bluepulsar
697 lines
27 KiB
Python
697 lines
27 KiB
Python
"""
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Sensor - Core sensor class for optical tactile sensing.
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This module provides the Sensor class for capturing, processing, and analyzing
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optical tactile sensor data with support for stitching, optical flow tracking,
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and Helmholtz-Hodge decomposition.
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"""
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import os
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import numpy as np
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import cv2
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import time
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from enum import Enum, auto
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from typing import Tuple
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from threading import Thread, Event
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from queue import Queue, Empty
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from .Stitcher import Stitcher
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from .tracker import Tracker
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from .HHD import nHHD
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from . import configs
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from .get_depth import Contact3D
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class Sensor:
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"""
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Optical tactile sensor interface with deformation analysis.
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Provides image capture, stitching, optical flow tracking, and force decomposition.
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Supports both synchronous and asynchronous (pipelined) processing modes.
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"""
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# =========================================================================
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# Info Enum (for read_info API)
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# =========================================================================
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class info(Enum):
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"""Data types available through read_info()."""
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VRAW = auto()
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VNORMAL = auto()
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VSHEAR = auto()
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FPS = auto()
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TIMESTAMP = auto()
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FNORMAL = auto()
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FSHEARX = auto()
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FSHEARY = auto()
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CENTROID = auto()
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CONTOUR = auto()
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DEPTH = auto()
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IMG = auto()
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# =========================================================================
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# Initialization
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# =========================================================================
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def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True,
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config_name="prototype", cal_path=None, override=False, box=None, mirror=True):
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"""
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Initialize Sensor with video source and configuration.
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Args:
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vid_src: Video source (int for camera ID, str for file path)
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cuda: Enable CUDA acceleration (default: True)
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isstitch: Enable stitching mode for multi-tile sensor (default: False)
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verbose: Print initialization logs (default: True)
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config_name: Configuration name (default: "prototype")
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cal_path: Path to calibration file (default: None)
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override: Skip auto-detection, use provided box (default: False)
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box: Manual bounding boxes when override=True (default: None)
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"""
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# Check CUDA availability
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try:
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import cupy as cp
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self.CUPY_AVAILABLE = True
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except ImportError:
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self.CUPY_AVAILABLE = False
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if vid_src is None:
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raise Exception("User must specify a video source.")
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# Store settings
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self.vid_src = vid_src
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self.isstitch = isstitch
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self.cuda = cuda
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self.verbose = verbose
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self.config_name = config_name
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self.cal_path = cal_path
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self.mirror = mirror
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# State variables
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self.stitch_valid = False
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self.stitch_calibration = []
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self.compute_deformation_valid = False
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self.compute_contact_valid = False
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# Data Variables (public)
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self.img = None
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self.img_size_raw = None
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self.img_filtered = None
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self.flow = None
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self.vnormal = None
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self.vshear = None
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self.n_boxes = None
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self.fnormal = self.fshearx = self.fsheary = self.ftor = 0.0
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# Contact Data Variables (public)
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self.centroid = None
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self.centroid_found = False
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self.contour = None
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self.depth_map = None
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# Timing
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self.last_log_time = time.perf_counter()
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self._fps = 0
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# Async pipeline state
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self._async_enabled = False
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self._capture_thread = None
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self._flow_thread = None
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self._compute_thread = None
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self._capture_queue = None
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self._flow_queue = None
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self._result_queue = None
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self._stop_event = None
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# Load configuration
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self.__config_cam = None
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self.__config_stitcher = None
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self.__config_contact = None
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self._load_config()
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# Initialize components
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self.stitcher = Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose)
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self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, dis_preset=self._config_dis_preset)
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self.__contact3d = Contact3D(config_contact=self.__config_contact, verbose=self.verbose)
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self.__hhd = None
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# Setup video capture
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self._open_video_source(override, box)
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if self.verbose:
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print(f" Video source: {vid_src}")
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print(f" Stitch mode: {self.stitch_valid}")
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print(f" CUDA acceleration: {self.cuda}")
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print(f" CuPy available: {self.CUPY_AVAILABLE}")
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print(f" OpenCV CUDA devices: {cv2.cuda.getCudaEnabledDeviceCount()}")
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# =========================================================================
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# Public API - Core Methods (Most Frequently Used)
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# =========================================================================
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def get_img(self, log_timing=False):
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"""
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Capture and preprocess a frame.
