602 lines
20 KiB
Python
602 lines
20 KiB
Python
"""
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JE-Skin DevKit — Python gRPC Sensor Server
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提供两个服务：
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1. SensorPush (streaming) — 接收实时传感器帧
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2. ExportProcessor (unary) — 处理导出的 CSV 文件：梯度过滤、xlsx 转换
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安装依赖：
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pip install grpcio grpcio-tools openpyxl
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生成 gRPC 代码：
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python -m grpc_tools.protoc -I../src-tauri/proto --python_out=. --grpc_python_out=. ../src-tauri/proto/sensor_stream.proto
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启动：
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python sensor_server.py [--port 50051]
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"""
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from __future__ import annotations
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import argparse
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import csv
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import os
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import signal
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import statistics
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import sys
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import time
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from concurrent import futures
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from pathlib import Path
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import grpc
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import sensor_stream_pb2
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import sensor_stream_pb2_grpc
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# ── 梯度过滤逻辑（来自用户的 main.py） ─────────────────────────
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def load_rows(path: Path) -> list[list[str]]:
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with path.open("r", encoding="utf-8-sig", newline="") as f:
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return [row for row in csv.reader(f) if row]
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def row_sum(row: list[str]) -> float:
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return sum(float(v) for v in row[1:] if v.strip())
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def find_threshold(sum_values: list[float]) -> float:
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if len(sum_values) < 2:
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raise ValueError("At least two rows are required.")
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sorted_v = sorted(sum_values)
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idx = max(
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range(len(sorted_v) - 1),
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key=lambda i: sorted_v[i + 1] - sorted_v[i],
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)
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return (sorted_v[idx] + sorted_v[idx + 1]) / 2.0
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def extract_press_groups(
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rows: list[list[str]], sum_values: list[float], threshold: float
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) -> tuple[list[list[str]], list[float]]:
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filtered: list[list[str]] = []
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group_means: list[float] = []
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current_group: list[float] = []
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for row, total in zip(rows, sum_values):
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if total >= threshold:
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filtered.append(row)
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current_group.append(total)
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continue
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if current_group:
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group_means.append(statistics.fmean(current_group))
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current_group = []
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if current_group:
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group_means.append(statistics.fmean(current_group))
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return filtered, group_means
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def write_csv(path: Path, rows: list[list[str]]) -> Path:
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out = path.with_name(f"{path.stem}_filtered.csv")
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with out.open("w", encoding="utf-8-sig", newline="") as f:
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csv.writer(f).writerows(rows)
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return out
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def write_xlsx(path: Path, rows: list[list[str]], stats: dict) -> Path:
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"""将过滤后的数据和统计信息写入 xlsx"""
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try:
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import openpyxl
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except ImportError:
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raise RuntimeError("openpyxl is required for xlsx output. Install it with: pip install openpyxl")
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from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
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wb = openpyxl.Workbook()
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# Sheet 1: 过滤后的数据
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ws_data = wb.active
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ws_data.title = "Filtered Data"
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for row in rows:
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ws_data.append([float(c) if c.strip().replace(".", "").replace("-", "").isdigit() else c for c in row])
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# Sheet 2: 统计信息
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ws_stats = wb.create_sheet("Statistics")
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header_font = Font(bold=True, size=11)
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header_fill = PatternFill(start_color="E0E0E0", end_color="E0E0E0", fill_type="solid")
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ws_stats.append(["Parameter", "Value"])
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ws_stats["A1"].font = header_font
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ws_stats["A1"].fill = header_fill
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ws_stats["B1"].font = header_font
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ws_stats["B1"].fill = header_fill
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stats_rows = [
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("Source File", stats.get("source_file", "")),
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("Total Rows", stats.get("rows_total", 0)),
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("Filtered Rows", stats.get("rows_kept", 0)),
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("Groups Used", stats.get("groups_used", 0)),
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("Mean Value", f"{stats.get('mean_value', 0):.3f}"),
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("Threshold", f"{stats.get('threshold', 0):.3f}"),
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("Process Time", stats.get("process_time", "")),
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]
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for label, value in stats_rows:
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ws_stats.append([label, value])
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ws_stats.column_dimensions["A"].width = 18
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ws_stats.column_dimensions["B"].width = 30
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out = path.with_name(f"{path.stem}_filtered.xlsx")
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wb.save(str(out))
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return out
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def process_csv(csv_path: str, save_as_xlsx: bool) -> dict:
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"""执行梯度过滤，返回结果统计"""
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path = Path(csv_path)
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if not path.is_file():
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raise FileNotFoundError(f"CSV file not found: {csv_path}")
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rows = load_rows(path)
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if not rows:
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raise ValueError("CSV file is empty.")
