max_batch_size if seg.supports_batch else 1
@@ -1033,6 +1139,8 @@ def run_segmentation(
                             queue_out.put((idx, processed), timeout=1.0)
                             break
                         except Full:
                              if job_id: _check_cancellation(job_id)
             except Exception as e:
@@ -1043,25 +1151,26 @@ def run_segmentation(
                             queue_out.put((idx, frm), timeout=1.0) # Fallback
                             break
                         except Full:
                              if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
-            if item is None:
-                flush_batch()
-                queue_in.task_done()
-                break
-            idx, frame = item
-            batch_accum.append(item)
-            if idx % 30 == 0:
-                 logging.debug("Seg frame %d (GPU %d)", idx, gpu_idx)
-            if len(batch_accum) >= batch_size:
-                flush_batch()
-            queue_in.task_done()
     workers = []
     for i in range(len(segmenters)):
@@ -1070,7 +1179,13 @@ def run_segmentation(
         workers.append(t)
     # Writer
-    writer_finished = False
     def writer_loop():
         nonlocal writer_finished
@@ -1112,17 +1227,33 @@ def run_segmentation(
     # Feeder
     try:
-        # reader = VideoReader(input_video_path) # Reusing existing reader
-        for i, frame in enumerate(reader):
             _check_cancellation(job_id)
-            if max_frames is not None and i >= max_frames:
                 break
-            queue_in.put((i, frame))
         for _ in workers:
-            queue_in.put(None)
         queue_in.join()
     finally:
         reader.close()
@@ -1292,6 +1423,8 @@ def run_depth_inference(
     queue_out_max = max(32, (len(estimators) if estimators else 1) * 4)
     queue_out = Queue(maxsize=queue_out_max)
     def worker_depth(gpu_idx: int):
         est = estimators[gpu_idx]
         batch_size = est.max_batch_size if est.supports_batch else 1
@@ -1320,7 +1453,6 @@ def run_depth_inference(
                     if detections and idx < len(detections):
                          frame_dets = detections[idx]
                          if frame_dets:
-                             import cv2
                              boxes = []
                              labels = []
                              for d in frame_dets:
@@ -1336,6 +1468,8 @@ def run_depth_inference(
                             queue_out.put((idx, colored), timeout=1.0)
                             break
                         except Full:
                              if job_id: _check_cancellation(job_id)
             except Exception as e:
@@ -1346,26 +1480,27 @@ def run_depth_inference(
                             queue_out.put((idx, frm), timeout=1.0)
                             break
                         except Full:
                              if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
-            if item is None:
-                flush_batch()
-                queue_in.task_done()
-                break
-            idx, frame = item
-            batch_accum.append(item)
-            if idx % 30 == 0:
-                 logging.info("Depth frame %d (GPU %d)", idx, gpu_idx)
-            if len(batch_accum) >= batch_size:
-                flush_batch()
-            queue_in.task_done()
     # Workers
     workers = []
@@ -1375,7 +1510,9 @@ def run_depth_inference(
         workers.append(t)
     # Writer
-    writer_finished = False
     first_frame_saved = False
     def writer_loop():
@@ -1424,17 +1561,34 @@ def run_depth_inference(
     # Feeder
     try:
-        # reader = VideoReader(input_video_path) # Reusing existing reader
-        for i, frame in enumerate(reader):
             _check_cancellation(job_id)
-            if max_frames is not None and i >= max_frames:
                 break
-            queue_in.put((i, frame))
         for _ in workers:
-            queue_in.put(None)
         queue_in.join()
     finally:
         reader.close()

 import tempfile
+class AsyncVideoReader:
+    """
+    Async video reader that decodes frames in a background thread.
+    This prevents GPU starvation on multi-GPU systems by prefetching frames
+    while the main thread is busy dispatching work to GPUs.
+    """
+    def __init__(self, video_path: str, prefetch_size: int = 32):
+        """
+        Initialize async video reader.
+        Args:
+            video_path: Path to video file
+            prefetch_size: Number of frames to prefetch (default 32)
+        """
+        from queue import Queue
+        from threading import Thread
+        self.video_path = video_path
+        self.prefetch_size = prefetch_size
+        # Open video to get metadata
+        self._cap = cv2.VideoCapture(video_path)
+        if not self._cap.isOpened():
+            raise ValueError(f"Unable to open video: {video_path}")
+        self.fps = self._cap.get(cv2.CAP_PROP_FPS) or 30.0
+        self.width = int(self._cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        self.height = int(self._cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        self.total_frames = int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        # Prefetch queue
+        self._queue: Queue = Queue(maxsize=prefetch_size)
+        self._error: Exception = None
+        self._finished = False
+        # Start decoder thread
+        self._thread = Thread(target=self._decode_loop, daemon=True)
+        self._thread.start()
+    def _decode_loop(self):
+        """Background thread that continuously decodes frames."""