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Returns:
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np.ndarray: Processed grayscale image
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"""
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ret, frame = self.cap.read()
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if not ret:
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print("Failed to get_img from video source.")
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return None
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if log_timing:
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timings = {}
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t_start = time.perf_counter()
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# Convert to grayscale
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if log_timing: t0 = time.perf_counter()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# gray = frame
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if log_timing: timings['cvt_color'] = (time.perf_counter() - t0) * 1000
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# Extract and stitch tiles
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if log_timing: t0 = time.perf_counter()
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self.stitcher.get_stitch_unit(gray)
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self._extract_tiles(gray)
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if log_timing: timings['get_tiles'] = (time.perf_counter() - t0) * 1000
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if log_timing: t0 = time.perf_counter()
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img = np.ascontiguousarray(self.stitcher.stitch_rt_optimized())
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if log_timing: timings['stitch'] = (time.perf_counter() - t0) * 1000
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self.img_size_raw = img.shape
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# Resize
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img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
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# Temporal filtering (noise reduction)
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alpha = 0.7
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if self.img_filtered is None:
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self.img_filtered = img.astype(np.float32)
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else:
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self.img_filtered = alpha * img + (1 - alpha) * self.img_filtered
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self.img = self.img_filtered.astype(np.uint8)
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if log_timing:
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timings['total'] = (time.perf_counter() - t_start) * 1000
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self._print_timings("get_img", timings)
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if self.mirror:
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self.img = cv2.flip(self.img, 1)
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return self.img.copy()
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def compute_deformation(self):
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"""
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Compute optical flow and decompose into force components.
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Returns:
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np.ndarray: Optical flow field
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"""
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if self.img is None:
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raise Exception("Warning: Get image first")
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self.__compute_flow()
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self.__compute_hhd()
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# FPS calculation
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self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
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self.last_log_time = time.perf_counter()
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return True
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def __compute_flow(self):
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if self.img is None:
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raise Exception("Warning: Get image first")
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flow = self.__tracker.track(self.img)
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if hasattr(flow, 'download'):
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flow = flow.download()
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self.flow = flow
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def __compute_hhd(self):
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flow = self.flow
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if flow is None:
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raise Exception("Warning: Compute flow first")
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# Initialize HHD solver
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if self.__hhd is None:
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self.__hhd = nHHD(grid=flow.shape[:2], spacings=(18, 18))
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# Force computation
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if self.cuda and self.CUPY_AVAILABLE:
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_, forces = self.__hhd.get_force_gpu_v2(flow)
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else:
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_, forces = self.__hhd.get_force(flow)
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self.fnormal, self.fshearx, self.fsheary, self.ftor = forces
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# Get components
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self.vnormal = self.__hhd.get_normal()
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self.vshear = self.__hhd.get_shear()
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# Update state
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self.compute_deformation_valid = True
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return True
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def compute_contact(self, vnormal=None):
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"""
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Compute contact information from vnormal field.
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Args:
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vnormal: Optional vnormal field. If None, uses self.vnormal.
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Returns:
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dict: Contact result with centroid, contour, and depth_map
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"""
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multiple_foe = False
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# Use provided vnormal or fall back to internal state
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vnormal_to_use = vnormal if vnormal is not None else self.vnormal
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if vnormal_to_use is None:
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raise Exception("Warning: vnormal is required for contact computation")
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if multiple_foe:
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result = self.__contact3d.compute_multiple_foe(vnormal_to_use)
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else:
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if self.cuda:
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result = self.__contact3d.compute_foe_gpu(vnormal_to_use)
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else:
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result = self.__contact3d.compute_foe(vnormal_to_use)
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self.centroid = result['centroid']
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self.centroid_found = result['centroid_found']
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if 'contour' in result:
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self.contour = result['contour']
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self.depth_map = result['divergence_map']
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self.compute_contact_valid = True
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return result
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def read_info(self, *args) -> Tuple:
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"""
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Get sensor data by info type.