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sum_values = [row_sum(r) for r in rows]
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threshold = find_threshold(sum_values)
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filtered_rows, group_means = extract_press_groups(rows, sum_values, threshold)
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if not filtered_rows:
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raise ValueError("No large press-down data was found.")
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overall_mean = statistics.fmean(group_means)
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stats = {
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"source_file": path.name,
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"rows_total": len(rows),
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"rows_kept": len(filtered_rows),
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"groups_used": len(group_means),
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"mean_value": overall_mean,
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"threshold": threshold,
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"process_time": time.strftime("%Y-%m-%d %H:%M:%S"),
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}
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if save_as_xlsx:
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output_path = write_xlsx(path, filtered_rows, stats)
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# 删除源 CSV
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try:
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path.unlink()
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except OSError:
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pass
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else:
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output_path = write_csv(path, filtered_rows)
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# 用过滤后的文件替换源文件
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try:
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path.unlink()
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output_path.rename(path)
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output_path = path
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except OSError:
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pass
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# 追加一行到汇总 xlsx
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_append_analysis_log(csv_path, stats)
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return {
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"ok": True,
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"output_path": str(output_path),
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"groups_used": len(group_means),
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"mean_value": overall_mean,
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"threshold": threshold,
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"rows_total": len(rows),
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"rows_kept": len(filtered_rows),
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"message": "OK",
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}
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def _append_analysis_log(source_csv: str, stats: dict):
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"""将处理结果追加到 devkit_analysis_results.xlsx"""
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try:
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import openpyxl
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except ImportError:
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return # openpyxl 不可用时跳过
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log_path = Path(source_csv).parent / "devkit_analysis_results.xlsx"
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if log_path.exists():
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wb = openpyxl.load_workbook(str(log_path))
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ws = wb.active
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else:
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wb = openpyxl.Workbook()
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ws = wb.active
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ws.title = "Analysis Log"
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ws.append(["Time", "Source File", "Total Rows", "Kept Rows",
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"Groups", "Mean Value", "Threshold", "Output File"])
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ws.append([
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stats.get("process_time", ""),
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stats.get("source_file", ""),
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stats.get("rows_total", 0),
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stats.get("rows_kept", 0),
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stats.get("groups_used", 0),
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round(stats.get("mean_value", 0), 3),
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round(stats.get("threshold", 0), 3),
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f"{Path(stats.get('source_file', '')).stem}_filtered",
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])
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wb.save(str(log_path))
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# ── gRPC 服务实现 ────────────────────────────────────────────────
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class SensorPushServicer(sensor_stream_pb2_grpc.SensorPushServicer):
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"""接收实时传感器帧（streaming）"""
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_csv_path = None # 类变量，记录当前 CSV 路径
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def __init__(self):
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self.frame_count = 0
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self.last_report_time = time.time()
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self.last_angle = None
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self._csv_file = None
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self._csv_writer = None
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def _open_csv(self):
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"""打开一个新的 CSV 文件用于持续写入"""
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ts = time.strftime("%Y%m%d_%H%M%S")
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SensorPushServicer._csv_path = os.path.join(os.getcwd(), f"sensor_log_{ts}.csv")
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self._csv_file = open(SensorPushServicer._csv_path, "w", newline="", encoding="utf-8-sig")
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self._csv_writer = csv.writer(self._csv_file)
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header = ["seq", "timestamp_ms", "dts_ms", "angle", "magnitude", "state", "resultant_force"] + [f"ch{i}" for i in range(SENSOR_ROWS * SENSOR_COLS)]
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self._csv_writer.writerow(header)
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self._csv_file.flush()
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print(f"[SensorPush] CSV logging to: {SensorPushServicer._csv_path}")
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def _close_csv(self):
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"""关闭 CSV 文件"""
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if self._csv_file:
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self._csv_file.close()
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print(f"[SensorPush] CSV saved: {SensorPushServicer._csv_path}")
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self._csv_file = None
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self._csv_writer = None
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def Upload(self, request_iterator, context):
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print("[SensorPush] Client connected, waiting for frames...")