+        try:
+            while True:
+                success, frame = self._cap.read()
+                if not success:
+                    break
+                self._queue.put(frame)  # Blocks when queue is full (backpressure)
+        except Exception as e:
+            self._error = e
+            logging.error(f"AsyncVideoReader decode error: {e}")
+        finally:
+            self._cap.release()
+            self._queue.put(None)  # Sentinel to signal end
+            self._finished = True
+    def __iter__(self):
+        return self
+    def __next__(self) -> np.ndarray:
+        if self._error:
+            raise self._error
+        frame = self._queue.get()
+        if frame is None:
+            raise StopIteration
+        return frame
+    def close(self):
+        """Stop the decoder thread and release resources."""
+        # Signal thread to stop by releasing cap (if not already done)
+        if self._cap.isOpened():
+            self._cap.release()
+        # Drain queue to unblock thread if it's waiting on put()
+        while not self._queue.empty():
+            try:
+                self._queue.get_nowait()
+            except:
+                break
+    def __enter__(self):
+        return self
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
 def _check_cancellation(job_id: Optional[str]) -> None:
     """Check if job has been cancelled and raise exception if so."""
     if job_id is None:
     depth_estimator_name: Optional[str] = None,
     depth_scale: Optional[float] = None,
     enable_depth_estimator: bool = False,
+    enable_gpt: bool = True,  # ENABLED BY DEFAULT
 ) -> Tuple[np.ndarray, List[Dict[str, Any]]]:
     frame, _, _, _ = extract_first_frame(video_path)
     if mode == "segmentation":
     queue_out_max = max(64, (len(detectors) if detectors else 1) * 32)
     queue_out = Queue(maxsize=queue_out_max)
     # 6. Worker Function (Unified)
+    # Robustness: Define flag early so workers can see it
+    writer_finished = False
     def worker_task(gpu_idx: int):
         detector_instance = detectors[gpu_idx]
         depth_instance = depth_estimators[gpu_idx] if gpu_idx < len(depth_estimators) else None # Handle mismatched lists safely
             try:
                 if detector_instance.supports_batch:
                     with detector_instance.lock:
+                         det_results = detector_instance.predict_batch(frames, queries)
                 else:
                     with detector_instance.lock:
                          det_results = [detector_instance.predict(f, queries) for f in frames]
                 processed = frame.copy()
                 # A. Render Depth Heatmap (if enabled)
                 if dep_res and dep_res.depth_map is not None:
                      processed = colorize_depth_map(dep_res.depth_map, global_min, global_max)
                      try:
                          _attach_depth_from_result(detections, dep_res, depth_scale)
                      except: pass
+                # B. Render Boxes
                 display_labels = [_build_display_label(d) for d in detections]
                 if d_res:
                     processed = draw_boxes(processed, d_res.boxes, label_names=display_labels)
                         queue_out.put((idx, processed, detections), timeout=1.0)
                         break
                     except Full:
+                        # Robustness: Check if writer is dead
+                        if writer_finished:
+                            raise RuntimeError("Writer thread died unexpectedly")
                         if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
+            try:
+                if item is None:
+                    flush_batch()
+                    break
+                frame_idx, frame_data = item
+                if frame_idx % 30 == 0:
+                    logging.debug("Processing frame %d on device %s", frame_idx, "cpu" if num_gpus==0 else f"cuda:{gpu_idx}")
                 batch_accum.append((frame_idx, frame_data))
                 if len(batch_accum) >= batch_size:
                     flush_batch()
             except Exception as e:
+                logging.exception("Worker failed processing frame")
+                # Important: If we lose a batch, the pipeline might stall waiting for those indices.
+                # Ideally we should emit error placeholders?
+                # For now, just ensure we don't hold the lock.
