""" Sensor - Core sensor class for optical tactile sensing. This module provides the Sensor class for capturing, processing, and analyzing optical tactile sensor data with support for stitching, optical flow tracking, and Helmholtz-Hodge decomposition. """ # import os # only used by commented-out stitch_cal import numpy as np import cv2 import time from enum import Enum, auto from pathlib import Path from typing import Optional, Tuple # from threading import Thread, Event # from queue import Queue, Empty from .Stitcher import Stitcher from .tracker import Tracker from .hhd import nHHD from . import calibration as cal_mod from . import configs from .get_depth import Contact3D from .backend_availability import (check_cupy_available, check_opencl_available) from .frame_processors import create_frame_processor from .signal_processing import ( DEFAULT_SAMPLE_RATE_HZ, LowpassFilter, ) from .capture import ( CaptureConfig, CaptureSource, ReconnectPolicy, is_live_camera, normalize_source, open_capture, ) class Sensor: """ Optical tactile sensor interface with deformation analysis. Provides image capture, stitching, optical flow tracking, and force decomposition. Supports both synchronous and asynchronous (pipelined) processing modes. """ # ========================================================================= # Info Enum (for read_info API) # ========================================================================= class info(Enum): """Data types available through read_info().""" VRAW = auto() VNORMAL = auto() VSHEAR = auto() FPS = auto() TIMESTAMP = auto() FNORMAL = auto() FSHEARX = auto() FSHEARY = auto() FNORMAL_RAW = auto() FSHEARX_RAW = auto() FSHEARY_RAW = auto() CENTROID = auto() CONTOUR = auto() DEPTH = auto() IMG = auto() # ========================================================================= # Initialization # ========================================================================= def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True, debug=False, config_name="prototype", cal_path=None, override=False, box=None, mirror=True, backend='auto', capture_config: Optional[CaptureConfig] = None, auto_reconnect: bool = False, capture_mode: str = "latest", capture_queue_size: int = 1, capture_overflow: str = "drop_oldest", **_deprecated_capture_kwargs): """ Initialize Sensor with video source and configuration. Args: vid_src: Video source (int for camera ID, str for file path) cuda: Enable CUDA acceleration (default: True) isstitch: Enable stitching mode for multi-tile sensor (default: False) verbose: Print initialization logs (default: True) config_name: Configuration name (default: "prototype") cal_path: Explicit force_calibration.json path (overrides bundle default) override: Skip auto-detection, use provided box (default: False) box: Manual bounding boxes when override=True (default: None) backend: Poisson backend (default: "auto") "auto": Automatically select the best backend based on availability "cupy": Use CuPy for GPU acceleration "opencl": Use OpenCL for GPU acceleration "cpu": Use CPU for computation capture_config: Optional ``CaptureConfig`` overriding capture kwargs below. auto_reconnect: Enable USB disconnect auto-reconnect for live cameras. capture_mode: Deprecated; ignored (latest-frame capture only). capture_queue_size: Deprecated; ignored. capture_overflow: Deprecated; ignored. """ self.CUPY_AVAILABLE = check_cupy_available() self.OPENCL_AVAILABLE = check_opencl_available() if vid_src is None: raise Exception("User must specify a video source.") self._external_only = isinstance(vid_src, str) and vid_src.lower() == "external" # Store settings self.vid_src = normalize_source(vid_src) if not self._external_only else vid_src self.isstitch = isstitch self.cuda = cuda self.verbose = verbose self.debug = debug self.config_name = config_name self.cal_path = cal_path self.mirror = mirror self._capture_config_override = capture_config self._auto_reconnect = bool(auto_reconnect) # Load configuration self.__config_cam = None self.__config_stitcher = None self.__config_contact = None self._load_config() # State variables self.stitch_valid = False self.compute_deformation_valid = False self.compute_contact_valid = False # Image Data Variables (public) # self.stitch_calibration = [] self.frame = self.img = None self.initial_img = None self._img_bgr_raw = None self._img_gray_buf = None shape = (self.