write frames and animation tweaks
This commit is contained in:
Ruben van de Ven
parent
c56f6ff3b4
commit
0a4cfc1766
11 changed files with 285 additions and 52 deletions
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@ -47,7 +47,10 @@ process_data = "trap.process_data:main"
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blacklist = "trap.tools:blacklist_tracks"
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rewrite_tracks = "trap.tools:rewrite_raw_track_files"
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model_train = "trap.models.train:train"
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trap_video_source = "trap.frame_emitter:FrameEmitter.parse_and_start"
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trap_video_writer = "trap.frame_writer:FrameWriter.parse_and_start"
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trap_tracker = "trap.tracker:Tracker.parse_and_start"
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trap_stage = "trap.stage:Stage.parse_and_start"
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trap_prediction = "trap.prediction_server:PredictionServer.parse_and_start"
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@ -23,8 +23,8 @@ directory=%(here)s
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autostart=false
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[program:video]
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command=uv run trap_video_source --homography ../DATASETS/hof3/homography.json --video-src ../DATASETS/hof3/hof3-cam-demo-twoperson.mp4 --calibration ../DATASETS/hof3/calibration.json --video-loop
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# command=uv run trap_video_source --homography ../DATASETS/hof3-cam-baumer/homography.json --video-src gige://../DATASETS/hof3-cam-baumer/gige_config.json --calibration ../DATASETS/hof3-cam-baumer/calibration.json
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# command=uv run trap_video_source --homography ../DATASETS/hof3/homography.json --video-src ../DATASETS/hof3/hof3-cam-demo-twoperson.mp4 --calibration ../DATASETS/hof3/calibration.json --video-loop
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command=uv run trap_video_source --homography ../DATASETS/hof3-cam-baumer-cropped/homography.json --video-src gige://../DATASETS/hof3-cam-baumer-cropped/gige_config.json --calibration ../DATASETS/hof3-cam-baumer-cropped/calibration.json
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directory=%(here)s
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directory=%(here)s
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@ -38,6 +38,8 @@ directory=%(here)s
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[program:predictor]
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command=uv run trap_prediction --eval_device cuda:0 --model_dir EXPERIMENTS/models/models_20241229_21_35_13_hof3-m2-ud-split-conv12-f2.0-map-2024-12-29/ --num-samples 1 --map_encoding --eval_data_dict EXPERIMENTS/trajectron-data/hof3-m2-ud-split-nostep-conv12-f2.0-map-2024-12-29_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode
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# uv run trajectron_train --continue_training_from EXPERIMENTS/models/models_20241229_21_35_13_hof3-m2-ud-split-conv12-f2.0-map-2024-12-29/ --eval_every 5 --train_data_dict hof3-nostep-conv12-f2.0-map-2024-12-27_train.pkl --eval_data_dict hof3-nostep-conv12-f2.0-map-2024-12-27_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _hof3-conv12-f2.0-map-2024-12-27 --train_epochs 10 --conf EXPERIMENTS/config.json --data_dir EXPERIMENTS/trajectron-data --map_encoding
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directory=%(here)s
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[program:render_cv]
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17
trap/base.py
17
trap/base.py
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@ -179,6 +179,7 @@ class DistortedCamera(ABC):
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coords = self.project_points(coords, scale)
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return coords
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class FisheyeCamera(DistortedCamera):
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def __init__(self, dim1, dim2, dim3, K, D, new_K, scaled_K, balance, H, fps):
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# dimensions as per: https://medium.com/@kennethjiang/calibrate-fisheye-lens-using-opencv-part-2-13990f1b157f
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@ -198,8 +199,24 @@ class FisheyeCamera(DistortedCamera):
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self.map1, self.map2 = cv2.fisheye.initUndistortRectifyMap(self.scaled_K, self.D, self._R, self.new_K, self.dim3, cv2.CV_16SC2)
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# self.map1, self.map2 = cv2.fisheye.initUndistortRectifyMap(self.scaled_K, self.D, self._R, self.new_K, self.dim3, cv2.CV_32FC1)
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def undistort_img(self, img: MatLike):
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# map1, map2 = adjust_remap_maps(self.map1, self.map2, 2, (0,0))
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# this only works on the undistort, but screws up when doing subsequent homography,
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# there needs to be a way to combine both this remap and warpPerspective into a
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# single remap call...