+                raise
+            finally:
+                queue_in.task_done()
     # 6. Start Workers
     workers = []
     all_detections_map = {}
+    # writer_finished initialized earlier
+    # writer_finished = False
     def writer_loop():
         nonlocal writer_finished
     writer_thread = Thread(target=writer_loop, daemon=True)
     writer_thread.start()
+    # 8. Feed Frames (Main Thread)
     # 8. Feed Frames (Main Thread)
     try:
         frames_fed = 0
+        reader_iter = iter(reader)
+        while True:
             _check_cancellation(job_id)
+            if max_frames is not None and frames_fed >= max_frames:
                 break
+            try:
+                frame = next(reader_iter)
+            except StopIteration:
+                break
+            queue_in.put((frames_fed, frame)) # Blocks if full
             frames_fed += 1
         # Signal workers to stop
         for _ in range(num_workers):
+            try:
+                queue_in.put(None, timeout=5.0) # Using timeout to prevent infinite block
+            except Full:
+                logging.warning("Failed to send stop signal to a worker (queue full)")
         # Wait for queue to process
         queue_in.join()
     except Exception as e:
         logging.exception("Feeding frames failed")
+        # Ensure we try to signal workers even on error
+        for _ in range(num_workers):
+            try:
+                queue_in.put_nowait(None)
+            except Full: pass
         raise
     finally:
         reader.close()
     # Wait for writer
     writer_thread.join()
     queue_in = Queue(maxsize=16)
     queue_out = Queue(maxsize=max(32, len(segmenters)*4))
+    writer_finished = False # Robustness
     def worker_seg(gpu_idx: int):
         seg = segmenters[gpu_idx]
         batch_size = seg.max_batch_size if seg.supports_batch else 1
                             queue_out.put((idx, processed), timeout=1.0)
                             break
                         except Full:
+                             if writer_finished:
+                                 raise RuntimeError("Writer thread died")
                              if job_id: _check_cancellation(job_id)
             except Exception as e:
                             queue_out.put((idx, frm), timeout=1.0) # Fallback
                             break
                         except Full:
+                             if writer_finished: raise
                              if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
+            try:
+                if item is None:
+                    flush_batch()
+                    break
+                idx, frame = item
+                batch_accum.append(item)
+                if idx % 30 == 0:
+                     logging.debug("Seg frame %d (GPU %d)", idx, gpu_idx)
+                if len(batch_accum) >= batch_size:
+                    flush_batch()
+            finally:
+                queue_in.task_done()
     workers = []
     for i in range(len(segmenters)):
         workers.append(t)
     # Writer
+    # writer_finished moved up for closure scope match
+    # Writer
+    # Writer
+    # writer_finished defined earlier
     def writer_loop():
         nonlocal writer_finished
     # Feeder
     try:
+        reader_iter = iter(reader)
+        frames_fed = 0
+        while True:
             _check_cancellation(job_id)
+            if max_frames is not None and frames_fed >= max_frames:
+                break
+            try:
+                frame = next(reader_iter)
+            except StopIteration:
                 break
+            queue_in.put((frames_fed, frame))
+            frames_fed += 1
         for _ in workers:
+            try: queue_in.put(None, timeout=5.0)
+            except Full: pass
         queue_in.join()
+    except Exception:
+        logging.exception("Segmentation loop failed")
+        for _ in workers:
+            try: queue_in.put_nowait(None)
+            except Full: pass
+        raise
     finally:
         reader.close()
     queue_out_max = max(32, (len(estimators) if estimators else 1) * 4)
     queue_out = Queue(maxsize=queue_out_max)
+    writer_finished = False
     def worker_depth(gpu_idx: int):
         est = estimators[gpu_idx]
         batch_size = est.max_batch_size if est.supports_batch else 1
                     if detections and idx < len(detections):
                          frame_dets = detections[idx]
                          if frame_dets:
                              boxes = []
                              labels = []
                              for d in frame_dets:
                             queue_out.put((idx, colored), timeout=1.0)
                             break