__config_cam["resolution_stitch"][1], self.__config_cam["resolution_stitch"][0]) self.img_size_raw = None self.img_filtered = None self.tail_frames_count = 0 # Optical Flow Data Variables (public) self.flow = np.zeros(shape + (2,), dtype=np.float32) self.vtemplate = np.zeros_like(self.flow) self.vnormal = self.vtemplate self.vshear = self.vtemplate self.n_boxes = None self.fnormal = self.fshearx = self.fsheary = self.ftor = 0.0 self._force_raw = (0.0, 0.0, 0.0) self.calibration: Optional[cal_mod.Calibration] = None self._force_cal_sensor_name: Optional[str] = None self._init_force_calibration() self._lowpass_enabled = False self._lowpass_cutoff_hz = 0.0 self._lowpass_sample_rate_hz = float(DEFAULT_SAMPLE_RATE_HZ) self._lowpass_order = 2 self._force_filter = LowpassFilter( cutoff_hz=self._lowpass_cutoff_hz, sample_rate_hz=self._lowpass_sample_rate_hz, order=self._lowpass_order, n_channels=3, ) if self.verbose: if self.calibration is not None and self.calibration.source_path is not None: print(f"Force calibration: {self.calibration.source_path}") elif self._force_cal_sensor_name: print( f"Force calibration: sensors/{self._force_cal_sensor_name}/force_calibration.json" ) else: print("Force calibration: raw (no force_calibration.json)") # Contact Data Variables (public) self.centroid = (0, 0) self.centroid_found = False self.contour = np.empty(0) self.depth_map = np.zeros_like(self.img) self.valid_mask_raw = None self.valid_mask = None self.valid_mask_hhd = None self.mask_stats = { "coverage": 0.0, "flow_mean_valid": 0.0, "flow_mean_invalid": 0.0, } # Timing self.last_log_time = time.perf_counter() self._fps = 0 self._last_capture_time = 0.0 self._last_processing_time = 0.0 self._last_stage_times = { "extract_tiles": 0.0, "stitch": 0.0, "resize": 0.0, "mirror": 0.0, "astype": 0.0, } # Initialize components self.stitcher = ( Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose) if self._is_compound_eye() else None ) self.frame_processor = create_frame_processor(self.sensor_type, stitcher=self.stitcher) self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, config_tracker=self.__config_tracker) self.__contact3d = Contact3D(config_contact=self.__config_contact, verbose=self.verbose) self.__hhd = None # Integer >= 1: run HHD on flow downsampled by this factor for speed (1 = full res). _raw_ds = self.__config_tracker.get("hhd_downsample", 1) try: self._hhd_downsample = max(1, int(_raw_ds)) if _raw_ds is not None else 1 except (TypeError, ValueError): self._hhd_downsample = 1 self._hhd_grid_shape = None cfg_backend = self.__config_tracker.get("backend", None) self.poisson_backend = cfg_backend if cfg_backend is not None else backend self.masked_poisson_solver = self.__config_tracker.get("masked_poisson_solver", "sparse") self.masked_poisson_max_boundary = self.__config_tracker.get("masked_poisson_max_boundary", 96) self.masked_poisson_boundary_stride = self.__config_tracker.get("masked_poisson_boundary_stride", 1) self.masked_poisson_max_iters = self.__config_tracker.get("masked_poisson_max_iters", 3) self.masked_poisson_tol = self.__config_tracker.get("masked_poisson_tol", 1e-4) self.masked_poisson_reg = self.__config_tracker.get("masked_poisson_reg", 1e-8) # Legacy PyAV capture state (unused after orisys.capture refactor). # self._use_pyav = False # self._av_container = None # self._av_stream = None # self._av_decoder = None self.cap = None # compat alias for _capture_worker.source.cap self._capture_source: Optional[CaptureSource] = None self._is_live_camera = ( is_live_camera(self.vid_src) and not self._external_only ) # Setup video capture unless this sensor is fed externally. if self._external_only: self.stitch_valid = False if self.verbose: print("Sensor initialized in external-frame mode.") else: self._open_video_source(override, box) if self.verbose: print(f" Video source: {vid_src}") print(f" Stitch mode: {self.stitch_valid}") print(f" Optical flow algorithm: {self.