mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 03:14:00 +08:00
Ethan Weber
55ebaaf1b5
* might fix the stun server issue * still debugging webrtc * another turn server * working on github actions * updating actions file * fixing actions * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * ga * updating viewer * update docs
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Video Stream objects for WebRTC"""
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import math
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import cv2
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import numpy as np
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from aiortc import VideoStreamTrack
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from av import VideoFrame
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class FlagVideoStreamTrack(VideoStreamTrack):
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"""
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A video track that returns an animated flag. For debugging purposes :')
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"""
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def __init__(self):
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super().__init__() # don't forget this!
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self.counter = 0
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height, width = 480, 640
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# generate flag
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data_bgr = np.hstack(
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[
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self._create_rectangle(width=213, height=480, color=(255, 0, 0)), # blue
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self._create_rectangle(width=214, height=480, color=(255, 255, 255)), # white
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self._create_rectangle(width=213, height=480, color=(0, 0, 255)), # red
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]
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)
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# shrink and center it
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M = np.float32([[0.5, 0, width / 4], [0, 0.5, height / 4]])
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data_bgr = cv2.warpAffine(data_bgr, M, (width, height))
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# compute animation
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omega = 2 * math.pi / height
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id_x = np.tile(np.array(range(width), dtype=np.float32), (height, 1))
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id_y = np.tile(np.array(range(height), dtype=np.float32), (width, 1)).transpose()
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self.frames = []
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for k in range(30):
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phase = 2 * k * math.pi / 30
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map_x = id_x + 10 * np.cos(omega * id_x + phase)
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map_y = id_y + 10 * np.sin(omega * id_x + phase)
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frame = cv2.remap(data_bgr, map_x, map_y, cv2.INTER_LINEAR)
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self.frames.append(VideoFrame.from_ndarray(frame, format="bgr24"))
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async def recv(self):
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pts, time_base = await self.next_timestamp()
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frame = self.frames[self.counter % 30]
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frame.pts = pts
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frame.time_base = time_base
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self.counter += 1
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return frame
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@staticmethod
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def _create_rectangle(width, height, color):
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data_bgr = np.zeros((height, width, 3), np.uint8)
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data_bgr[:, :] = color
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return data_bgr
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class SingleFrameStreamTrack(VideoStreamTrack):
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"""Single Frame stream class: pushes single frames to a stream"""
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def __init__(self):
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super().__init__()
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self.background_frame = np.ones((480, 640, 3), dtype="uint8") * 100 # gray background
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self.frame = None
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self.put_frame(self.background_frame)
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def put_frame(self, frame: np.ndarray) -> None:
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"""Sets the current viewing frame
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Args:
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frame: image to be viewed
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"""
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self.frame = VideoFrame.from_ndarray(frame)
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async def recv(self):
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"""Async method to grab and wait on frame"""
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pts, time_base = await self.next_timestamp()
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frame = self.frame
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frame.pts = pts
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frame.time_base = time_base
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return frame
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