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Args:
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*args: info enum values (e.g., sensor.info.FPS, sensor.info.FNORMAL)
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Returns:
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Tuple: Requested data in order
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"""
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if any(arg in [self.info.IMG] for arg in args):
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if self.img is None:
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raise Exception("Warning: Get image first")
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if any(arg in [self.info.VRAW, self.info.VNORMAL, self.info.VSHEAR, self.info.FNORMAL, self.info.FSHEARX, self.info.FSHEARY] for arg in args):
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if not self.compute_deformation_valid:
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raise Exception("Warning: Compute deformation first")
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if any(arg in [self.info.CENTROID, self.info.CONTOUR, self.info.DEPTH] for arg in args):
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if not self.compute_contact_valid:
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raise Exception("Warning: Compute contact first")
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mapping = {
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# Return copies of arrays to prevent external modification of internal state
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self.info.VRAW: lambda: self.flow.copy() if self.flow is not None else np.array([]),
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self.info.VNORMAL: lambda: self.vnormal.copy() if self.vnormal is not None else np.array([]),
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self.info.VSHEAR: lambda: self.vshear.copy() if self.vshear is not None else np.array([]),
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# Scalar values are safe to return directly
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self.info.FNORMAL: lambda: self.fnormal,
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self.info.FSHEARX: lambda: self.fshearx,
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self.info.FSHEARY: lambda: self.fsheary,
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self.info.FPS: lambda: self._fps,
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self.info.TIMESTAMP: lambda: time.strftime('%H:%M:%S'),
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# Return copies of arrays to prevent external modification
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self.info.DEPTH: lambda: self.depth_map.copy() if self.depth_map is not None else np.array([]),
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# Centroid: can be tuple, list, or array - return a copy to prevent modification
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# Return None if centroid is not found or empty
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self.info.CENTROID: lambda: (
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np.array(self.centroid).copy()
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if self.centroid is not None and len(self.centroid) > 0
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else None
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),
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self.info.CONTOUR: lambda: self.contour.copy() if self.contour is not None else np.array([]),
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self.info.IMG: lambda: self.img.copy() if self.img is not None else np.array([]),
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}
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return tuple(mapping.get(arg, lambda: None)() for arg in args if isinstance(arg, self.info))
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def reset(self):
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"""Reset optical flow tracker."""
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self.__tracker.reset()
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def disconnect(self):
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"""Release resources and stop async threads."""
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if self._async_enabled:
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self.disable_async()
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if self.cap.isOpened():
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self.cap.release()
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if self.verbose:
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print("Sensor disconnected.")
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cv2.destroyAllWindows()
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# =========================================================================
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# Public API - Configuration & Calibration
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# =========================================================================
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# def export_config(self, path="./config/exported_config.json"):
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# """Return current configuration dict."""
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# configs.export_config(self._config, path)
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# if self.verbose:
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# print(f"Config exported to {path}")
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# return True
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def stitch_cal(self):
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"""
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Load or create stitch calibration.
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Returns:
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np.ndarray: Stitched calibration image
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"""
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if self.cal_path is None:
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self.cal_path = "./config/cal.npy"
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if os.path.exists(self.cal_path):
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if self.verbose:
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print("Loading calibration from", self.cal_path)
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self.stitch_calibration = np.load(self.cal_path, allow_pickle=True)
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self.stitcher.stitch_calibration = self.stitch_calibration
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return self.stitcher.stitch_rt_optimized()
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else:
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if self.verbose:
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print("Creating new calibration...")
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img, self.stitch_calibration = self.stitcher.stitch_cal()
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os.makedirs("./config", exist_ok=True)
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np.save(self.cal_path, self.stitch_calibration)
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if self.verbose:
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print("Calibration saved to", self.cal_path)
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return img
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# =========================================================================
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# Private - Initialization Helpers
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# =========================================================================
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def _load_config(self):
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"""Load sensor configuration from JSON file."""
