Improve the display effect of the picture

SignX/{detailed_prediction_20260101_133848 → detailed_prediction_20260101_135859}/3381121/analysis_report.txt

@@ -2,7 +2,7 @@
   Sign Language Recognition - Attention分析报告
 ================================================================================
 
-生成时间: 2026-01-01 13:38:55
+生成时间: 2026-01-01 13:59:05
 
 翻译结果:
 --------------------------------------------------------------------------------

SignX/{detailed_prediction_20260101_133848 → detailed_prediction_20260101_135859}/3381121/attention_keyframes/keyframes_index.txt

@@ -1,7 +1,7 @@
 关键帧索引
 ============================================================
 
-样本目录: /common/users/sf895/output/huggingface_asllrp_repo/SignX/detailed_prediction_20260101_133848/3381121
+样本目录: /common/users/sf895/output/huggingface_asllrp_repo/SignX/detailed_prediction_20260101_135859/3381121
 视频路径: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/3381121.mp4
 总关键帧数: 30
 

SignX/detailed_prediction_20260101_150706/632051/analysis_report.txt

@@ -0,0 +1,43 @@
+================================================================================
+  Sign Language Recognition - Attention分析报告
+================================================================================
+
+生成时间: 2026-01-01 15:07:12
+
+翻译结果:
+--------------------------------------------------------------------------------
+#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p
+
+视频信息:
+--------------------------------------------------------------------------------
+总帧数: 28
+词数量: 8
+
+Attention权重信息:
+--------------------------------------------------------------------------------
+形状: (26, 28)
+  - 解码步数: 26
+
+词-帧对应详情:
+================================================================================
+No.   Word                 Frames          Peak     Attn     Conf      
+--------------------------------------------------------------------------------
+1     #IF                  2-2             2        0.472    medium    
+2     FRIEND               5-5             5        0.425    medium    
+3     GROUP/TOGETHER       8-8             8        0.375    medium    
+4     DEPART               27-27           27       0.348    medium    
+5     PARTY                27-27           27       0.383    medium    
+6     IX-1p                27-27           27       0.333    medium    
+7     JOIN                 11-11           11       0.520    high      
+8     IX-1p                14-14           14       0.368    medium    
+
+================================================================================
+
+统计摘要:
+--------------------------------------------------------------------------------
+平均attention权重: 0.403
+高置信度词: 1 (12.5%)
+中置信度词: 7 (87.5%)
+低置信度词: 0 (0.0%)
+
+================================================================================

SignX/detailed_prediction_20260101_150706/632051/attention_keyframes/keyframes_index.txt

@@ -0,0 +1,35 @@
+关键帧索引
+============================================================
+
+样本目录: /common/users/sf895/output/huggingface_asllrp_repo/SignX/detailed_prediction_20260101_150706/632051
+视频路径: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4
+总关键帧数: 26
+
+关键帧列表:
+------------------------------------------------------------
+Gloss   0: keyframe_000_feat2_frame9_att0.472.jpg
+Gloss   1: keyframe_001_feat5_frame20_att0.425.jpg
+Gloss   2: keyframe_002_feat8_frame32_att0.375.jpg
+Gloss   3: keyframe_003_feat27_frame104_att0.348.jpg
+Gloss   4: keyframe_004_feat27_frame104_att0.383.jpg
+Gloss   5: keyframe_005_feat27_frame104_att0.333.jpg
+Gloss   6: keyframe_006_feat11_frame43_att0.520.jpg
+Gloss   7: keyframe_007_feat14_frame54_att0.368.jpg
+Gloss   8: keyframe_008_feat17_frame66_att0.252.jpg
+Gloss   9: keyframe_009_feat19_frame73_att0.884.jpg
+Gloss  10: keyframe_010_feat0_frame1_att0.118.jpg
+Gloss  11: keyframe_011_feat27_frame104_att0.164.jpg
+Gloss  12: keyframe_012_feat25_frame96_att0.265.jpg
+Gloss  13: keyframe_013_feat25_frame96_att0.282.jpg
+Gloss  14: keyframe_014_feat25_frame96_att0.278.jpg
+Gloss  15: keyframe_015_feat25_frame96_att0.277.jpg
+Gloss  16: keyframe_016_feat27_frame104_att0.219.jpg
+Gloss  17: keyframe_017_feat27_frame104_att0.190.jpg
+Gloss  18: keyframe_018_feat27_frame104_att0.225.jpg
+Gloss  19: keyframe_019_feat23_frame88_att0.150.jpg
+Gloss  20: keyframe_020_feat27_frame104_att0.151.jpg
+Gloss  21: keyframe_021_feat25_frame96_att0.360.jpg
+Gloss  22: keyframe_022_feat25_frame96_att0.153.jpg
+Gloss  23: keyframe_023_feat27_frame104_att0.144.jpg
+Gloss  24: keyframe_024_feat25_frame96_att0.144.jpg
+Gloss  25: keyframe_025_feat27_frame104_att0.186.jpg

