238 lines
9.3 KiB
Python
238 lines
9.3 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import multiprocessing.shared_memory as shared_memory
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import time
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import zlib
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from matplotlib.animation import FuncAnimation
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import logging
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# 本文件用于演示如何从厂家提供的共享内存中持续读取压力矩阵，
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# 并实时可视化为热力图。代码仅做读取与显示，不负责写入共享内存。
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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class SharedMemoryContext:
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"""共享内存上下文管理器。
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作用：
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- 进入 with 语句时连接共享内存。
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- 退出 with 语句时自动关闭连接，避免句柄泄漏。
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"""
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def __init__(self, name):
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self.name = name
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self.shm = None
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def __enter__(self):
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# 按名字连接已存在的共享内存（由厂家驱动/进程创建）
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self.shm = shared_memory.SharedMemory(name=self.name)
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logging.info(f"Connected to shared memory: {self.name}")
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return self.shm
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def __exit__(self, exc_type, exc_val, exc_tb):
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# 只关闭连接，不 unlink（不能销毁由外部创建的共享内存）
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if self.shm is not None:
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self.shm.close()
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logging.info(f"Closed shared memory: {self.name}")
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def read_shared_memory_stream(name="x2_pressure"):
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"""生成器函数：持续从共享内存读取数据流。
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共享内存协议（按当前厂家示例）：
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- 前 72 字节：头部（元数据，当前示例未解析）
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- 后续数据区：288 * 64 个 float32（4 字节），总计 73728 字节
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- 数据矩阵形状：reshape 为 (288, 64)
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Yields:
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tuple(counter, event_tick, datas)
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- counter: 帧序号（从 0 开始）
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- event_tick: 相对启动时间（毫秒）
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- datas: ndarray，shape=(288,64)，dtype=float32
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"""
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counter = 0 # 帧计数器
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start_tick = int(time.time() * 1000) # 记录开始时间戳
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while True:
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try:
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# 每次循环使用 with 连接共享内存，确保异常时也能释放句柄
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with SharedMemoryContext(name) as shm:
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# 建立连接后，持续读取同一块共享内存
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while True:
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# 以 uint8 视图读取原始字节流（零拷贝）
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buffer = np.frombuffer(shm.buf, dtype=np.uint8)
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# 验证缓冲区长度是否满足“头部 + 数据区”最小要求
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if buffer.nbytes < 72 + 288 * 64 * 4:
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logging.warning("Data size is insufficient. Waiting for more data...")
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time.sleep(0.1)
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continue
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# 跳过 72 字节头部，仅解析数据区并转换为 float32 矩阵
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header = bytes(buffer[:72])
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datas = buffer[72:72 + 288 * 64 * 4].view(np.float32).reshape((288, 64))
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event_tick = int(time.time() * 1000) - start_tick # 计算相对时间戳
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# 通过生成器把一帧数据交给上层消费者（可视化或业务处理）
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yield counter, event_tick, datas, header
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counter += 1
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except FileNotFoundError:
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# 共享内存尚未创建：通常是写端程序未启动
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logging.warning("Waiting for shared memory...")
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time.sleep(1)
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except KeyboardInterrupt:
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# 手动中断（Ctrl+C）时优雅退出
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break
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def visualize_data_stream(
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data_stream,
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update_interval=50,
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low_percentile=5,
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high_percentile=98,
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gamma=1.0,
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ema_alpha=0.1,
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):
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"""实时可视化数据流。
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Args:
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data_stream: 来自 read_shared_memory_stream 的生成器
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update_interval: 动画刷新间隔（毫秒）
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low_percentile: 低端分位数（用于抑制噪声地板）
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high_percentile: 高端分位数（用于抑制少量极值，提升主体对比度）
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gamma: 非线性增强系数，>1 时高压区域颜色更“深”更突出
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ema_alpha: 分位数平滑系数，越小越稳定、越大越灵敏
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"""
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fig, ax = plt.subplots()
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# 初始化一个 64x64 的空图，后续每帧覆盖更新
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init_data = np.zeros((64, 64))
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im = ax.imshow(init_data, cmap="jet", aspect='equal') #,interpolation='bicubic'
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plt.colorbar(im) # 添加颜色条
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# 使用“分位数 + 指数平滑”的动态范围，兼顾灵敏度与稳定性
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vmin, vmax = None, None
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last_crc = None
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last_change_ts = time.time()
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last_diag_ts = 0.0
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frame_counter = 0
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fps_window_start = time.time()
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prev_u64 = {}
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prev_u32 = {}