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# scale = 0.3
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# cx = self.dim3[0] / 2
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# cy = self.dim3[1] / 2
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# map1 = (self.map1 - cx) / scale + cx
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# map2 = (self.map2 - cy) / scale + cy
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# map1 += 900 #translate x (>0 left, <0 right)
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# map2 += 1500 #translate y (>0 up, <0 down)
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return cv2.remap(img, self.map1, self.map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
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def undistort_points(self, distorted_points: PointList):
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@ -333,6 +333,8 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
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points = frame.camera.points_img_to_world(points, scale)
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points = [to_point(p) for p in points] # to int
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w = points[1][0]-points[2][0]
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feet = [int(points[2][0] + .5 * w), points[2][1]]
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cv2.rectangle(img, points[1], points[2], (255,255,0), 2)
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cv2.circle(img, points[0], 5, (255,255,0), 2)
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@ -31,8 +31,6 @@ class FrameEmitter(node.Node):
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self.video_srcs = self.config.video_src
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def run(self):
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offset = int(self.config.video_offset or 0)
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source = get_video_source(self.video_srcs, self.config.camera, offset, self.config.video_end, self.config.video_loop)
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97
trap/frame_writer.py
Normal file
97
trap/frame_writer.py
Normal file
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@ -0,0 +1,97 @@
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# used for "Forward Referencing of type annotations"
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from __future__ import annotations
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import datetime
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import logging
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import time
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from argparse import ArgumentParser
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from pathlib import Path
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import zmq
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from trap.frame_emitter import Frame
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from trap.node import Node
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from trap.preview_renderer import FrameWriter as CvFrameWriter
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logger = logging.getLogger("trap.simple_renderer")
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class FrameWriter(Node):
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def setup(self):
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self.frame_sock = self.sub(self.config.zmq_frame_addr)
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self.out_writer = self.start_writer()
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def start_writer(self):
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if not self.config.output_dir.exists():
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raise FileNotFoundError("Path does not exist")
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date_str = datetime.datetime.now().isoformat(timespec="minutes")
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filename = self.config.output_dir / f"render-source-{date_str}.mp4"
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logger.info(f"Write to {filename}")
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return CvFrameWriter(str(filename), None, None)
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# fourcc = cv2.VideoWriter_fourcc(*'vp09')
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# return cv2.VideoWriter(str(filename), fourcc, self.fps, self.frame_size)
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def run(self):
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i=0
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try:
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while self.run_loop():
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i += 1
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# zmq_ev = self.frame_sock.poll(timeout=2000)
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# if not zmq_ev:
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# # when no data comes in, loop so that is_running is checked
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# continue
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try:
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frame: Frame = self.frame_sock.recv_pyobj(zmq.NOBLOCK)
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# else:
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# logger.debug(f'new video frame {frame.index}')
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if frame is None:
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# might need to wait a few iterations before first frame comes available
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time.sleep(.1)
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continue
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self.logger.debug(f"write frame {frame.time:.3f}")
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self.out_writer.write(frame.img)
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except zmq.ZMQError as e:
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# idx = frame.index if frame else "NONE"
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# logger.debug(f"reuse video frame {idx}")
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pass
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except KeyboardInterrupt as e:
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print('stopping on interrupt')
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self.logger.info('Stopping')
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# if i>2:
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if self.out_writer:
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self.out_writer.release()
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self.logger.info(f'Wrote to {self.out_writer.filename}')
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self.logger.info('stopped')
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@classmethod
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def arg_parser(cls):
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argparser = ArgumentParser()
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argparser.add_argument('--zmq-frame-addr',
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help='Manually specity communication addr for the frame messages',
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type=str,
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default="ipc:///tmp/feeds_frame")
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argparser.add_argument("--output-dir",
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help="Directory to save the video in",
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required=True,
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type=Path)
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return argparser
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@ -86,7 +86,8 @@ def get_maps_for_input(input_dict, scene, hyperparams, device):
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scene_map = scene.map[node.type]
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# map_point = x[-1, :2]
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map_point = x[:2]
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# map_point = x[:2]
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map_point = x[:2].clip(0) # prevent crash for out of map point.