SignX/detailed_prediction_20260101_150706/632051/attention_weights.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d89932ced21ae95e0d7e034d8b3917146effa747bf7236c5eed30dd8cb9a258a
+size 3040

SignX/detailed_prediction_20260101_150706/632051/debug_video_path.txt

@@ -0,0 +1,4 @@
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4'
+video_path type = <class 'str'>
+video_path is None: False
+bool(video_path): True

SignX/detailed_prediction_20260101_150706/632051/feature_frame_mapping.json

@@ -0,0 +1,176 @@
+{
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+  "feature_count": 28,
+  "downsampling_ratio": 3.7857142857142856,
+  "fps": 24.0,
+  "mapping": [
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+  ]
+}

SignX/detailed_prediction_20260101_150706/632051/frame_alignment.json

@@ -0,0 +1,86 @@
+{
+  "translation": "#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p",
+  "words": [
+    "#IF",
+    "FRIEND",
+    "GROUP/TOGETHER",
+    "DEPART",
+    "PARTY",
+    "IX-1p",
+    "JOIN",
+    "IX-1p"
+  ],
+  "total_video_frames": 28,
+  "frame_ranges": [
+    {
+      "word": "#IF",
+      "start_frame": 2,
+      "end_frame": 2,
+      "peak_frame": 2,
+      "avg_attention": 0.47214657068252563,
+      "confidence": "medium"
+    },
+    {
+      "word": "FRIEND",
+      "start_frame": 5,
+      "end_frame": 5,
+      "peak_frame": 5,
+      "avg_attention": 0.4252290427684784,
+      "confidence": "medium"
+    },
+    {
+      "word": "GROUP/TOGETHER",
+      "start_frame": 8,
+      "end_frame": 8,
+      "peak_frame": 8,
+      "avg_attention": 0.37518179416656494,
+      "confidence": "medium"
+    },
+    {
+      "word": "DEPART",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.3480324149131775,
+      "confidence": "medium"
+    },
+    {
+      "word": "PARTY",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.38299673795700073,
+      "confidence": "medium"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.33272165060043335,
+      "confidence": "medium"
+    },
+    {
+      "word": "JOIN",
+      "start_frame": 11,
+      "end_frame": 11,
+      "peak_frame": 11,
+      "avg_attention": 0.5199229121208191,
+      "confidence": "high"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 14,
+      "end_frame": 14,
+      "peak_frame": 14,
+      "avg_attention": 0.3677118122577667,
+      "confidence": "medium"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.4029928669333458,
+    "high_confidence_words": 1,
+    "medium_confidence_words": 7,
+    "low_confidence_words": 0
+  }
+}

