我想解码 H.264 视频序列并将它们显示在屏幕上。视频序列来自 pi 相机，我使用以下代码捕获
import io
import picamera
stream = io.BytesIO()
while True:
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_recording(stream, format='h264', quality=23)
camera.wait_recording(15)
camera.stop_recording()
有什么方法可以解码“流”数据序列并使用 opencv 或其他 python 库显示它们？
我找到了一个使用ffmpeg-python的解决方案。
我无法验证 raspberry-pi 中的解决方案，所以我不确定它是否适合您。
假设：
stream将整个捕获的 h264 流保存在内存缓冲区中。 该解决方案适用以下内容：
FFmpeg的子流程中执行。
输入将是视频流（内存缓冲区）。
输出格式是 BGR 像素格式的原始视频帧。 sdtinpipestdoutpipepipe(to stdin)。 cv2.imshow） 这是代码：
import ffmpeg
import numpy as np
import cv2
import io
width, height = 640, 480
# Seek to stream beginning
stream.seek(0)
# Execute FFmpeg in a subprocess with sdtin as input pipe and stdout as output pipe
# The input is going to be the video stream (memory buffer)
# The output format is raw video frames in BGR pixel format.
# https://github.com/kkroening/ffmpeg-python/blob/master/examples/README.md
# https://github.com/kkroening/ffmpeg-python/issues/156
# http://zulko.github.io/blog/2013/09/27/read-and-write-video-frames-in-python-using-ffmpeg/
process = (
ffmpeg
.input('pipe:')
.video
.output('pipe:', format='rawvideo', pix_fmt='bgr24')
.run_async(pipe_stdin=True, pipe_stdout=True)
)
# https://stackoverflow.com/questions/20321116/can-i-pipe-a-io-bytesio-stream-to-subprocess-popen-in-python
# https://gist.github.com/waylan/2353749
process.stdin.write(stream.getvalue()) # Write stream content to the pipe
process.stdin.close() # close stdin (flush and send EOF)
#Read decoded video (frame by frame), and display each frame (using cv2.imshow)
while(True):
# Read raw video frame from stdout as bytes array.
in_bytes = process.stdout.read(width * height * 3)
if not in_bytes:
break
# transform the byte read into a numpy array
in_frame = (
np
.frombuffer(in_bytes, np.uint8)
.reshape([height, width, 3])
)
#Display the frame
cv2.imshow('in_frame', in_frame)
if cv2.waitKey(100) & 0xFF == ord('q'):
break
process.wait()
cv2.destroyAllWindows()
注意：我使用sdtinandstdout作为管道（而不是使用命名管道），因为我希望代码也可以在 Windows 中工作。
为了测试解决方案，我创建了一个示例视频文件，并将其读入内存缓冲区（编码为 H.264）。
我使用内存缓冲区作为上述代码的输入（替换你的stream）。
这是完整的代码，包括测试代码：
import ffmpeg
import numpy as np
import cv2
import io
in_filename = 'in.avi'
# Build synthetic video, for testing begins:
###############################################
# ffmpeg -y -r 10 -f lavfi -i testsrc=size=160x120:rate=1 -c:v libx264 -t 5 in.mp4
width, height = 160, 120
(
ffmpeg
.input('testsrc=size={}x{}:rate=1'.format(width, height), r=10, f='lavfi')
.output(in_filename, vcodec='libx264', crf=23, t=5)
.overwrite_output()
.run()
)
###############################################
# Use ffprobe to get video frames resolution
###############################################
p = ffmpeg.probe(in_filename, select_streams='v');
width = p['streams'][0]['width']
height = p['streams'][0]['height']
n_frames = int(p['streams'][0]['nb_frames'])
###############################################
# Stream the entire video as one large array of bytes
###############################################
# https://github.com/kkroening/ffmpeg-python/blob/master/examples/README.md
in_bytes, _ = (
ffmpeg
.input(in_filename)
.video # Video only (no audio).
.output('pipe:', format='h264', crf=23)
.run(capture_stdout=True) # Run asynchronous, and stream to stdout
)
###############################################
# Open In-memory binary streams
stream = io.BytesIO(in_bytes)
# Execute FFmpeg in a subprocess with sdtin as input pipe and stdout as output pipe
# The input is going to be the video stream (memory buffer)
# The ouptut format is raw video frames in BGR pixel format.
# https://github.com/kkroening/ffmpeg-python/blob/master/examples/README.md
# https://github.com/kkroening/ffmpeg-python/issues/156
# http://zulko.github.io/blog/2013/09/27/read-and-write-video-frames-in-python-using-ffmpeg/
process = (
ffmpeg
.input('pipe:')
.video
.output('pipe:', format='rawvideo', pix_fmt='bgr24')
.run_async(pipe_stdin=True, pipe_stdout=True)
)
# https://stackoverflow.com/questions/20321116/can-i-pipe-a-io-bytesio-stream-to-subprocess-popen-in-python
# https://gist.github.com/waylan/2353749
process.stdin.write(stream.getvalue()) # Write stream content to the pipe
process.stdin.close() # close stdin (flush and send EOF)
#Read decoded video (frame by frame), and display each frame (using cv2.imshow)
while(True):
# Read raw video frame from stdout as bytes array.
in_bytes = process.stdout.read(width * height * 3)
if not in_bytes:
break
# transform the byte read into a numpy array
in_frame = (
np
.frombuffer(in_bytes, np.uint8)
.reshape([height, width, 3])
)
#Display the frame
cv2.imshow('in_frame', in_frame)
if cv2.waitKey(100) & 0xFF == ord('q'):
break
process.wait()
cv2.destroyAllWindows()
我不知道你到底想做什么，但没有 FFMPEG 的另一种方法是：
如果您阅读 picam 文档，您会看到视频端口有分离器，您可以使用splitter_port=x (1<=x<=3) kwarg for camera 访问这些分离器。start_recorder()：
https ://picamera.readthedocs.io/en/release-1.13/api_camera.html#picamera.PiCamera.start_recording
基本上，这意味着您可以将录制的流拆分为 2 个子流，一个编码为 h264 以进行保存或其他，另一个将其编码为 OPENCV 兼容格式。https://picamera.readthedocs.io/en/release-1.13/recipes2.html?highlight=splitter#capturing-to-an-opencv-object
这一切主要发生在 GPU 中，因此速度非常快（有关更多信息，请参阅 picamera 文档）
如果您需要一个示例，这与他们在这里所做的相同： https://picamera.readthedocs.io/en/release-1.13/recipes2.html?highlight=splitter#recording-at-multiple-resolutions 但随后使用 opencv对象和一个 h264 流
我认为 OpenCV 不知道如何解码 H264，因此您必须依靠其他库将其转换为 RGB 或 BGR。
另一方面，您可以format='bgr'在picamera中使用，让您的生活更轻松：
@Rotem 的答案是正确的，但它不适用于大视频块。
process.stdin.write要处理更大的视频，我们需要替换process.communicate. 更新以下行
...
# process.stdin.write(stream.getvalue()) # Write stream content to the pipe
outs, errs = process.communicate(input=stream.getvalue())
# process.stdin.close() # close stdin (flush and send EOF)
# Read decoded video (frame by frame), and display each frame (using cv2.imshow)
position = 0
ct = time.time()
while(True):
# Read raw video frame from stdout as bytes array.
in_bytes = outs[position: position + width * height * 3]
position += width * height * 3
...