update dd02 3d viewer v1
This commit is contained in:
ykcheungab
+37
-34
@@ -7,7 +7,6 @@ and Helmholtz-Hodge decomposition.
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"""
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import os
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import platform
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import numpy as np
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import cv2
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import time
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@@ -23,10 +22,7 @@ from .hhd import nHHD
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from . import configs
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from .get_depth import Contact3D
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from .backend_availability import (check_cupy_available, check_opencl_available)
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try:
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import av # Optional: used for Linux camera capture path.
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except ImportError:
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av = None
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from .frame_processors import create_frame_processor
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class Sensor:
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"""
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@@ -149,7 +145,12 @@ class Sensor:
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}
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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.stitcher = (
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Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose)
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if self._is_compound_eye()
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else None
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)
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self.frame_processor = create_frame_processor(self.sensor_type, stitcher=self.stitcher)
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self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, config_tracker=self.__config_tracker)
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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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@@ -167,7 +168,6 @@ class Sensor:
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self._av_stream = None
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self._av_decoder = None
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self.cap = None
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# Treat both camera index (0) and V4L2 path ("/dev/video0") as live camera for threading/low-latency path.
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self._is_live_camera = (
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isinstance(self.vid_src, int)
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or (isinstance(self.vid_src, str) and self.vid_src.startswith("/dev/video"))
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@@ -219,10 +219,9 @@ class Sensor:
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t_processing_start = time.perf_counter()
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self.frame = frame
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self._extract_tiles(frame)
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img_bgr = self.frame_processor.process(frame)
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t_after_extract = time.perf_counter()
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img_bgr = self.stitcher.stitch_rt_optimized(blend=False)
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t_after_stitch = time.perf_counter()
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self.img_size_raw = img_bgr.shape
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@@ -538,22 +537,29 @@ class Sensor:
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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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self.sensor_type = self._config.get(
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"type",
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"compound_eye" if "stitcher" in self._config else "single_camera",
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)
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self.__config_cam = self._config["camera"]
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self._config_resolution_raw = self.__config_cam["resolution_raw"]
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self._config_fps = self.__config_cam["fps"]
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self._config_units = self.__config_cam["units"]
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self._config_units = self.__config_cam.get("units", 1)
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self._config_resolution_stitch = self.__config_cam["resolution_stitch"]
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self._config_dis_preset = self._config["tracker"]["dis_preset"]
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self.__config_tracker = self._config["tracker"]
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self.__config_stitcher = self._config["stitcher"]
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self.__config_stitcher = self._config.get("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 _is_compound_eye(self):
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return str(self.sensor_type).lower() in ("compound_eye", "compound-eye", "compound")
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def _open_video_source(self, override, box):
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"""Initialize video capture and calibration."""
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# _is_camera_index = isinstance(self.vid_src, int)
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@@ -589,16 +595,11 @@ class Sensor:
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self.use_pyav = False
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if not self._use_pyav:
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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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# Note: Not all backends support this, but it helps when available
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# try:
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# self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# except Exception:
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# pass # Some backends don't support this property
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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._is_live_camera:
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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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@@ -633,7 +634,10 @@ class Sensor:
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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 not self._is_compound_eye():
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self.stitch_valid = False
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self.initial_img = self._make_initial_gray(frame)
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elif 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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@@ -641,24 +645,23 @@ class Sensor:
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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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# Feature-based stitching no longer needs eager overlap calibration here.
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# Keep stitch_cal() as a fallback so older overlap-based stitching still works.
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img = self.stitcher.stitch_rt_optimized()
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# if img is None:
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# img = self.stitch_cal()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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self.resolution_stitch = img.shape
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self.initial_img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
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if self.mirror:
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self.initial_img = cv2.flip(self.initial_img, 1)
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self.initial_img = self.initial_img.astype(np.uint8)
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img = self.frame_processor.process(frame)
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self.initial_img = self._make_initial_gray(img)
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# For live cameras, start a latest-frame capture thread to avoid latency buildup.
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if self._is_live_camera:
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self._start_capture_thread()
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def _make_initial_gray(self, img_bgr):
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gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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self.resolution_stitch = gray.shape
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initial = cv2.resize(gray, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
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if self.mirror:
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initial = cv2.flip(initial, 1)
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return initial.astype(np.uint8)
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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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