SignX/detailed_prediction_20260101_150706/632051/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p<br>
+            <strong>Total Words:</strong> 8 |
+            <strong>Total Features:</strong> 28
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    帧的注意力权重 ≥ (峰值权重 × 阈值%) 时被认为是"显著帧"
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                每个词显示为彩色矩形，宽度表示该词对应的特征帧范围。★ = 峰值帧。矩形内部显示注意力权重波形。
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "#IF", "word_idx": 0, "weights": [0.013499895110726357, 0.02982642501592636, 0.47214657068252563, 0.4107391834259033, 0.04950176924467087, 0.011385880410671234, 0.007043282967060804, 0.0014652750687673688, 0.0005238102748990059, 0.00040972864371724427, 0.0001160625834017992, 6.416538963094354e-05, 5.9505786339286715e-05, 5.076597517472692e-05, 6.82844765833579e-05, 0.00012157609307905659, 6.597878382308409e-05, 0.00010269331687595695, 0.00013462362403515726, 6.423696322599426e-05, 8.642762986710295e-05, 9.25226995605044e-05, 0.00011670421372400597, 0.0001578366500325501, 0.00020240909361746162, 0.0003825947642326355, 0.0007172566256485879, 0.0008544913143850863]}, {"word": "FRIEND", "word_idx": 1, "weights": [0.009660173207521439, 0.010518566705286503, 0.011222519911825657, 0.014483344741165638, 0.1795402616262436, 0.4252290427684784, 0.25737643241882324, 0.05393827706575394, 0.01512613520026207, 0.013365501537919044, 0.002376752672716975, 0.00014935070066712797, 8.692959818290547e-05, 0.0004998841905035079, 0.0008451194153167307, 0.0011626698542386293, 0.00042453958303667605, 0.00017692227265797555, 0.00016767902707215399, 4.8644251364748925e-05, 8.348096889676526e-05, 0.0001094180770451203, 0.00030694258748553693, 0.0002885134017560631, 0.00031121523352339864, 0.0006241592927835882, 0.0008697768207639456, 0.0010077793849632144]}, {"word": "GROUP/TOGETHER", "word_idx": 2, "weights": [0.010994982905685902, 0.004551935940980911, 0.002873026067391038, 0.003936904948204756, 0.008626177906990051, 0.014811795204877853, 0.02318989858031273, 0.12032425403594971, 0.37518179416656494, 0.2971201539039612, 0.08549409359693527, 0.014250868931412697, 0.008063109591603279, 0.00339426938444376, 0.0037573552690446377, 0.004879903048276901, 0.0018731161253526807, 0.0011690640822052956, 0.0013268929906189442, 0.0007135092164389789, 0.000632062554359436, 0.000777124660089612, 0.0009553946438245475, 0.0009487943025305867, 0.0007010120898485184, 0.001496487995609641, 0.0037835948169231415, 0.004172381013631821]}, {"word": "DEPART", "word_idx": 3, "weights": [0.22514434158802032, 0.12377114593982697, 0.00781786348670721, 0.0074639273807406425, 0.01298774778842926, 0.00438598683103919, 0.004350316245108843, 0.006786263547837734, 0.006216868292540312, 0.0061629218980669975, 0.004193580709397793, 0.0015793128404766321, 0.0011525226291269064, 0.0014239393640309572, 0.0007423617644235492, 0.0008507575839757919, 0.0008870838792063296, 0.00024679809575900435, 0.00034805957693606615, 0.005230794660747051, 0.0011639633448794484, 0.001367528340779245, 0.010013289749622345, 0.018452608957886696, 0.0018141826149076223, 0.001117207808420062, 0.19629621505737305, 0.3480324149131775]}, {"word": "PARTY", "word_idx": 4, "weights": [0.1664648950099945, 0.06123431771993637, 0.0020844682585448027, 0.0020428383722901344, 0.0058554718270897865, 0.004360921215265989, 0.004692059941589832, 0.009323552250862122, 0.015183845534920692, 0.016528787091374397, 0.015347503125667572, 0.007253072690218687, 0.005231750197708607, 0.009598116390407085, 0.00704572768881917, 0.007053114008158445, 0.006423295009881258, 0.0010452027199789882, 0.0009786873124539852, 0.004494669381529093, 0.005323153454810381, 0.006433582864701748, 0.022334398701786995, 0.03912580758333206, 0.004556183237582445, 0.0021732028108090162, 0.18481463193893433, 0.38299673795700073]}, {"word": "IX-1p", "word_idx": 5, "weights": [0.2268882542848587, 0.10439852625131607, 0.005018203519284725, 0.005008632782846689, 0.005379822570830584, 0.00215631234459579, 0.0024426421150565147, 0.007580526173114777, 0.011461855843663216, 0.010575865395367146, 0.010204891674220562, 0.004322281572967768, 0.0023845669347792864, 0.0016265056328848004, 0.0011272492120042443, 0.0014091862831264734, 0.0019118450582027435, 0.0019068039255216718, 0.002558623207733035, 0.005466249771416187, 0.002576562575995922, 0.0033958060666918755, 0.014094071462750435, 0.03357496112585068, 0.005502632353454828, 0.003941097296774387, 0.19036439061164856, 0.33272165060043335]}, {"word": "JOIN", "word_idx": 6, "weights": [0.006536237895488739, 0.002151536289602518, 0.0006580766057595611, 0.0008207014761865139, 0.0003112705599050969, 0.0003111894184257835, 0.0008894064230844378, 0.004121360369026661, 0.01069970428943634, 0.008291625417768955, 0.01931559480726719, 0.5199229121208191, 0.40212148427963257, 0.004480497911572456, 0.0010337198618799448, 0.0007998707587830722, 0.00024323497200384736, 7.284984894795343e-05, 0.00011325528612360358, 0.00540410028770566, 0.0011726750526577234, 0.0009422790608368814, 0.0003188242844771594, 0.00024731658049859107, 3.1396619306178764e-05, 4.355102646513842e-05, 0.0036189379170536995, 0.005326398182660341]}, {"word": "IX-1p", "word_idx": 7, "weights": [0.0013159031514078379, 0.0007256589597091079, 0.00017777174070943147, 0.0001744187029544264, 0.00025140171055682003, 0.00039260604535229504, 0.0003829205525107682, 0.000333531730575487, 0.0007308170897886157, 0.0010108469286933541, 0.0015992401167750359, 0.003526317421346903, 0.012568545527756214, 0.2852487564086914, 0.3677118122577667, 0.19535787403583527, 0.07697467505931854, 0.012815488502383232, 0.007124335505068302, 0.0009805350564420223, 0.007633780129253864, 0.007437399588525295, 0.005485337693244219, 0.003693929873406887, 0.0026681837625801563, 0.0011110405903309584, 0.0008843602845445275, 0.0016825739294290543]}];
+        const numGlosses = 8;
+        const numFeatures = 28;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/detailed_prediction_20260101_150706/632051/translation.txt