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self._config = configs.load_config(self.config_name)
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cam = self._config["camera"]
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self._config_resolution_raw = cam["resolution_raw"]
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self._config_fps = cam["fps"]
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self._config_units = cam["units"]
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self._config_resolution_stitch = cam["resolution_stitch"]
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self._config_dis_preset = self._config["tracker"]["dis_preset"]
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self.__config_stitcher = self._config["stitcher"]
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self.__config_contact = self._config["contact"]
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if self.verbose:
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print(f"Config: {self.config_name}")
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return True
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def _open_video_source(self, override, box):
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"""Initialize video capture and calibration."""
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# Use DirectShow backend on Windows for cameras (more stable than MSMF)
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if isinstance(self.vid_src, int):
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# For camera indices, try DirectShow first (avoids MSMF errors)
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try:
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self.cap = cv2.VideoCapture(self.vid_src, cv2.CAP_DSHOW)
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except:
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# Fallback to default backend
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self.cap = cv2.VideoCapture(self.vid_src)
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else:
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# For file paths, use default backend
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self.cap = cv2.VideoCapture(self.vid_src)
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# Set buffer size to 1 to reduce latency and avoid frame accumulation
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self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
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self.cap.set(cv2.CAP_PROP_FPS, self._config_fps)
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self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self._config_resolution_raw[0])
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self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self._config_resolution_raw[1])
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if self.verbose:
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print("Connecting to video source...")
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if not self.cap.isOpened():
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raise Exception("Failed to open video source.")
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# Wait for first valid frame
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frame = None
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while frame is None:
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ret, frame = self.cap.read()
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if not ret:
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time.sleep(0.1)
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if override:
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if box is not None:
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self.stitcher.isstitch = self.isstitch = False
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self.stitcher.boxes = box
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else:
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print("Override=True but no box provided.")
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else:
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self._find_stitch_boxes(frame)
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self._extract_tiles(frame)
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self.stitch_cal()
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def _find_stitch_boxes(self, img_src, show=False):
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"""Detect vision tile boxes with camera stabilization."""
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n_boxes, v_mean0, timeout = 0, 0, 5
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start_time = cv2.getTickCount()
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while n_boxes < self._config_units:
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ret, frame = self.cap.read()
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v_mean = np.sum(cv2.mean(frame))
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if abs(v_mean - v_mean0) < 5:
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n_boxes, boxes = self.stitcher.find_stitch_boxes(frame, show=show)
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if (cv2.getTickCount() - start_time) / cv2.getTickFrequency() > timeout:
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print("Timeout finding vision units.")
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self.stitcher.isstitch = self.isstitch = False
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break
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v_mean0 = v_mean
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self.n_boxes = n_boxes
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self.boxes = boxes
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self.stitch_valid = self.isstitch and n_boxes == self._config_units
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status = "valid for stitching" if self.stitch_valid else "non-stitch mode"
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print(f"Sensor: {status}")
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return n_boxes
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def _extract_tiles(self, img_src):
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"""Extract tiles from source image."""
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self.stitcher.get_stitch_unit(img_src)
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self.stitcher.extract_tiles()
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return True
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# =========================================================================
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# Private - Utilities
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# =========================================================================
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def _print_timings(self, name, timings):
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"""Print timing breakdown."""
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print(f"\n[{name} timing]")
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for k, v in timings.items():
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if k != 'total':
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print(f" {k}: {v:7.2f} ms")
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print(f" ─────────────────────────")
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print(f" TOTAL: {timings['total']:7.2f} ms ({1000/timings['total']:.1f} FPS)")
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# =========================================================================
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# Public API - Async Pipeline
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# =========================================================================
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# def enable_async(self, queue_size=2, compute_contact=False):