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def probe_header_fields(header: bytes):
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"""解析72字节头部的候选计数器字段并返回变化摘要。"""
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candidates_u64 = {}
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candidates_u32 = {}
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for off in range(0, min(len(header), 72) - 7, 8):
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candidates_u64[off] = int.from_bytes(header[off:off + 8], byteorder="little", signed=False)
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for off in range(0, min(len(header), 72) - 3, 4):
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candidates_u32[off] = int.from_bytes(header[off:off + 4], byteorder="little", signed=False)
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changed_u64 = []
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changed_u32 = []
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for off, val in candidates_u64.items():
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prev = prev_u64.get(off)
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if prev is not None and val != prev:
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delta = val - prev
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changed_u64.append((off, val, delta))
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prev_u64[off] = val
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for off, val in candidates_u32.items():
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prev = prev_u32.get(off)
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if prev is not None and val != prev:
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delta = val - prev
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changed_u32.append((off, val, delta))
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prev_u32[off] = val
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return changed_u64, changed_u32
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def update(frame):
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nonlocal vmin, vmax, last_crc, last_change_ts, last_diag_ts, frame_counter, fps_window_start
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try:
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counter, event_tick, data, header = next(data_stream) # 从生成器获取最新数据
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# 示例展示策略：取前 64 行并转置，得到 64x64 画面
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# 说明：这只是可视化截取方式，不代表业务计算必须这样切片
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data = data[:64, :].T.astype(np.float64) # 取前 64 行，再转置 → 64×64
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# 1) 使用分位数而不是绝对 min/max，避免少量尖峰值“拉扁”整体颜色层次
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p_low = np.percentile(data, low_percentile)
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p_high = np.percentile(data, high_percentile)
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if p_high <= p_low:
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p_low, p_high = float(np.min(data)), float(np.max(data))
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if p_high <= p_low:
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p_high = p_low + 1e-9
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# 2) 用 EMA 平滑动态范围，减少每帧抖动导致的闪烁
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if vmin is None or vmax is None:
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vmin, vmax = p_low, p_high
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else:
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vmin = (1 - ema_alpha) * vmin + ema_alpha * p_low
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vmax = (1 - ema_alpha) * vmax + ema_alpha * p_high
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if vmax <= vmin:
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vmax = vmin + 1e-9
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# 3) 归一化后做 gamma 增强：gamma>1 可让高压区域更快进入深色高亮区
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norm = np.clip((data - vmin) / (vmax - vmin), 0.0, 1.0)
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enhanced = np.power(norm, gamma)
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# 颜色范围固定在 [0,1]，增强后高值会更“深”更突出
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im.set_data(enhanced)
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im.set_clim(vmin=0.0, vmax=1.0)
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# 每秒打印一次“帧新鲜度诊断”，用于判断写端是否在持续更新
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crc = int(zlib.crc32(np.ascontiguousarray(data).tobytes()))
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now = time.time()
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if last_crc is None or crc != last_crc:
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last_crc = crc
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last_change_ts = now
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frame_counter += 1
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elapsed = now - fps_window_start
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fps = (frame_counter / elapsed) if elapsed > 0 else 0.0
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if now - last_diag_ts >= 1.0:
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frame_age_ms = int(max(0.0, now - last_change_ts) * 1000.0)
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changed_u64, changed_u32 = probe_header_fields(header)
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u64_msg = "none"
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u32_msg = "none"
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if changed_u64:
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# 取前3个变化字段，格式 off:value(delta)
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u64_msg = ", ".join([f"{off}:{val}({delta:+d})" for off, val, delta in changed_u64[:3]])
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if changed_u32:
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u32_msg = ", ".join([f"{off}:{val}({delta:+d})" for off, val, delta in changed_u32[:3]])
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logging.info(
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"VIS诊断 frame_age_ms=%d crc=%s fps=%.1f tick_ms=%d | hdr_u64_changed=%s | hdr_u32_changed=%s",
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frame_age_ms,
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crc,
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fps,
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event_tick,
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u64_msg,
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u32_msg
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)
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last_diag_ts = now
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frame_counter = 0
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fps_window_start = now
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return [im]
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except StopIteration:
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plt.close()
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ani = FuncAnimation(fig, update, interval=update_interval, blit=True, cache_frame_data=False)
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plt.show()
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if __name__ == "__main__":
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# 读取内存数据
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data_stream = read_shared_memory_stream()
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# 启动可视化（20ms 间隔约等于 50Hz 刷新）
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visualize_data_stream(data_stream, update_interval=20) # 20Hz更新
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