@@ -0,0 +1,3 @@
+With BPE: #IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p JO@@ I@@ N IX-1p
+Clean: #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p
+Ground Truth: #IF FRIEND GROUP/TOGETHER GO-OUT PARTY IX-1p JOIN IX-1p

SignX/eval/attention_analysis.py

@@ -82,6 +82,7 @@ class AttentionAnalyzer:
 
         # 计算词-帧对应关系
         self.word_frame_ranges = self._compute_word_frame_ranges()
+        self.frame_attention_strength = self._compute_frame_attention_strength()
 
     def _compute_word_frame_ranges(self):
         """
@@ -153,6 +154,54 @@ class AttentionAnalyzer:
 
         return word_ranges
 
+    def _compute_frame_attention_strength(self):
+        """Compute average attention per feature frame (normalized 0-1)."""
+        if self.attn_best.size == 0:
+            return np.zeros(self.video_frames, dtype=np.float32)
+
+        if self.attn_best.ndim == 1:
+            frame_strength = self.attn_best.copy()
+        else:
+            frame_strength = self.attn_best.mean(axis=0)
+
+        if frame_strength.shape[0] != self.video_frames:
+            frame_strength = np.resize(frame_strength, self.video_frames)
+
+        max_val = frame_strength.max()
+        if max_val > 0:
+            frame_strength = frame_strength / max_val
+        return frame_strength
+
+    def _map_strength_to_original_frames(self, mapping_list, original_frame_count):
+        """Map latent attention strength to original video frame resolution."""
+        if not mapping_list or original_frame_count <= 0:
+            return None
+
+        orig_strength = np.zeros(original_frame_count, dtype=np.float32)
+        counts = np.zeros(original_frame_count, dtype=np.float32)
+
+        for feat_idx, mapping in enumerate(mapping_list):
+            if feat_idx >= len(self.frame_attention_strength):
+                break
+            start = int(mapping.get('frame_start', 0))
+            end = int(mapping.get('frame_end', start))
+            end = max(end, start + 1)
+            start = max(start, 0)
+            end = min(end, original_frame_count)
+            if start >= end:
+                continue
+            orig_strength[start:end] += self.frame_attention_strength[feat_idx]
+            counts[start:end] += 1
+
+        mask = counts > 0
+        if mask.any():
+            orig_strength[mask] = orig_strength[mask] / counts[mask]
+
+        max_val = orig_strength.max()
+        if max_val > 0:
+            orig_strength = orig_strength / max_val
+        return orig_strength
+
     def generate_all_visualizations(self, output_dir):
         """
         生成所有可视化图表
@@ -280,6 +329,7 @@ class AttentionAnalyzer:
         mapping_list = None
         orig_full_limit = None
         orig_short_limit = None
+        pixel_strength_curve = None
         if feature_mapping:
             original_frame_count = feature_mapping.get('original_frame_count', self.video_frames)
             mapping_list = feature_mapping.get('mapping', [])
@@ -287,14 +337,15 @@ class AttentionAnalyzer:
             if mapping_list:
                 idx = min(max_feat_end, len(mapping_list) - 1)
                 orig_short_limit = mapping_list[idx]['frame_end'] + 2
+            pixel_strength_curve = self._map_strength_to_original_frames(mapping_list, original_frame_count)
 