__config_tracker['algorithm']}") print(f" Poisson backend: {self.poisson_backend}") # print(f" CUDA acceleration: {self.cuda}") print(f" CuPy available: {self.CUPY_AVAILABLE}") print(f" OpenCL available: {self.OPENCL_AVAILABLE}") # ========================================================================= # Public API - Core Methods (Most Frequently Used) # ========================================================================= def _resize_mask(self, mask, size): if mask is None: return None mask_u8 = np.asarray(mask, dtype=np.uint8) resized = cv2.resize(mask_u8, size, interpolation=cv2.INTER_NEAREST) return resized.astype(bool) def _prepare_valid_mask(self, valid_mask, raw_shape, stitched_shape): if valid_mask is None: self.valid_mask_raw = None self.valid_mask = None self.valid_mask_hhd = None self.mask_stats["coverage"] = 0.0 return mask_raw = np.asarray(valid_mask, dtype=bool) if mask_raw.shape != raw_shape: mask_raw = self._resize_mask(mask_raw, (raw_shape[1], raw_shape[0])) if self.mirror: mask_raw = np.fliplr(mask_raw) self.valid_mask_raw = mask_raw self.valid_mask = self._resize_mask(mask_raw, (stitched_shape[1], stitched_shape[0])) self._update_hhd_mask_cache() self.mask_stats["coverage"] = float(np.mean(self.valid_mask)) if self.valid_mask.size else 0.0 def _hhd_grid_size(self): if self.valid_mask is None: return None h, w = self.valid_mask.shape[:2] k = max(1, int(self._hhd_downsample)) if k <= 1: return (w, h) return (max(1, w // k), max(1, h // k)) def _update_hhd_mask_cache(self): """Precompute valid_mask at HHD resolution when the UV domain is fixed.""" if self.valid_mask is None: self.valid_mask_hhd = None return ds_size = self._hhd_grid_size() if ds_size is None: self.valid_mask_hhd = None return if self.valid_mask.shape[1] == ds_size[0] and self.valid_mask.shape[0] == ds_size[1]: self.valid_mask_hhd = self.valid_mask.astype(bool) else: self.valid_mask_hhd = self._resize_mask(self.valid_mask, ds_size) def _compute_flow_means(self, flow): if flow is None: return mag = np.linalg.norm(flow, axis=-1) mask = self.valid_mask if mask is None or mask.shape != mag.shape: self.mask_stats["flow_mean_valid"] = float(mag.mean()) if mag.size else 0.0 self.mask_stats["flow_mean_invalid"] = 0.0 return valid = mask.astype(bool) invalid = ~valid self.mask_stats["flow_mean_valid"] = float(mag[valid].mean()) if np.any(valid) else 0.0 self.mask_stats["flow_mean_invalid"] = float(mag[invalid].mean()) if np.any(invalid) else 0.0 def get_img(self, frame=None, already_processed=False, valid_mask=None, already_gray=False): """ Capture and preprocess a frame. Parameters ---------- frame : np.ndarray | None Optional externally supplied frame. When provided, the sensor skips reading from its own video source and uses this frame instead. already_processed : bool If True, treat `frame` as the final BGR tactile image and skip the configured frame processor. This is useful for pipelines that generate a derived image, such as an unrolled tactile view. Returns: np.ndarray | None: Processed image, or None on capture failure. """ # Simple direct read - buffer size of 1 should minimize frame accumulation # Timing measurement helps identify if capture or processing is the bottleneck t_capture_start = time.perf_counter() if frame is None: ret, frame = self._read_frame() else: ret = frame is not None t_capture_end = time.perf_counter() self._last_capture_time = t_capture_end - t_capture_start if not ret: print("Failed to get_img from video source.") return None # Start processing timing t_processing_start = time.perf_counter() self.frame = frame if (already_processed and isinstance(frame, np.ndarray)) else ( frame.copy() if isinstance(frame, np.ndarray) else frame ) if already_processed: img_bgr = frame gray = None if already_gray: if frame.ndim == 2: gray = frame elif frame.ndim == 3 and frame.shape[2] == 1: gray = frame[:, :, 0] else: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) img_bgr = None else: if img_bgr.ndim == 2: img_bgr = cv2.cvtColor(img_bgr, cv2.COLOR_GRAY2BGR) elif img_bgr.ndim == 3 and img_bgr.shape[2] == 1: img_bgr = cv2.cvtColor(img_bgr[:, :, 0], cv2.COLOR_GRAY2BGR) else: img_bgr = self.frame_processor.process(frame) gray = None t_after_extract = time.perf_counter() t_after_stitch = time.perf_counter() if gray is None: self.img_size_raw = img_bgr.shape self._img_bgr_raw = img_bgr gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) else: self.img_size_raw = gray.shape self._img_bgr_raw = None res = tuple(self._config_resolution_stitch) if self._img_gray_buf is None or self._img_gray_buf.shape != (res[1], res[0]): self._img_gray_buf = np.empty((res[1], res[0]), dtype=np.uint8) cv2.resize(gray, res, dst=self._img_gray_buf, interpolation=cv2.INTER_LINEAR) t_after_resize = time.perf_counter() if self.mirror: cv2.flip(self._img_gray_buf, 1, dst=self._img_gray_buf) self.img = self._img_gray_buf self._prepare_valid_mask(valid_mask, gray.shape, self.img.shape) t_processing_end = time.perf_counter() self._last_processing_time = t_processing_end - t_processing_start self._last_stage_times["extract_tiles"] = t_after_extract - t_processing_start self._last_stage_times["stitch"] = t_after_stitch - t_after_extract self._last_stage_times["resize"] = t_processing_end - t_after_stitch # FPS calculation self._fps = 1.0 / (time.perf_counter() - self.last_log_time) self.last_log_time = time.perf_counter() return self.img.copy() def _get_img_bgr(self): """Produce a resized+mirrored BGR image on demand (for display).""" if self._img_bgr_raw is None: if self.img is None: return None return cv2.cvtColor(self.img, cv2.COLOR_GRAY2BGR) res = tuple(self._config_resolution_stitch) bgr = cv2.resize(self._img_bgr_raw, res, interpolation=cv2.INTER_LINEAR) if self.mirror: cv2.flip(bgr, 1, dst=bgr) return bgr def detect_motion(self, threshold=6): # detect motion by optical flow magnitude mag = np.linalg.norm(self.flow, axis=-1) if self.valid_mask is not None and self.valid_mask.shape == mag.shape: active = self.valid_mask.astype(bool) motion_value = mag[active].mean() if np.any(active) else 0.0 else: motion_value = mag.mean() return motion_value > threshold def compute_deformation(self, decompose=True, check_motion=False, threshold=6, tail_frames=10): """ Compute optical flow and optionally decompose it into force components. Parameters ---------- decompose : bool If True, run HHD decomposition (`compute_hhd`) after flow computation. check_motion : bool If True, gate decomposition with `detect_motion(threshold)`. When motion falls below threshold, decomposition may still run for up to `tail_frames` additional frames to avoid abrupt force drop-off. threshold : float Motion-detection threshold on mean flow magnitude. tail_frames : int Number of extra non-motion frames allowed to keep running HHD when `check_motion=True`. Returns ------- bool | None - `True` : motion is currently detected (`check_motion=True`) - `False` : motion is currently not detected (`check_motion=True`) - `None` : motion gating disabled (`check_motion=False`) """ if self.img is None: raise Exception("Warning: Get image first") self.compute_flow() if not check_motion: self.tail_frames_count = 0 if decompose: self.compute_hhd() return None is_motion = self.detect_motion(threshold=threshold) try: tail_frames = max(0, int(tail_frames)) except (TypeError, ValueError): tail_frames = 0 if is_motion: self.tail_frames_count = 0 if decompose: self.compute_hhd() return True # No motion this frame. Keep HHD alive briefly if requested, but do not # report this as "motion detected". if decompose and self.tail_frames_count < tail_frames: self.tail_frames_count += 1 self.compute_hhd() else: self.tail_frames_count = tail_frames return False def warmup_deformation(self): """Build optical-flow and masked-HHD caches using the current image.""" if self.img is None: raise Exception("Warning: Get image first") self.compute_flow() self.compute_hhd() return True def compute_flow(self): if self.img is None: raise Exception("Warning: Get image first") flow = self.__tracker.track(self.img, valid_mask=self.valid_mask) # flow = self.__tracker.track_raft(self.img) if hasattr(flow, 'download'): flow = flow.download() self.flow = flow self._compute_flow_means(self.flow) def compute_hhd(self): flow = self.flow if flow is None: raise Exception("Warning: Compute flow first") orig_h, orig_w = flow.shape[:2] try: k = max(1, int(self._hhd_downsample)) except (TypeError, ValueError): k = 1 mask_ds = None if self.valid_mask is not None: ds_size = self._hhd_grid_size() if ( self.valid_mask_hhd is not None and ds_size is not None and self.valid_mask_hhd.shape[1] == ds_size[0] and self.valid_mask_hhd.shape[0] == ds_size[1] ): mask_ds = self.valid_mask_hhd else: mask_ds = self.valid_mask.astype(bool) if k > 1: ds_w = max(1, orig_w // k) ds_h = max(1, orig_h // k) flow_ds = cv2.resize(flow, (ds_w, ds_h), interpolation=cv2.INTER_AREA) if mask_ds is not None and ( mask_ds.shape[1] != ds_w or mask_ds.shape[0] != ds_h ): mask_ds = self._resize_mask(mask_ds, (ds_w, ds_h)) else: flow_ds = flow ds_shape = flow_ds.shape[:2] self.valid_mask_hhd = mask_ds if self.__hhd is None or self._hhd_grid_shape != ds_shape: spacings = (18 * k, 18 * k) self.