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patch_size = hyperparams['map_encoder'][node.type]['patch_size']
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@ -102,6 +103,7 @@ def get_maps_for_input(input_dict, scene, hyperparams, device):
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heading_angles = torch.Tensor(heading_angles)
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# print(scene_maps, patch_sizes, heading_angles)
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# print(scene_pts)
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maps = scene_maps[0].get_cropped_maps_from_scene_map_batch(scene_maps,
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scene_pts=torch.Tensor(scene_pts),
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patch_size=patch_sizes[0],
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@ -163,6 +163,8 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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print(f"Camera FPS: {camera.fps}, actual fps: {camera.fps/step_size} (or {(1/camera.fps)*step_size})")
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names = {}
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for data_class, nr_of_items in destinations.items():
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env = Environment(node_type_list=['PEDESTRIAN'], standardization=standardization)
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attention_radius = dict()
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@ -172,6 +174,7 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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scenes = []
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split_id = f"{name}_{data_class}"
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data_dict_path = dst_dir / (split_id + '.pkl')
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names[data_class] = data_dict_path
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# subpath = src_dir / data_class
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@ -298,6 +301,7 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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# print(f"Linear: {l}")
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# print(f"Non-Linear: {nl}")
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print(f"error: {skipped_for_error}, used: {created}")
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return names
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def main():
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parser = argparse.ArgumentParser()
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@ -13,7 +13,9 @@ import time
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from typing import Dict, List, Optional, Tuple
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from matplotlib.pyplot import isinteractive
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import numpy as np
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from shapely import LineString, line_locate_point, linestrings
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from shapely import LineString, MultiLineString, line_locate_point, linestrings
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from shapely.ops import substring
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from statemachine import Event, State, StateMachine
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from statemachine.exceptions import TransitionNotAllowed
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import zmq
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@ -36,8 +38,9 @@ logger = logging.getLogger('trap.stage')
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Coordinate = Tuple[float, float]
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DeltaT = float # delta_t in seconds
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OPTION_POSITION_MARKER = False
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OPTION_GROW_ANOMALY_CIRCLE = False
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OPTION_RENDER_DIFF_SEGMENT = True
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# OPTION_RENDER_DIFF_SEGMENT = True
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class LineGenerator(ABC):
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@abstractmethod
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@ -328,7 +331,7 @@ class ScenarioScene(Enum):
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LOST = -1
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LOST_FADEOUT = 3
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PREDICTION_INTERVAL: float|None = 20 # frames
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PREDICTION_INTERVAL: float|None = 10 # frames
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PREDICTION_FADE_IN: float = 3
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PREDICTION_FADE_SLOPE: float = -10
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PREDICTION_FADE_AFTER_DURATION: float = 10 # seconds
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@ -576,8 +579,8 @@ class DrawnScenario(TrackScenario):
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# 0. Update anomaly, slowly decreasing it over time
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self.decay_anomaly_score(dt)
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for diff in self.prediction_diffs:
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diff.update_drawn_positions(dt, self)
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# for diff in self.prediction_diffs:
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# diff.update_drawn_positions(dt, self)
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# 1. track history, direct update
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@ -587,8 +590,8 @@ class DrawnScenario(TrackScenario):
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if self.drawn_position is None:
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self.drawn_position = self.drawn_positions[-1]
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else:
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self.drawn_position[0] = exponentialDecay(self.drawn_position[0], self.drawn_positions[-1][0], 3, dt)
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self.drawn_position[1] = exponentialDecay(self.drawn_position[1], self.drawn_positions[-1][1], 3, dt)
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self.drawn_position[0] = exponentialDecay(self.drawn_position[0], self.drawn_positions[-1][0], 13, dt)
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self.drawn_position[1] = exponentialDecay(self.drawn_position[1], self.drawn_positions[-1][1], 13, dt)
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# 3. predictions
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if len(self.drawn_predictions) < len(self.predictions):
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@ -720,14 +723,15 @@ class DrawnScenario(TrackScenario):
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# 2. Position Marker / anomaly score
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if OPTION_POSITION_MARKER:
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anomaly_marker_color = SrgbaColor(0.,0.,1, 1.-self.lost_factor()) # fadeout
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# lines.append(circle_arc(self.drawn_positions[-1][0], self.drawn_positions[-1][1], 1, t, self.anomaly_score, anomaly_marker_color))
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# last point, (but this draws line in circle, requiring a 'jump back' for the laser)
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cx, cy = self.drawn_positions[-1][0], self.drawn_positions[-1][1],
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cx, cy = self.drawn_position[0], self.drawn_position[1],
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radius = max(.1, self._drawn_anomaly_score * 1.) if OPTION_GROW_ANOMALY_CIRCLE else .1
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steps=5
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steps=0
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if len(self.drawn_positions) >= steps:
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dx, dy = self.drawn_positions[-1][0] - self.drawn_positions[-steps][0], self.drawn_positions[-1][1] - self.drawn_positions[-steps][1],
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diff = np.array([dx,dy])
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@ -745,11 +749,20 @@ class DrawnScenario(TrackScenario):
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# 3. Predictions
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if len(self.drawn_predictions):
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color = SrgbaColor(0.,1,0.,1.-self.lost_factor())
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prediction_track_age = time.time() - self.predictions[0].created_at
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t_factor = prediction_track_age / PREDICTION_FADE_IN
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# positions = [RenderablePosition.from_list(pos) for pos in self.drawn_positions]
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for a, drawn_prediction in enumerate(self.drawn_predictions):
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if a < (len(self.drawn_predictions) - 1):
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# not the newest: fade out:
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deprecation_age = t - self.predictions[a+1].created_at
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if deprecation_age > PREDICTION_FADE_IN:
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# old: skip drawing.