|
|
# """
|
|
# Enable 3-stage async pipeline: Capture → Flow → HHD.
|
|
|
|
# Each stage runs in parallel for maximum throughput.
|
|
# """
|
|
# if self._async_enabled:
|
|
# return
|
|
# self._compute_contact_enabled = compute_contact
|
|
|
|
# self._capture_queue = Queue(maxsize=queue_size)
|
|
# self._flow_queue = Queue(maxsize=queue_size)
|
|
# self._result_queue = Queue(maxsize=queue_size)
|
|
# if compute_contact:
|
|
# self._contact_queue = Queue(maxsize=queue_size)
|
|
# self._stop_event = Event()
|
|
# self._async_enabled = True
|
|
|
|
# self._capture_thread = Thread(target=self._capture_worker, daemon=True)
|
|
# self._flow_thread = Thread(target=self._flow_worker, daemon=True)
|
|
# self._compute_thread = Thread(target=self._hhd_worker, daemon=True)
|
|
# if compute_contact:
|
|
# self._contact_thread = Thread(target=self._contact_worker, daemon=True)
|
|
|
|
# self._capture_thread.start()
|
|
# self._flow_thread.start()
|
|
# self._compute_thread.start()
|
|
# if compute_contact:
|
|
# self._contact_thread.start()
|
|
|
|
# if self.verbose:
|
|
# if compute_contact:
|
|
# print("Async pipeline enabled: Capture → Flow → HHD → Contact")
|
|
# else:
|
|
# print("Async pipeline enabled: Capture → Flow → HHD")
|
|
|
|
# def get_async(self, timeout=0.05):
|
|
# """
|
|
# Get latest result from async pipeline (non-blocking).
|
|
|
|
# Returns:
|
|
# dict or None: Sensor data if available
|
|
# """
|
|
# compute_contact = self._compute_contact_enabled
|
|
# if not self._async_enabled:
|
|
# return None
|
|
# try:
|
|
# result = self._result_queue.get(timeout=timeout)
|
|
# t = time.perf_counter()
|
|
# result['fps'] = 1.0 / (t - self.last_log_time) if self.last_log_time else 0
|
|
# self.last_log_time = t
|
|
|
|
# # Sync instance variables
|
|
# # for k in ['img', 'flow', 'normal', 'shear', 'depth', 'fnormal', 'fshearx', 'fsheary', 'ftor', 'fps']:
|
|
# if compute_contact:
|
|
# for k in ['img', 'flow', 'normal', 'shear', 'depth', 'centroid', 'centroid_found', 'contour', 'fps', 'fnormal', 'fshearx', 'fsheary', 'ftor']:
|
|
# setattr(self, k, result.get(k))
|
|
# else:
|
|
# for k in ['img', 'flow', 'normal', 'shear', 'fnormal', 'fshearx', 'fsheary', 'ftor', 'fps']:
|
|
# setattr(self, k, result.get(k))
|
|
# self.vupdate = True
|
|
# return result
|
|
# except Empty:
|
|
# return None
|
|
|
|
# def disable_async(self):
|
|
# """Stop async pipeline."""
|
|
# if not self._async_enabled:
|
|
# return
|
|
# self._stop_event.set()
|
|
# for t in [self._capture_thread, self._flow_thread, self._compute_thread]:
|
|
# if t: t.join(timeout=2.0)
|
|
# self._async_enabled = False
|
|
# self._capture_thread = self._flow_thread = self._compute_thread = None
|
|
# self._capture_queue = self._flow_queue = self._result_queue = self._stop_event = None
|
|
# if self.verbose:
|
|
# print("Async pipeline disabled")
|
|
|
|
# =========================================================================
|
|
# Private - Async Workers
|
|
# =========================================================================
|
|
|
|
# def _queue_put(self, q, item):
|
|
# """Put item in queue, dropping old if full."""
|
|
# if q.full():
|
|
# try: q.get_nowait()
|
|
# except Empty: pass
|
|
# q.put(item)
|
|
|
|
# def _capture_worker(self):
|
|
# """Stage 1: Capture and preprocess frames by reusing get_img()."""
|
|
# while not self._stop_event.is_set():
|
|
# try:
|
|
# # Reuse get_img() to avoid code duplication
|
|
# if self.get_img() is None:
|
|
# continue
|
|
|
|
# # Put the processed image in the queue
|
|
# # get_img() updates self.img, so we use that
|
|
# if self.img is not None:
|
|
# self._queue_put(self._capture_queue, self.img.copy())
|
|
# except Exception as e:
|
|
# if self.verbose: print(f"Capture error: {e}")
|
|
|
|
# def _flow_worker(self):
|
|
# """Stage 2: Compute optical flow by reusing __compute_flow()."""
|
|
# while not self._stop_event.is_set():
|
|
# try:
|
|
# img = self._capture_queue.get(timeout=0.1)
|
|
# # Set __img to reuse __compute_flow()
|
|
# self.img = img
|
|
# self.__compute_flow()
|
|
# flow = self.flow.copy() if self.flow is not None else None
|
|
# self._queue_put(self._flow_queue, (img, flow))
|
|
# except Empty:
|
|
# continue
|
|
# except Exception as e:
|
|
# if self.verbose: print(f"Flow error: {e}")
|
|
|
|
# def _hhd_worker(self):
|
|
# """Stage 3: HHD decomposition by reusing __compute_hhd()."""
|
|
# while not self._stop_event.is_set():
|
|
# try:
|
|
# img, flow = self._flow_queue.get(timeout=0.1)
|
|
|
|
# # Set __flow to reuse __compute_hhd()
|
|
# self.flow = flow
|
|
# self.__compute_hhd()
|
|
# # Get results from internal variables set by __compute_hhd()
|
|
# result = {
|
|
# 'img': img, 'flow': flow,
|
|
# 'normal': self.vnormal.copy() if self.vnormal is not None else None,
|
|
# 'shear': self.vshear.copy() if self.vshear is not None else None,
|
|
# # 'depth': getFilteredDensity(flow, use_cuda=self.cuda),
|
|
# 'fnormal': self.fnormal, 'fshearx': self.fshearx,
|
|
# 'fsheary': self.fsheary, 'ftor': self.ftor,
|
|
# }
|
|
|
|
# # Always include contact fields to ensure consistent result structure
|
|
# # When compute_contact is True, contact worker will update these values
|
|
# # When compute_contact is False, they remain None
|
|
# result.update({
|
|
# 'centroid': None,
|
|
# 'centroid_found': False,
|
|
# 'contour': None,
|
|
# 'depth_map': None,
|
|
# 'depth': None, # Alias for depth_map for compatibility
|
|
# })
|
|
|
|
# self._queue_put(self._result_queue, result)
|
|
# except Empty:
|
|
# continue
|
|
# except Exception as e:
|
|
# if self.verbose: print(f"HHD error: {e}")
|
|
|
|
# def _contact_worker(self):
|
|
# """Stage 4: Contact computation using vnormal from HHD results."""
|
|
# while not self._stop_event.is_set():
|
|
# try:
|
|
# # Get results from HHD worker which includes vnormal
|
|
# hhd_result = self._result_queue.get(timeout=0.1)
|
|
# vnormal = hhd_result.get('normal') # This is vnormal from HHD
|
|
|
|
# if vnormal is not None:
|
|
# # Compute contact using vnormal directly
|
|
# contact_result = self.compute_contact(vnormal=vnormal)
|
|
# # Add contact results to the existing HHD result
|
|
# depth_map = contact_result.get('divergence_map')
|
|
# hhd_result.update({
|
|
# 'centroid': contact_result.get('centroid'),
|
|
# 'centroid_found': contact_result.get('centroid_found'),
|
|
# 'contour': contact_result.get('contour'),
|
|
# 'depth_map': depth_map,
|
|
# 'depth': depth_map, # Alias for depth_map for compatibility
|
|
# })
|
|
|
|
# # Put updated result back to queue
|
|
# self._queue_put(self._result_queue, hhd_result)
|
|
# except Empty:
|
|
# continue
|
|
# except Exception as e:
|
|
# if self.verbose: print(f"Contact error: {e}")
|
|
|