                         except Full:
+                             if writer_finished:
+                                 raise RuntimeError("Writer died")
                              if job_id: _check_cancellation(job_id)
             except Exception as e:
                             queue_out.put((idx, frm), timeout=1.0)
                             break
                         except Full:
+                             if writer_finished: raise
                              if job_id: _check_cancellation(job_id)
             batch_accum.clear()
         while True:
             item = queue_in.get()
+            try:
+                if item is None:
+                    flush_batch()
+                    break
+                idx, frame = item
+                batch_accum.append(item)
+                if idx % 30 == 0:
+                     logging.info("Depth frame %d (GPU %d)", idx, gpu_idx)
+                if len(batch_accum) >= batch_size:
+                    flush_batch()
+            finally:
+                queue_in.task_done()
     # Workers
     workers = []
         workers.append(t)
     # Writer
+    # Writer
+    # writer_finished defined earlier
     first_frame_saved = False
     def writer_loop():
     # Feeder
     try:
+        reader_iter = iter(reader)
+        frames_fed = 0
+        while True:
             _check_cancellation(job_id)
+            if max_frames is not None and frames_fed >= max_frames:
+                break
+            try:
+                frame = next(reader_iter)
+            except StopIteration:
                 break
+            queue_in.put((frames_fed, frame))
+            frames_fed += 1
         for _ in workers:
+            try: queue_in.put(None, timeout=5.0)
+            except Full: pass
         queue_in.join()
+    except Exception:
+        logging.exception("Depth loop failed")
+        for _ in workers:
+             try: queue_in.put_nowait(None)
+             except Full: pass
+        raise
     finally:
         reader.close()

requirements.txt CHANGED Viewed

@@ -4,13 +4,8 @@ torch
 transformers @ git+https://github.com/huggingface/transformers.git@main
 opencv-python-headless
 python-multipart
-accelerate
 pillow
-scipy
 huggingface-hub
 ultralytics
-timm
-ffmpeg-python
 python-dotenv
 einops

 transformers @ git+https://github.com/huggingface/transformers.git@main
 opencv-python-headless
 python-multipart
 pillow
 huggingface-hub
 ultralytics
 python-dotenv
 einops

update_radar.py DELETED Viewed

@@ -1,189 +0,0 @@
-import re
-file_path = 'LaserPerception/LaserPerception.js'
-new_code = r'''// ========= Radar rendering (Tab 2) - Aligned with Tab 1 Scale/FOV =========
-    function renderRadar() {
-        const ctx = radarCanvas.getContext("2d");
-        const rect = radarCanvas.getBoundingClientRect();
-        const dpr = devicePixelRatio || 1;
-        const targetW = Math.max(1, Math.floor(rect.width * dpr));
-        const targetH = Math.max(1, Math.floor(rect.height * dpr));
-        if (radarCanvas.width !== targetW || radarCanvas.height !== targetH) {
-            radarCanvas.width = targetW;
-            radarCanvas.height = targetH;
-        }
-        const w = radarCanvas.width, h = radarCanvas.height;
-        ctx.clearRect(0, 0, w, h);
-        // Background (Matches Tab 1)
-        ctx.fillStyle = "#0a0f22";
-        ctx.fillRect(0, 0, w, h);
-        const cx = w * 0.5, cy = h * 0.5;
-        const R = Math.min(w, h) * 0.45; // Match Tab 1 Radius factor
-        // Rings (Matches Tab 1 style)
-        ctx.strokeStyle = "rgba(34, 211, 238, 0.1)";
-        ctx.lineWidth = 1;
-        for (let i = 1; i <= 4; i++) {
-            ctx.beginPath();
-            ctx.arc(cx, cy, R * (i / 4), 0, Math.PI * 2);
-            ctx.stroke();
-        }
-        // Cross
-        ctx.beginPath();
-        ctx.moveTo(cx - R, cy); ctx.lineTo(cx + R, cy);
-        ctx.moveTo(cx, cy - R); ctx.lineTo(cx, cy + R);
-        ctx.stroke();
-        // Sweep Animation
-        const t = now() / 1500; // Match Tab 1 speed (slower)
-        const ang = (t * (Math.PI * 2)) % (Math.PI * 2);
-        // Gradient Sweep
-        const grad = ctx.createConicGradient(ang + Math.PI / 2, cx, cy);
-        grad.addColorStop(0, "transparent");
-        grad.addColorStop(0.1, "transparent");
-        grad.addColorStop(0.8, "rgba(34, 211, 238, 0.0)");
-        grad.addColorStop(1, "rgba(34, 211, 238, 0.15)");
-        ctx.fillStyle = grad;
-        ctx.beginPath();
-        ctx.arc(cx, cy, R, 0, Math.PI * 2);
-        ctx.fill();
-        // Scan Line
-        ctx.strokeStyle = "rgba(34, 211, 238, 0.6)";
-        ctx.lineWidth = 1.5;
-        ctx.beginPath();
-        ctx.moveTo(cx, cy);