__hhd = nHHD( cuda=self.cuda, grid=ds_shape, spacings=spacings, poisson_backend=self.poisson_backend, debug=self.debug, masked_poisson_solver=self.masked_poisson_solver, masked_poisson_max_boundary=self.masked_poisson_max_boundary, masked_poisson_boundary_stride=self.masked_poisson_boundary_stride, masked_poisson_max_iters=self.masked_poisson_max_iters, masked_poisson_tol=self.masked_poisson_tol, masked_poisson_reg=self.masked_poisson_reg, ) self._hhd_grid_shape = ds_shape _, forces = self.__hhd.get_force(flow_ds, valid_mask=mask_ds) fn, fx, fy, ftor = forces self._force_raw = (float(fn), float(fx), float(fy)) self.ftor = float(ftor) self._apply_force_filter() vnormal_ds = self.__hhd.get_normal() vshear_ds = self.__hhd.get_shear() if k > 1: self.vnormal = cv2.resize(vnormal_ds, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR) self.vshear = cv2.resize(vshear_ds, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR) else: self.vnormal = vnormal_ds self.vshear = vshear_ds if self.valid_mask is not None: active = self.valid_mask.astype(bool) self.vnormal = self.vnormal.copy() self.vshear = self.vshear.copy() self.vnormal[~active] = 0 self.vshear[~active] = 0 self.compute_deformation_valid = True return True def compute_contact(self, vnormal=None): """ Compute contact information from vnormal field. Args: vnormal: Optional vnormal field. If None, uses self.vnormal. Returns: dict: Contact result with centroid, contour, and depth_map """ multiple_foe = False # Use provided vnormal or fall back to internal state vnormal_to_use = vnormal if vnormal is not None else self.vnormal if vnormal_to_use is None: raise Exception("Warning: vnormal is required for contact computation") if multiple_foe: result = self.__contact3d.compute_multiple_foe(vnormal_to_use, valid_mask=self.valid_mask) else: if self.cuda: result = self.__contact3d.compute_foe_gpu(vnormal_to_use, valid_mask=self.valid_mask) else: result = self.__contact3d.compute_foe(vnormal_to_use, valid_mask=self.valid_mask) self.centroid = result['centroid'] self.centroid_found = result['centroid_found'] if 'contour' in result: self.contour = result['contour'] self.depth_map = result['divergence_map'] if self.valid_mask is not None and self.centroid_found: cx, cy = int(self.centroid[0]), int(self.centroid[1]) inside = ( 0 <= cy < self.valid_mask.shape[0] and 0 <= cx < self.valid_mask.shape[1] and bool(self.valid_mask[cy, cx]) ) if not inside and self.verbose: print("Warning: contact centroid lies outside the valid UV mask.") self.compute_contact_valid = True return result def read_info(self, *args) -> Tuple: """ Get sensor data by info type. Args: *args: info enum values (e.g., sensor.info.FPS, sensor.info.FNORMAL) Returns: Tuple: Requested data in order """ if any(arg in [self.info.IMG] for arg in args): if self.img is None: raise Exception("Warning: Get image first") # 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): # if not self.compute_deformation_valid: # print("Warning: Compute deformation first") # # raise Exception("Warning: Compute deformation first") # if any(arg in [self.info.CENTROID, self.info.CONTOUR, self.info.DEPTH] for arg in args): # if not self.compute_contact_valid: # print("Warning: Compute contact first") # raise Exception("Warning: Compute contact first") mapping = { # Return copies of arrays to prevent external modification of internal state self.info.VRAW: lambda: self.flow.copy() if self.flow is not None else np.array([]), self.info.VNORMAL: lambda: self.vnormal.copy() if self.vnormal is not None else np.array([]), self.info.VSHEAR: lambda: self.vshear.copy() if self.vshear is not None else np.array([]), # Scalar values are safe to return directly self.info.FNORMAL: lambda: self.fnormal, self.info.FSHEARX: lambda: self.fshearx, self.info.FSHEARY: lambda: self.fsheary, self.info.FNORMAL_RAW: lambda: self._force_raw[0], self.info.FSHEARX_RAW: lambda: self._force_raw[1], self.info.FSHEARY_RAW: lambda: self._force_raw[2], self.info.FPS: lambda: self._fps, self.info.TIMESTAMP: lambda: time.strftime('%H:%M:%S'), # Return copies of arrays to prevent external modification self.info.DEPTH: lambda: self.depth_map.copy() if self.depth_map is not None else np.array([]), # Centroid: can be tuple, list, or array - return a copy to prevent modification # Return None if centroid is not found or empty self.info.CENTROID: lambda: ( np.array(self.centroid).copy() if self.centroid is not None and len(self.centroid) > 0 else None ), self.info.CONTOUR: lambda: self.contour.copy() if self.contour is not None else np.array([]), self.info.IMG: lambda: self._get_img_bgr() if self._img_bgr_raw is not None else np.array([]), } return tuple(mapping.get(arg, lambda: None)() for arg in args if isinstance(arg, self.info)) def reset(self): """Reset optical flow tracker and force-filter state.""" self.