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reset_baseline()
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self.last_angle = None
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self.frame_count = 0
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self._open_csv()
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for frame in request_iterator:
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self.frame_count += 1
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angle = 0.0
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magnitude = 0.0
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state = 0
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ok = True
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message = "OK"
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if len(frame.matrix) == SENSOR_ROWS * SENSOR_COLS:
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try:
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angle, magnitude, state = get_pzt_angle(frame.matrix)
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self.last_angle = angle
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# 打印接收到的数据
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print(f"[Recv #{frame.seq}] seq={frame.seq} ts={frame.timestamp_ms} dts={frame.dts_ms} "
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f"data={list(frame.matrix)}")
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except Exception as e:
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ok = False
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message = str(e)
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print(f"[SensorPush] PZT compute error on frame #{frame.seq}: {e}")
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else:
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ok = False
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message = f"Invalid matrix length: {len(frame.matrix)}"
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print(f"[Recv #{frame.seq}] INVALID len={len(frame.matrix)}")
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# 持续写入 CSV
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if self._csv_writer:
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row = [frame.seq, frame.timestamp_ms, frame.dts_ms,
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f"{angle:.4f}", f"{magnitude:.4f}", state, frame.resultant_force]
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row += list(frame.matrix)
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self._csv_writer.writerow(row)
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if self.frame_count % 10 == 0:
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self._csv_file.flush()
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yield sensor_stream_pb2.PztAngleResponse(
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seq=frame.seq,
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timestamp_ms=frame.timestamp_ms,
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angle=angle,
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dts_ms=frame.dts_ms,
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ok=ok,
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message=message,
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)
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if self.frame_count % 100 == 0:
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now = time.time()
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elapsed = now - self.last_report_time
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fps = 100 / elapsed if elapsed > 0 else 0
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self.last_report_time = now
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angle_text = (
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f"{self.last_angle:.2f}"
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if self.last_angle is not None
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else "n/a"
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)
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print(
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f"[SensorPush] Frame #{frame.seq} | "
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f"{frame.rows}x{frame.cols} | "
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f"angle={angle_text} | "
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f"force={frame.resultant_force:.1f} | "
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f"total={self.frame_count} | ~{fps:.1f} fps"
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)
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self._close_csv()
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print(f"[SensorPush] Stream ended. Total: {self.frame_count}")
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class ExportProcessorServicer(sensor_stream_pb2_grpc.ExportProcessorServicer):
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"""处理导出的 CSV 文件（unary）"""
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def ProcessFile(self, request, context):
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csv_path = request.csv_path
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save_as_xlsx = request.save_as_xlsx
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print(f"[ExportProcessor] Processing: {csv_path} (xlsx={save_as_xlsx})")
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try:
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result = process_csv(csv_path, save_as_xlsx)
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return sensor_stream_pb2.ProcessResponse(
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ok=result["ok"],
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output_path=result["output_path"],
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groups_used=result["groups_used"],
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mean_value=result["mean_value"],
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threshold=result["threshold"],
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rows_total=result["rows_total"],
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rows_kept=result["rows_kept"],
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message=result["message"],
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)
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except Exception as e:
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print(f"[ExportProcessor] Error: {e}")
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return sensor_stream_pb2.ProcessResponse(
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ok=False,
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output_path="",
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message=str(e),
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)
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# ── 启动 ────────────────────────────────────────────────────────
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def serve(port: int):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=4))
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sensor_stream_pb2_grpc.add_SensorPushServicer_to_server(SensorPushServicer(), server)
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sensor_stream_pb2_grpc.add_ExportProcessorServicer_to_server(ExportProcessorServicer(), server)
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listen_addr = f"0.0.0.0:{port}"
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server.add_insecure_port(listen_addr)
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server.start()
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print(f"[DevKit Server] gRPC listening on {listen_addr}")
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print(f"[DevKit Server] Services: SensorPush (streaming), ExportProcessor (unary)")
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def shutdown(signum, frame):
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print("\n[DevKit Server] Shutting down...")