         def render_alignment(out_path, latent_xlim_end, orig_xlim_end=None):
             if feature_mapping:
-                fig = plt.figure(figsize=(18, 10))
-                gs = GridSpec(4, 1, height_ratios=[4, 1, 1, 0.5], hspace=0.4)
+                fig = plt.figure(figsize=(18, 9))
+                gs = GridSpec(3, 1, height_ratios=[4, 1, 1], hspace=0.32)
             else:
-                fig = plt.figure(figsize=(18, 8))
-                gs = GridSpec(3, 1, height_ratios=[4, 1, 0.5], hspace=0.4)
+                fig = plt.figure(figsize=(18, 7.5))
+                gs = GridSpec(2, 1, height_ratios=[4, 1], hspace=0.32)
 
             # === 上图: 词-帧对齐 ===
             ax1 = fig.add_subplot(gs[0])
@@ -354,6 +405,11 @@ class AttentionAnalyzer:
             ax2.set_title('Latent Feature Timeline', fontsize=13, fontweight='bold')
             ax2.grid(True, alpha=0.3, axis='x', linestyle='--')
 
+            if self.frame_attention_strength is not None and len(self.frame_attention_strength) >= self.video_frames:
+                latent_curve_x = np.arange(self.video_frames)
+                latent_curve_y = self.frame_attention_strength[:self.video_frames] * 0.6 - 0.3
+                ax2.plot(latent_curve_x, latent_curve_y, color='#E53935', linewidth=1.5, alpha=0.9)
+
             timeline_axes = [ax2]
 
             if feature_mapping:
@@ -383,16 +439,12 @@ class AttentionAnalyzer:
                              f'{feature_mapping["downsampling_ratio"]:.2f}x downsampling)',
                              fontsize=13, fontweight='bold')
                 ax3.grid(True, alpha=0.3, axis='x', linestyle='--')
-                timeline_axes.append(ax3)
-                legend_row = 3
-            else:
-                legend_row = 2
 
-            ax_legend = fig.add_subplot(gs[legend_row])
-            ax_legend.axis('off')
-            legend_text = "Confidence:  ■ High (avg attn > 0.5)  ■ Medium (0.2-0.5)  ■ Low (< 0.2)"
-            ax_legend.text(0.5, 0.5, legend_text, ha='center', va='center',
-                    fontsize=11, transform=ax_legend.transAxes)
+                if pixel_strength_curve is not None and len(pixel_strength_curve) >= original_frame_count:
+                    pixel_curve_x = np.arange(original_frame_count)
+                    pixel_curve_y = pixel_strength_curve[:original_frame_count] * 0.6 - 0.3
+                    ax3.plot(pixel_curve_x, pixel_curve_y, color='#E53935', linewidth=1.5, alpha=0.9)
+                timeline_axes.append(ax3)
 
             plt.tight_layout()
             fig.canvas.draw()

SignX/inference_output.txt

@@ -1 +1 @@
-BOX/ROOM I@@ X NOT-YET ARRIVE I@@ X SHOULD CONT@@ ACT ns-fs-@@ F@@ E@@ DE@@ X
+#IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p JO@@ I@@ N IX-1p

SignX/inference_output.txt.clean

@@ -1 +1 @@
-BOX/ROOM IX NOT-YET ARRIVE IX SHOULD CONTACT ns-fs-FEDEX
+#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p