__tracker.reset() self._force_filter.reset() if self.img is not None: self.initial_img = self.img.copy() def disconnect(self): """Release resources and stop async threads.""" if self._capture_source is not None: self._capture_source.close() self._capture_source = None self.cap = None if self.verbose: print("Sensor disconnected.") # Legacy PyAV / direct-cap fallbacks (unused after orisys.capture refactor). # elif self._use_pyav: # if self._av_container is not None: # self._av_container.close() # self._av_container = None # self._av_stream = None # self._av_decoder = None # if self.verbose: # print("Sensor disconnected.") # elif self.cap is not None: # try: # if self.cap.isOpened(): # self.cap.release() # except Exception: # pass # self.cap = None # if self.verbose: # print("Sensor disconnected.") cv2.destroyAllWindows() # ========================================================================= # Public API - Configuration & Calibration # ========================================================================= def configure( self, *, lowpass_cutoff_hz=None, lowpass_sample_rate_hz=None, lowpass_order=None, lowpass_enabled=None, hhd_downsample=None, ): """Update runtime-safe settings without reconnecting the camera. Parameters ---------- lowpass_cutoff_hz : float, optional Force low-pass cutoff in Hz. ``0`` disables filtering. A positive value enables filtering unless ``lowpass_enabled=False``. lowpass_sample_rate_hz : float, optional Assumed force sample rate for the Butterworth design (default 30). lowpass_order : int, optional Butterworth order (default 2). lowpass_enabled : bool, optional Explicit on/off for the causal force filter. hhd_downsample : int, optional Integer ``>= 1``. Rebuilds the HHD solver on the next frame. """ filter_changed = False if lowpass_cutoff_hz is not None: self._lowpass_cutoff_hz = float(lowpass_cutoff_hz) filter_changed = True if lowpass_enabled is None: self._lowpass_enabled = self._lowpass_cutoff_hz > 0 if lowpass_sample_rate_hz is not None: rate = float(lowpass_sample_rate_hz) if rate <= 0: raise ValueError("lowpass_sample_rate_hz must be > 0") self._lowpass_sample_rate_hz = rate filter_changed = True if lowpass_order is not None: order = int(lowpass_order) if order < 1: raise ValueError("lowpass_order must be >= 1") self._lowpass_order = order filter_changed = True if lowpass_enabled is not None: self._lowpass_enabled = bool(lowpass_enabled) filter_changed = True if filter_changed: cutoff = self._lowpass_cutoff_hz if self._lowpass_enabled else 0.0 self._force_filter.configure( cutoff_hz=cutoff, sample_rate_hz=self._lowpass_sample_rate_hz, order=self._lowpass_order, ) if self.compute_deformation_valid: self._apply_force_filter() if hhd_downsample is not None: try: k = max(1, int(hhd_downsample)) except (TypeError, ValueError) as exc: raise ValueError("hhd_downsample must be an integer >= 1") from exc if k != self._hhd_downsample: self._hhd_downsample = k self.__hhd = None self._hhd_grid_shape = None self._update_hhd_mask_cache() return self def _bundle_name_candidates(self) -> list[str]: """Product bundle names implied by ``config_name``.""" text = str(self.config_name or "").strip().replace("\\", "/") if not text: return [] candidates: list[str] = [] stem = Path(text).stem if text else "" if stem: candidates.append(stem) parts = [p for p in text.split("/") if p] if "sensors" in parts: idx = parts.index("sensors") if idx + 1 < len(parts): candidates.append(parts[idx + 1]) for name in configs.PRODUCT_BUNDLES: if text == name or text.endswith(f"/sensors/{name}") or text.endswith(f"/sensors/{name}/sensor.json"): candidates.append(name) seen: set[str] = set() ordered: list[str] = [] for candidate in candidates: alias = configs.CONFIG_ALIASES.get(candidate, candidate) for name in (alias, candidate): if name in seen: continue seen.add(name) ordered.append(name) return ordered def _init_force_calibration(self) -> None: """Load force calibration from ``cal_path`` or the product