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server.stop(grace=5)
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sys.exit(0)
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signal.signal(signal.SIGINT, shutdown)
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signal.signal(signal.SIGTERM, shutdown)
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server.wait_for_termination()
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import numpy as np
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import threading
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from collections import deque
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# ===================== 算法参数=====================
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COP_INIT_MEDIAN_FRAMES = 15 # 初始COP取中位数的帧数
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NOISE_COLLECT_FRAMES = 20 # 动态阈值基线采集帧数
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THRESH_K = 5 # 阈值 = mean + K * std
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SENSOR_ROWS = 12
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SENSOR_COLS = 7
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# ===================== 二次静置精修参数 =====================
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POST_INIT_WINDOW_CNT = 60000
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POST_INIT_STABLE_CNT = 200
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POST_INIT_STABLE_THRESH = 0.1
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# ===================== 线程安全全局状态 =====================
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first_frame = None
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first_frame_lock = threading.Lock()
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first_contact_CoP_x = None
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first_contact_CoP_y = None
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contact_initialized = False
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# 候选初始CoP缓冲
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cop_init_x_buf = deque(maxlen=COP_INIT_MEDIAN_FRAMES)
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cop_init_y_buf = deque(maxlen=COP_INIT_MEDIAN_FRAMES)
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# 动态阈值
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noise_sum_buf = deque(maxlen=NOISE_COLLECT_FRAMES)
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dynamic_thresh = None
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# 二次静置精修状态
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post_init_frame_cnt = 0
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post_stable_cnt = 0
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post_refined_flag = False
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post_cand_x = None
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post_cand_y = None
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# ===================== 基线减除 =====================
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def subtract_baseline(current_frame):
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global first_frame
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current_frame = np.array(current_frame, dtype=np.float32).flatten()
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with first_frame_lock:
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if first_frame is None:
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first_frame = current_frame.copy()
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diff = current_frame - first_frame
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return np.clip(diff, 0, None)
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# ===================== 重置CoP状态 =====================
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def reset_cop_state():
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global first_contact_CoP_x, first_contact_CoP_y, contact_initialized
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global post_init_frame_cnt, post_stable_cnt, post_refined_flag
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global post_cand_x, post_cand_y
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first_contact_CoP_x = None
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first_contact_CoP_y = None
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contact_initialized = False
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cop_init_x_buf.clear()
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cop_init_y_buf.clear()
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post_init_frame_cnt = 0
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post_stable_cnt = 0
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post_refined_flag = False
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post_cand_x = None
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post_cand_y = None
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# ===================== CoP压力中心计算 =====================
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def compute_pressure_direction(baseline_subtracted_frame):
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global first_contact_CoP_x, first_contact_CoP_y, contact_initialized
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global post_init_frame_cnt, post_stable_cnt, post_refined_flag