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continue
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else:
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fade_factor = 1 - (deprecation_age / PREDICTION_FADE_IN)
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color = color.as_faded(fade_factor)
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prediction_track_age = time.time() - self.predictions[a].created_at
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t_factor = prediction_track_age / PREDICTION_FADE_IN
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associated_diff = self.prediction_diffs[a]
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progress = associated_diff.nr_of_passed_points()
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@ -772,7 +785,16 @@ class DrawnScenario(TrackScenario):
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# points = [RenderablePoint(pos, pos_color) for pos, pos_color in zip(drawn_prediction[PREDICTION_OFFSET:], colors[PREDICTION_OFFSET:])]
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points = [RenderablePoint(pos, pos_color) for pos, pos_color in zip(drawn_prediction, colors)]
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points = points[progress//2:]
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lines.append(RenderableLine(points))
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ls = LineString(drawn_prediction)
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if t_factor < 1:
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ls = substring(ls, 0, t_factor*ls.length, ls.length)
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dashed = dashed_line(ls, 1, .5, t)
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# print(dashed)
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for line in dashed.geoms:
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dash_points = [RenderablePoint(point, color) for point in line.coords]
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lines.append(RenderableLine(dash_points))
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# lines.append(RenderableLine(points))
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# 4. Diffs
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# for drawn_diff in self.drawn_diffs:
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@ -781,9 +803,10 @@ class DrawnScenario(TrackScenario):
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# points = [RenderablePoint(pos, pos_color) for pos, pos_color in zip(drawn_diff, colors)]
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# lines.append(RenderableLine(points))
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if OPTION_RENDER_DIFF_SEGMENT:
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for diff in self.prediction_diffs:
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lines.append_lines(diff.as_renderable())
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# if OPTION_RENDER_DIFF_SEGMENT:
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# for diff in self.prediction_diffs:
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# lines.append_lines(diff.as_renderable())
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# pass
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# # print(self.current_state)
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@ -1001,7 +1024,7 @@ class Stage(Node):
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# TODO place somewhere else:
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# Gemma3:27b prompt: "python. Given a list of coordinates, that describes a line: `drawable_points: List[Tuple[float,float]]` apply perlin noise over the normal of the line, that changes over time `dt`."
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def apply_perlin_noise_to_line_normal(drawable_points: np.ndarray, dt: float, amplitude: float = 1.0, frequency: float = 1.0) -> np.ndarray:
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def apply_perlin_noise_to_line_normal(drawable_points: np.ndarray, dt: float, amplitude: float = 1.0, frequency: float = 1.0, fade_over_n_points = 8) -> np.ndarray:
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"""
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Applies Perlin noise to the normals of a line described by a list of coordinates, changing over time.