bundle.""" self.calibration = None self._force_cal_sensor_name = None if self.cal_path is not None and str(self.cal_path).strip(): # Explicit path: missing/invalid raises (no silent fallback). self.calibration = cal_mod.load(self.cal_path, strict=True) return for name in self._bundle_name_candidates(): cal = cal_mod.load(sensor_type=name) if cal is not None: self.calibration = cal self._force_cal_sensor_name = name return @property def force_calibrated(self) -> bool: """True when at least one channel curve is loaded.""" return self.calibration is not None and bool(self.calibration.channels) @property def force_unit(self) -> str: return "N" if self.force_calibrated else "raw" def set_calibration( self, cal: "cal_mod.Calibration | str | Path | None", ) -> None: """Attach or replace the active force calibration (live swap after save).""" if cal is None: self.calibration = None self._force_cal_sensor_name = None return if isinstance(cal, cal_mod.Calibration): self.calibration = cal return self.calibration = cal_mod.load(cal, strict=True) def _apply_force_filter(self): raw = np.asarray(self._force_raw, dtype=np.float64) if self._lowpass_enabled and self._lowpass_cutoff_hz > 0: filtered = self._force_filter.process(raw) fn, fx, fy = (float(v) for v in filtered) else: fn, fx, fy = float(raw[0]), float(raw[1]), float(raw[2]) # Apply loaded force_calibration.json when present; otherwise keep raw. if self.calibration is not None: fn, fx, fy = self.calibration.convert(fn, fx, fy) fx, fy = float(fx), float(fy) fn = float(fn) self.fnormal, self.fshearx, self.fsheary = fn, fx, fy # def export_config(self, path="./config/exported_config.json"): # """Return current configuration dict.""" # configs.export_config(self._config, path) # if self.verbose: # print(f"Config exported to {path}") # return True # def stitch_cal(self): # """ # Load or create stitch calibration. # Returns: # np.ndarray: Stitched calibration image # """ # if self.cal_path is None: # self.cal_path = "./config/cal.npy" # if os.path.exists(self.cal_path): # if self.verbose: # print("Loading calibration from", self.cal_path) # self.stitch_calibration = np.load(self.cal_path, allow_pickle=True) # self.stitcher.stitch_calibration = self.stitch_calibration # return self.stitcher.stitch_rt_optimized() # else: # if self.verbose: # print("Creating new calibration...") # img, self.stitch_calibration = self.stitcher.stitch_cal() # os.makedirs("./config", exist_ok=True) # np.save(self.cal_path, self.stitch_calibration) # if self.verbose: # print("Calibration saved to", self.cal_path) # return img # ========================================================================= # Private - Initialization Helpers # ========================================================================= def _load_config(self): """Load sensor configuration from JSON file.""" self._config = configs.load_config(self.config_name) self.sensor_type = self._config.get( "type", "compound_eye" if "stitcher" in self._config else "single_camera", ) self.__config_cam = self._config["camera"] # Capture resolution/fps now applied via CaptureConfig.from_camera_dict(__config_cam). # self._config_resolution_raw = self.__config_cam["resolution_raw"] # self._config_fps = self.__config_cam["fps"] self._config_units = self.__config_cam.get("units", 1) self._config_resolution_stitch = self.__config_cam["resolution_stitch"] # self._config_dis_preset = self._config["tracker"]["dis_preset"] self.__config_tracker = self._config["tracker"] self.__config_stitcher = self._config.get("stitcher") self.__config_contact = self._config["contact"] if self.verbose: print(f"Config: {self.config_name}") return True def _is_compound_eye(self): return str(self.sensor_type).lower() in ("compound_eye", "compound-eye", "compound") def _open_video_source(self, override, box): """Initialize video capture and calibration.""" if self.verbose: print("Connecting to video source...") if self._capture_config_override is not None: capture_config = self._capture_config_override else: capture_config = CaptureConfig.from_camera_dict( self.vid_src, self.__config_cam, reconnect=ReconnectPolicy(enabled=self._auto_reconnect), verbose=self.verbose, background=False, ) try: self._capture_source = open_capture(capture_config, start_background=False) except Exception as exc: raise Exception("Failed to open video source.") from exc self.cap = self._capture_source.cap if not self._is_source_open(): raise Exception("Failed to open video source.") self._capture_source.warmup_frames(3) # Wait for first valid frame frame = None while frame is None: ret, frame = self._read_frame_raw() if not ret: time.sleep(0.1) if not self._is_compound_eye(): self.stitch_valid = False self.initial_img = self._make_initial_gray(frame) elif override: if box is not None: self.stitcher.isstitch = self.isstitch = False self.stitcher.boxes = box self.stitch_valid = bool(self.isstitch) img = self.frame_processor.process(frame) self.initial_img = self._make_initial_gray(img) else: print("Override=True but no box provided.") else: self._find_stitch_boxes(frame) if not self.stitch_valid: raise RuntimeError( f"Failed to detect {self._config_units} vision tiles for stitching. " "Check camera index, lighting, and that MJPG 1080p is negotiated." ) img = self.frame_processor.process(frame) self.initial_img = self._make_initial_gray(img) # For live cameras, start background capture to avoid latency buildup. if self._is_live_camera and self._capture_source is not None: self._capture_source.start_background() def _make_initial_gray(self, img_bgr): gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) self.resolution_stitch = gray.shape initial = cv2.resize(gray, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC) if self.mirror: initial = cv2.flip(initial, 1) return initial.astype(np.uint8) def _find_stitch_boxes(self, img_src, show=False): """Detect vision tile boxes with camera stabilization.""" n_boxes, v_mean0, timeout = 0, 0, 5 boxes = [] best_n_boxes = 0 best_boxes = [] start_time = cv2.getTickCount() while n_boxes < self._config_units: ret, frame = self._read_frame_raw() if not ret: continue v_mean = np.sum(cv2.mean(frame)) if abs(v_mean - v_mean0) < 5: n_boxes, boxes = self.stitcher.find_stitch_boxes(frame, show=show) if n_boxes > best_n_boxes: best_n_boxes = n_boxes best_boxes = boxes if (cv2.getTickCount() - start_time) / cv2.getTickFrequency() > timeout: print("Timeout finding vision units.") break v_mean0 = v_mean if n_boxes < self._config_units and best_n_boxes >= self._config_units: n_boxes = best_n_boxes boxes = best_boxes self.n_boxes = n_boxes self.boxes = boxes self.stitch_valid = self.isstitch and n_boxes == self._config_units status = "valid for stitching" if self.stitch_valid else "non-stitch mode" print(f"Sensor: {status}") return n_boxes # Unused after frame_processor owns stitch extract/process. # def _extract_tiles(self, img_src): # """Extract tiles from source image.""" # self.stitcher.get_stitch_unit(img_src) # self.stitcher.extract_tiles() # return True def _is_source_open(self): if self._capture_source is not None: return self._capture_source.is_open() return False # Legacy PyAV / direct-cap fallbacks (unused after orisys.capture refactor). # if self._use_pyav: # return self._av_container is not None # return self.cap is not None and self.cap.isOpened() def _read_frame(self): if self._capture_source is not None and self._capture_source.running: return self._capture_source.read_latest(copy=True, wait_s=1.0) return self._read_frame_raw() def _read_frame_raw(self): if self._capture_source is not None: return self._capture_source.read_raw() return False, None # Legacy PyAV / direct-cap fallbacks (unused after orisys.capture refactor). # if self._use_pyav: # if self._av_decoder is None: # return False, None # try: # frame = next(self._av_decoder) # except StopIteration: # return False, None # except Exception: # return False, None # return True, frame.to_ndarray(format="bgr24") # if self.cap is None: # return False, None # return self.cap.read() @property def capture_status(self): """Return capture health snapshot, or None in external-frame mode.""" if self._capture_source is None: return None return self._capture_source.status @property def _capture_worker(self): """Deprecated alias for ``_capture_source``.""" return self._capture_source # ========================================================================= # Private - Utilities # ========================================================================= def _print_timings(self, name, timings): """Print timing breakdown.""" print(f"\n[{name} timing]") for k, v in timings.items(): if k != 'total': print(f" {k}: {v:7.2f} ms") print(f" ─────────────────────────") print(f" TOTAL: {timings['total']:7.2f} ms ({1000/timings['total']:.1f} FPS)")