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global post_cand_x, post_cand_y
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global noise_sum_buf, dynamic_thresh
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rows, cols = SENSOR_ROWS, SENSOR_COLS
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frame_flat = np.asarray(baseline_subtracted_frame, dtype=np.float32).flatten()
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frame2d = frame_flat.reshape(rows, cols)
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total_pressure = np.sum(frame2d)
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# 动态阈值
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if dynamic_thresh is None:
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noise_sum_buf.append(total_pressure)
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if len(noise_sum_buf) >= NOISE_COLLECT_FRAMES:
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sums = np.array(noise_sum_buf)
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dynamic_thresh = float(np.mean(sums) + THRESH_K * np.std(sums))
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# 低压重置
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if dynamic_thresh is not None and total_pressure < dynamic_thresh:
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if contact_initialized:
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reset_cop_state()
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return 0.0, 0.0, 0, rows-1, 0, cols-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0
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if total_pressure == 0:
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return 0.0, 0.0, 0, rows-1, 0, cols-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0
|
||
|
||
x_grid = np.tile(np.arange(cols), (rows, 1))
|
||
y_grid = np.repeat(np.arange(rows), cols).reshape(rows, cols)
|
||
cop_x = np.sum(frame2d * x_grid) / total_pressure
|
||
cop_y = np.sum(frame2d * y_grid) / total_pressure
|
||
|
||
delta_CoP_x = 0.0
|
||
delta_CoP_y = 0.0
|
||
base_x = cop_x
|
||
base_y = cop_y
|
||
|
||
# ============ 初始点稳定判断（中位数判定） ============
|
||
if not contact_initialized:
|
||
cop_init_x_buf.append(cop_x)
|
||
cop_init_y_buf.append(cop_y)
|
||
|
||
if len(cop_init_x_buf) >= COP_INIT_MEDIAN_FRAMES:
|
||
xs = list(cop_init_x_buf)
|
||
ys = list(cop_init_y_buf)
|
||
first_contact_CoP_x = float(np.median(xs))
|
||
first_contact_CoP_y = float(np.median(ys))
|
||
print(f"[CoP Init] 前{COP_INIT_MEDIAN_FRAMES}帧坐标:")
|
||
for i in range(len(xs)):
|
||
print(f" frame {i}: x={xs[i]:.3f}, y={ys[i]:.3f}")
|
||
print(f" 中位数: x={first_contact_CoP_x:.3f}, y={first_contact_CoP_y:.3f}")
|
||
contact_initialized = True
|
||
cop_init_x_buf.clear()
|
||
cop_init_y_buf.clear()
|
||
|
||
# ========== 计算偏移量 ==========
|
||
else:
|
||
# 二次静置精修
|
||
post_init_frame_cnt += 1
|
||
if not post_refined_flag and post_init_frame_cnt <= POST_INIT_WINDOW_CNT:
|
||
if post_cand_x is not None:
|
||
dist_val = np.hypot(cop_x - post_cand_x, cop_y - post_cand_y)
|
||
if dist_val <= POST_INIT_STABLE_THRESH:
|
||
post_stable_cnt += 1
|
||
else:
|
||
post_cand_x = cop_x
|
||
post_cand_y = cop_y
|
||
post_stable_cnt = 1
|
||
else:
|
||
post_cand_x = cop_x
|
||
post_cand_y = cop_y
|
||
post_stable_cnt = 1
|
||
|
||
if post_stable_cnt >= POST_INIT_STABLE_CNT:
|
||
first_contact_CoP_x = post_cand_x
|
||
first_contact_CoP_y = post_cand_y
|
||
post_refined_flag = True
|
||
else:
|
||
post_refined_flag = True
|
||
|
||
delta_CoP_x = cop_x - first_contact_CoP_x
|
||
delta_CoP_y = first_contact_CoP_y - cop_y
|
||
base_x = first_contact_CoP_x
|
||
base_y = first_contact_CoP_y
|
||
|
||
magnitude = np.hypot(delta_CoP_x, delta_CoP_y)
|
||
if not contact_initialized:
|
||
state = 0
|
||
elif not post_refined_flag:
|
||
state = 1
|
||
else:
|
||
state = 2
|
||
|
||
return (cop_x, cop_y,
|
||
0, rows-1, 0, cols-1,
|
||
delta_CoP_x, delta_CoP_y,
|
||
base_x, base_y,
|
||
magnitude, state)
|
||
|
||
|
||
# ===================== 角度计算核心 =====================
|
||
def compute_vector_angle(x: float, y: float) -> tuple[float, float]:
|
||
epsilon = 1e-8
|
||
mag = np.hypot(x, y)
|
||
angle = np.degrees(np.arctan2(y, x + epsilon))
|
||
if angle < 0:
|
||
angle += 360
|
||
return angle, mag
|
||
|
||
def compute_PZT_angle(Px: float, Py: float) -> tuple[float, float]:
|
||
return compute_vector_angle(Px, Py)
|
||
|
||
|
||
# ===================== 核心入口函数 =====================
|
||
def get_pzt_angle(adc_data):
|
||
if len(adc_data) != 84:
|
||
raise ValueError("ADC数据长度必须为84")
|
||
baseline_subtracted = subtract_baseline(adc_data)
|
||
result = compute_pressure_direction(baseline_subtracted)
|
||
dx, dy = result[6], result[7]
|
||
magnitude = result[10]
|
||
state = int(result[11])
|
||
pzt_angle, _ = compute_PZT_angle(dx, dy)
|
||
return pzt_angle, magnitude, state
|
||
|
||
|
||
# ===================== 重置基线（校准用） =====================
|
||
def reset_baseline():
|
||
global first_frame, dynamic_thresh
|
||
with first_frame_lock:
|
||
first_frame = None
|
||
dynamic_thresh = None
|
||
noise_sum_buf.clear()
|
||
reset_cop_state()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser(description="JE-Skin DevKit gRPC Server")
|
||
parser.add_argument("--port", type=int, default=50051, help="gRPC listen port (default: 50051)")
|
||
args = parser.parse_args()
|
||
serve(args.port)
|