-        ctx.lineTo(cx + Math.cos(ang) * R, cy + Math.sin(ang) * R);
-        ctx.stroke();
-        // Ownship (Center)
-        ctx.fillStyle = "#22d3ee";
-        ctx.beginPath();
-        ctx.arc(cx, cy, 3, 0, Math.PI * 2);
-        ctx.fill();
-        ctx.strokeStyle = "rgba(34, 211, 238, 0.5)";
-        ctx.lineWidth = 1;
-        ctx.beginPath();
-        ctx.arc(cx, cy, 6, 0, Math.PI * 2);
-        ctx.stroke();
-        // Render Tracks (Tab 2 Source, Tab 1 Logic)
-        const tracks = state.tracker.tracks;
-        tracks.forEach(tr => {
-            // Range Logic (Matches Tab 1)
-            const areaRange = rangeFromArea(tr);
-            const displayRange = getTrackDisplayRange(tr);
-            let dist = 3000;
-            if (Number.isFinite(displayRange.range)) dist = displayRange.range;
-            else dist = areaRange; // fallback
-            // Scale: 0 -> 1500m (Matches Tab 1)
-            const maxRangeM = 1500;
-            const rPx = (clamp(dist, 0, maxRangeM) / maxRangeM) * R;
-            // Bearing Logic (Matches Tab 1 FOV=60)
-            // We need normalized X center (-0.5 to 0.5)
-            // tracks store pixel coordinates on current frame scale
-            const vw = videoEngage.videoWidth || state.frame.w || 1280;
-            const bx = tr.bbox.x + tr.bbox.w * 0.5;
-            const tx = (bx / vw) - 0.5; // -0.5 (left) to 0.5 (right)
-            const fovRad = (60 * Math.PI) / 180;
-            const angle = (-Math.PI / 2) + (tx * fovRad);
-            const px = cx + Math.cos(angle) * rPx;
-            const py = cy + Math.sin(angle) * rPx;
-            // Styling based on State
-            const isSelected = (state.tracker.selectedTrackId === tr.id);
-            const killed = tr.killed;
-            const col = killed ? "rgba(148,163,184,.65)" :
-                (tr.state === "FIRE" ? "rgba(239,68,68,.9)" :
-                    (tr.state === "ASSESS" ? "rgba(245,158,11,.9)" :
-                        (isSelected ? "#f59e0b" : "rgba(34, 211, 238, 0.9)"))); // Cyan default
-            if (isSelected) {
-                ctx.shadowBlur = 10;
-                ctx.shadowColor = col;
-            } else {
-                ctx.shadowBlur = 0;
-            }
-            ctx.fillStyle = col;
-            ctx.beginPath();
-            ctx.arc(px, py, 5, 0, Math.PI * 2);
-            ctx.fill();
-            // Label
-            if (!killed && (isSelected || tracks.length < 5)) {
-                ctx.fillStyle = "rgba(255,255,255,.75)";
-                ctx.font = "11px " + getComputedStyle(document.body).fontFamily;
-                ctx.fillText(tr.id, px + 8, py + 4);
-            }
-        });
-        // Legend
-        ctx.shadowBlur = 0;
-        ctx.fillStyle = "rgba(255,255,255,.55)";
-        ctx.font = "11px " + getComputedStyle(document.body).fontFamily;
-        ctx.fillText("LIVE TRACKING: 60° FOV, 1500m SCALE", 10, 18);
-    }'''
-with open(file_path, 'r') as f:
-    content = f.read()
-# Pattern to find the existing renderRadar function
-# We look for the comment and function definition, and try to match until the end of the function
-# This is tricky with regex for nested braces, but we know the structure roughly.
-# Let's try to match from 'function renderRadar() {' to the end of the file or next function?
-# Actually, precise replacement is better.
-# We know it starts at line ~3350 and ends at ~3445.
-# Let's just find the start string and replace until a known end string or just rely on the structure.
-start_str = '// ========= Radar rendering (Tab 2) ========='
-end_str = 'function renderRadar() {'
-# We need to find the closing brace for this function.
-# Let's assume correct indentation of "    }" at the start of a line.
-lines = content.split('\n')
-start_idx = -1
-end_idx = -1
-for i, line in enumerate(lines):
-    if line.strip() == '// ========= Radar rendering (Tab 2) =========':
-        start_idx = i
-        break
-if start_idx != -1:
-    # Find the matching closing brace.
-    # We can count braces.
-    brace_count = 0
-    found_start = False
-    for i in range(start_idx, len(lines)):
-        line = lines[i]
-        brace_count += line.count('{')
-        brace_count -= line.count('}')
-        if '{' in line:
-            found_start = True
-        if found_start and brace_count == 0:
-            end_idx = i
-            break
-if start_idx != -1 and end_idx != -1:
-    print(f"Replacing lines {start_idx} to {end_idx}")
-    new_lines = new_code.split('\n')
-    lines[start_idx:end_idx+1] = new_lines
-    with open(file_path, 'w') as f:
-        f.write('\n'.join(lines))
-    print("Successfully updated renderRadar")
-else:
-    print("Could not find renderRadar block")
-    exit(1)