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#!/bin/bash
# 测试 SMKD 视频特征提取的效率 (包含 pose 协助)
set -e

GREEN='\033[0;32m'
BLUE='\033[0;34m'
NC='\033[0m'

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"

echo ""
echo "======================================================================"
echo "  SMKD Feature Extraction Benchmark (with Pose Assistance)"
echo "======================================================================"
echo ""

# 激活conda
CONDA_BASE=$(conda info --base 2>/dev/null || echo "")
source "${CONDA_BASE}/etc/profile.d/conda.sh"

# 切换到 PyTorch 环境
conda activate signx-slt

if [ $? -ne 0 ]; then
    echo "错误: 无法激活 signx-slt 环境"
    exit 1
fi

# 使用测试集视频进行基准测试
TEST_VIDEO_DIR="${PROJECT_ROOT}/eval/tiny_test_data/videos"

if [ ! -d "$TEST_VIDEO_DIR" ]; then
    echo "错误: 测试视频目录不存在: $TEST_VIDEO_DIR"
    exit 1
fi

# 获取所有测试视频
TEST_VIDEOS=($(ls ${TEST_VIDEO_DIR}/*.mp4 2>/dev/null | head -10))
NUM_VIDEOS=${#TEST_VIDEOS[@]}

if [ $NUM_VIDEOS -eq 0 ]; then
    echo "错误: 未找到测试视频"
    exit 1
fi

echo "找到 $NUM_VIDEOS 个测试视频"
echo ""

# 创建临时视频列表文件
TEMP_DIR=$(mktemp -d)
VIDEO_LIST_FILE="$TEMP_DIR/video_list.txt"

for video in "${TEST_VIDEOS[@]}"; do
    echo "$video" >> "$VIDEO_LIST_FILE"
done

FEATURE_OUTPUT="$TEMP_DIR/features.h5"

# 配置文件（使用禁用 pose assistance 的配置）
SMKD_CONFIG="${PROJECT_ROOT}/smkd/asllrp_baseline_benchmark.yaml"
SMKD_MODEL="${PROJECT_ROOT}/smkd/work_dir第一次训练的基线/asllrp_smkd/best_model.pt"
GLOSS_DICT="${PROJECT_ROOT}/smkd/asllrp/gloss_dict.npy"

cd "$PROJECT_ROOT"

echo -e "${BLUE}开始 SMKD 特征提取基准测试...${NC}"
echo ""

# 记录GPU功耗（后台进程）
nvidia-smi --query-gpu=power.draw --format=csv,noheader,nounits -l 1 > /tmp/power_smkd.log &
POWER_PID=$!

# 测量特征提取时间
START=$(date +%s.%N)

python -c "
import sys
import os
sys.path.insert(0, 'smkd')

from smkd.sign_embedder import SignEmbedding
import h5py
import numpy as np

print('  加载 SMKD 模型...')
embedder = SignEmbedding(
    cfg='$SMKD_CONFIG',
    gloss_path='$GLOSS_DICT',
    sign_video_path='$VIDEO_LIST_FILE',
    model_path='$SMKD_MODEL',
    gpu_id='0',
    batch_size=1
)

print('  提取特征...')
features = embedder.embed()

print('  保存特征到 h5 文件...')
with h5py.File('$FEATURE_OUTPUT', 'w') as hf:
    for key, feature in features.items():
        hf.create_dataset(key, data=feature)

print('  ✓ 特征提取完成')
print('  特征数量:', len(features))
"

END=$(date +%s.%N)

# 停止功耗监控
kill $POWER_PID 2>/dev/null || true

# 计算结果
SMKD_TIME=$(echo "$END - $START" | bc)
SMKD_POWER=$(awk '{ sum += $1; n++ } END { if (n > 0) print sum / n }' /tmp/power_smkd.log)
SMKD_FPS=$(echo "scale=2; $NUM_VIDEOS / $SMKD_TIME" | bc)

echo ""
echo -e "${GREEN}✓ SMKD 特征提取完成${NC}"
echo "  处理视频数: $NUM_VIDEOS"
echo "  总时间: ${SMKD_TIME}s"
echo "  平均功耗: ${SMKD_POWER}W"
echo "  FPS: $SMKD_FPS"
echo ""

# 清理
rm -rf "$TEMP_DIR"
rm -f /tmp/power_smkd.log

echo "======================================================================"
echo "  SMKD Benchmark Results"
echo "======================================================================"
echo ""
echo "Configuration: SMKD (视频→特征, 包含 Pose 协助)"
echo "Videos: $NUM_VIDEOS"
echo "Time: ${SMKD_TIME}s"
echo "FPS: $SMKD_FPS"
echo "Power: ${SMKD_POWER}W"
echo ""
echo -e "${GREEN}✓ Benchmark complete!${NC}"
echo ""