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@ -1068,8 +1091,15 @@ def apply_perlin_noise_to_line_normal(drawable_points: np.ndarray, dt: float, am
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# noise_y = noise([x * frequency, (y + dt) * frequency]) * amplitude * normal_y
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noise = snoise2(i * frequency, dt % 1000, octaves=4)
|
||||
|
||||
noise_x = noise * amplitude * normal_x
|
||||
noise_y = noise * amplitude * normal_y
|
||||
use_amp = amplitude
|
||||
if fade_over_n_points > 0:
|
||||
rev_step = len(drawable_points) - i
|
||||
amp_factor = rev_step / fade_over_n_points
|
||||
if amp_factor < 1:
|
||||
use_amp *= amp_factor
|
||||
|
||||
noise_x = noise * use_amp * normal_x
|
||||
noise_y = noise * use_amp * normal_y
|
||||
|
||||
# print(noise_x, noise_y, dt, frequency, i, dt, snoise2(i * frequency, dt % 1000, octaves=4))
|
||||
|
||||
|
|
@ -1083,3 +1113,28 @@ def apply_perlin_noise_to_line_normal(drawable_points: np.ndarray, dt: float, am
|
|||
# print(drawable_points, new_points)
|
||||
|
||||
return np.array(new_points)
|
||||
|
||||
|
||||
import math
|
||||
|
||||
def distance(p1, p2):
|
||||
return math.hypot(p2[0] - p1[0], p2[1] - p1[1])
|
||||
|
||||
|
||||
def dashed_line(line: LineString, dash_len: float, gap_len: float, offset: float = 0) -> MultiLineString:
|
||||
total_length = line.length
|
||||
|
||||
segments = []
|
||||
pos = offset % (dash_len + gap_len)
|
||||
|
||||
if pos > gap_len:
|
||||
segments.append(substring(line, 0, pos - gap_len))
|
||||
|
||||
while pos < total_length:
|
||||
end = min(pos + dash_len, total_length)
|
||||
if pos < end:
|
||||
dash = substring(line, pos, end)
|
||||
segments.append(dash)
|
||||
pos += dash_len + gap_len
|
||||
|
||||
return MultiLineString(segments)
|
||||
|
|
@ -443,8 +443,10 @@ class Tracker(Node):
|
|||
# self.model = YOLO('EXPERIMENTS/yolov8x.pt')
|
||||
# best from arsen:
|
||||
# self.model = YOLO('./tracker/all_yolo11-2-20-15-41/weights')
|
||||
# self.model = YOLO('tracker/all_yolo11-2-20-15-41/weights/best.pt')
|
||||
# self.model = YOLO('models/yolo11x-pose.pt')
|
||||
# self.model = YOLO("models/yolo12l.pt")
|
||||
# self.model = YOLO("models/yolo12x.pt", imgsz=self.config.imgsz) #see https://github.com/orgs/ultralytics/discussions/8812
|
||||
self.model = YOLO("models/yolo12x.pt")
|
||||
# NOTE: changing the model, also tweak imgsz in
|
||||
elif self.config.detector == DETECTOR_RTDETR:
|
||||
|
|
@ -724,7 +726,7 @@ class Tracker(Node):
|
|||
argparser.add_argument("--imgsz",
|
||||
help="Detector imgsz parameter (applicable to ultralytics detectors)",
|
||||
type=int,
|
||||
default=960)
|
||||
default=480)
|
||||
return argparser
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -35,6 +35,14 @@ class GigEConfig:
|
|||
binning_v: BinningValue = 1
|
||||
pixel_format: int = neoapi.PixelFormat_BayerRG8
|
||||
|
||||
# when changing these values, make sure you also tweak the calibration
|
||||
width: int = 2448
|
||||
height: int = 2048
|
||||
|
||||
# changing these _automatically changes calibration cx and cy_!!
|
||||
offset_x: int = 0
|
||||
offset_y: int = 0
|
||||
|
||||
post_crop_tl: Optional[Coordinate] = None
|
||||
post_crop_br: Optional[Coordinate] = None
|
||||
|
||||
|
|
@ -58,47 +66,90 @@ class GigE(VideoSource):
|
|||
self.camera.SetImageBufferCycleCount(1)
|
||||
self.setPixelFormat(self.config.pixel_format)
|
||||
|
||||
self.cam_is_configured = False
|
||||
|
||||
self.converter_settings = neoapi.ConverterSettings()
|
||||
self.converter_settings.SetDebayerFormat('BGR8') # opencv
|
||||
self.converter_settings.SetDemosaicingMethod(neoapi.ConverterSettings.Demosaicing_Baumer5x5)
|
||||
# self.converter_settings.SetSharpeningMode(neoapi.ConverterSettings.Sharpening_Global)
|
||||
# self.converter_settings.SetSharpeningMode(neoapi.ConverterSettings.Sharpening_ActiveNoiseReduction)
|
||||
# self.converter_settings.SetSharpeningFactor(3)
|
||||
# self.converter_settings.SetSharpeningSensitivityThreshold(2)
|
||||
|
||||
|
||||
|
||||
|
||||
def configCam(self):
|
||||
if self.camera.IsConnected():
|
||||
self.setPixelFormat(self.config.pixel_format)
|
||||
|
||||
# self.camera.f.PixelFormat.Set(neoapi.PixelFormat_RGB8)
|
||||
self.camera.f.BinningHorizontal.Set(self.config.binning_h)
|
||||
self.camera.f.BinningVertical.Set(self.config.binning_v)
|
||||
self.camera.f.Height.Set(self.config.height)
|
||||
self.camera.f.Width.Set(self.config.width)
|
||||
self.camera.f.OffsetX.Set(self.config.offset_x)
|
||||
self.camera.f.OffsetY.Set(self.config.offset_y)
|
||||
|
||||
# print('exposure time', self.camera.f.ExposureAutoMaxValue.Set(20000)) # shutter 1/50
|
||||
print('exposure time', self.camera.f.ExposureAutoMaxValue.Set(25000))
|
||||
print('brightness targt', self.camera.f.BrightnessAutoNominalValue.Get())
|
||||
print('brightness targt', self.camera.f.BrightnessAutoNominalValue.Set(30))
|
||||
print('exposure time', self.camera.f.ExposureTime.Get())
|
||||
print('Gamma', self.camera.f.Gamma.Set(0.39))
|
||||
|
||||
# neoapi.region
|
||||
# self.camera.f.regeo
|
||||
# print('LUT', self.camera.f.LUTIndex.Get())
|
||||
# print('LUT', self.camera.f.LUTEnable.Get())
|
||||
# print('exposure time max', self.camera.f.ExposureTimeGapMax.Get())
|
||||
# print('exposure time min', self.camera.f.ExposureTimeGapMin.Get())
|
||||
# self.pixfmt = self.camera.f.PixelFormat.Get()
|
||||
|
||||
self.cam_is_configured = True
|
||||
|
||||
def setPixelFormat(self, pixfmt):
|
||||
self.pixfmt = pixfmt
|
||||
self.camera.f.PixelFormat.Set(pixfmt)
|
||||
# self.pixfmt = self.camera.f.PixelFormat.Get()
|
||||
|
||||
|
||||
def recv(self):
|
||||
while True:
|
||||
# print('receive')
|
||||
if not self.camera.IsConnected():
|
||||
self.cam_is_configured = False
|
||||
return
|
||||
|
||||
if not self.cam_is_configured:
|
||||
self.configCam()
|
||||
|
||||
|
||||
|
||||
i = self.camera.GetImage(0)
|
||||
if i.IsEmpty():
|
||||
time.sleep(.01)
|
||||
continue
|
||||
|
||||
imgarray = i.GetNPArray()
|
||||
if self.pixfmt == neoapi.PixelFormat_BayerRG12:
|
||||
img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
|
||||
elif self.pixfmt == neoapi.PixelFormat_BayerRG8:
|
||||
img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
|
||||
else:
|
||||
img = cv2.cvtColor(imgarray, cv2.COLOR_BGR2RGB)
|
||||
# print(i.GetAvailablePixelFormats())
|
||||
i = i.Convert(self.converter_settings)
|
||||
|
||||
if img.dtype == np.uint16:
|
||||
img = cv2.convertScaleAbs(img, alpha=(255.0/65535.0))
|
||||
if i.IsEmpty():
|
||||
time.sleep(.01)
|
||||
continue
|
||||
|
||||
img = i.GetNPArray()
|
||||
|
||||
# imgarray = i.GetNPArray()
|
||||
# if self.pixfmt == neoapi.PixelFormat_BayerRG12:
|
||||
# img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
|
||||
# elif self.pixfmt == neoapi.PixelFormat_BayerRG8:
|
||||
# img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
|
||||
# else:
|
||||
# img = cv2.cvtColor(imgarray, cv2.COLOR_BGR2RGB)
|
||||
|
||||
# if img.dtype == np.uint16:
|
||||
# img = cv2.convertScaleAbs(img, alpha=(255.0/65535.0))
|
||||
img = self._crop(img)
|
||||
yield img
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue