FFMPEGCV is an alternative to OPENCV for video read and write.

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The ffmpegcv provide Video Reader and Video Witer with ffmpeg backbone, which are faster and powerful than cv2.

The ffmpegcv is api compatible to open-cv.
The ffmpegcv can use GPU accelerate encoding and decoding*.
The ffmpegcv support much more video codecs v.s. open-cv.
The ffmpegcv support RGB & BGR & GRAY format as you like.
The ffmpegcv can support ROI operations.You can crop, resize and pad the ROI.

In all, ffmpegcv is just similar to opencv api. But is has more codecs and does't require opencv installed.

Functions:

VideoWriter: Write a video file.
VideoCapture: Read a video file.
VideoCaptureNV: Read a video file by NVIDIA GPU.
VideoCaptureQSV: Read a video file by Intel QuickSync Video.
VideoCaptureCAM: Read a camera.
VideoCaptureStream: Read a RTP/RTSP/RTMP/HTTP stream.
noblock: Read/Write a video file in background using mulitprocssing.

Install

You need to download ffmpeg before you can use ffmpegcv.

 #1A. LINUX: sudo apt install ffmpeg
 #1B. MAC (No NVIDIA GPU): brew install ffmpeg
 #1C. WINDOWS: download ffmpeg and add to the path
 #1D. CONDA: conda install ffmpeg=6.0.0     #don't use the default 4.x.x version
 
 #2. python
 pip install ffmpegcv

When should choose `ffmpegcv` other than `opencv`:

The opencv is hard to install. The ffmpegcv only requires numpy and FFmpeg, works across Mac/Windows/Linux platforms.
The opencv packages too much image processing toolbox. You just want a simple video/camero IO with GPU accessible.
The opencv didn't support h264/h265 and other video writers.
You want to crop, resize and pad the video/camero ROI.

Basic example

Read a video by CPU, and rewrite it by GPU.

vidin = ffmpegcv.VideoCapture(vfile_in)
vidout = ffmpegcv.VideoWriterNV(vfile_out, 'h264', vidin.fps)

with vidin, vidout:
    for frame in vidin:
        cv2.imshow('image', frame)
        vidout.write(frame)

Read the camera.

# by device ID
cap = ffmpegcv.VideoCaptureCAM(0)
# by device name
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")

GPU Accelation

Support NVIDIA card only, test in x86_64 only.
Works in Windows, Linux and Anaconda.
Works in the Google Colab notebook.
Infeasible in the MacOS. That ffmpeg didn't supports NVIDIA at all.

* The ffmegcv GPU reader is a bit slower than CPU reader, but much faster when use ROI operations (crop, resize, pad).

Codecs

Codecs	OpenCV-reader	ffmpegcv-cpu-r	gpu-r	OpenCV-writer	ffmpegcv-cpu-w	gpu-w
h264	√	√	√	×	√	√
h265 (hevc)	not sure	√	√	×	√	√
mjpeg	√	√	×	√	√	×
mpeg	√	√	×	√	√	×
others	not sure	ffmpeg -decoders	×	not sure	ffmpeg -encoders	×

Benchmark

On the way...（maybe never）

Video Reader

The ffmpegcv is just similar to opencv in api.

# open cv
import cv2
cap = cv2.VideoCapture(file)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

# ffmpegcv
import ffmpegcv
cap = ffmpegcv.VideoCapture(file)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass
cap.release()

# alternative
cap = ffmpegcv.VideoCapture(file)
nframe = len(cap)
for frame in cap:
    pass
cap.release()

# more pythonic, recommand
with ffmpegcv.VideoCapture(file) as cap:
    nframe = len(cap)
    for iframe, frame in enumerate(cap):
        if iframe>100: break
        pass

Use GPU to accelerate decoding. It depends on the video codes. h264_nvcuvid, hevc_nvcuvid ....

cap_cpu = ffmpegcv.VideoCapture(file)
cap_gpu = ffmpegcv.VideoCapture(file, codec='h264_cuvid') #NVIDIA GPU0
cap_gpu0 = ffmpegcv.VideoCaptureNV(file)         #NVIDIA GPU0
cap_gpu1 = ffmpegcv.VideoCaptureNV(file, gpu=1)  #NVIDIA GPU1
cap_qsv = ffmpegcv.VideoCaptureQSV(file)         #Intel QSV, experimental

Use rgb24 instead of bgr24. The gray version would be more efficient.

cap = ffmpegcv.VideoCapture(file, pix_fmt='rgb24') #rgb24, bgr24, gray
ret, frame = cap.read()
plt.imshow(frame)

ROI Operations

You can crop, resize and pad the video. These ROI operation is ffmpegcv-GPU > ffmpegcv-CPU >> opencv.

Crop video, which will be much faster than read the whole canvas.

cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480))

Resize the video to the given size.

cap = ffmpegcv.VideoCapture(file, resize=(640, 480))

Resize and keep the aspect ratio with black border padding.

cap = ffmpegcv.VideoCapture(file, resize=(640, 480), resize_keepratio=True)

Crop and then resize the video.

cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480), resize=(512, 512))

Video Writer

# cv2
out = cv2.VideoWriter('outpy.avi',
                       cv2.VideoWriter_fourcc('M','J','P','G'), 
                       10, 
                       (w, h))
out.write(frame1)
out.write(frame2)
out.release()

# ffmpegcv, default codec is 'h264' in cpu 'h265' in gpu.
# frameSize is decided by the size of the first frame
out = ffmpegcv.VideoWriter('outpy.mp4', None, 10)
out.write(frame1)
out.write(frame2)
out.release()

# more pythonic
with ffmpegcv.VideoWriter('outpy.mp4', None, 10) as out:
    out.write(frame1)
    out.write(frame2)

Use GPU to accelerate encoding. Such as h264_nvenc, hevc_nvenc.

out_cpu = ffmpegcv.VideoWriter('outpy.mp4', None, 10)
out_gpu0 = ffmpegcv.VideoWriterNV('outpy.mp4', 'h264', 10)        #NVIDIA GPU0
out_gpu1 = ffmpegcv.VideoWriterNV('outpy.mp4', 'hevc', 10, gpu=1) #NVIDIA GPU1
out_qsv  = ffmpegcv.VideoWriterQSV('outpy.mp4', 'h264', 10)        #Intel QSV, experimental

Input image is rgb24 instead of bgr24

out = ffmpegcv.VideoWriter('outpy.mp4', None, 10, pix_fmt='rgb24')
out.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

Video Reader and Writer

import ffmpegcv
vfile_in = 'A.mp4'
vfile_out = 'A_h264.mp4'
vidin = ffmpegcv.VideoCapture(vfile_in)
vidout = ffmpegcv.VideoWriter(vfile_out, None, vidin.fps)

with vidin, vidout:
    for frame in vidin:
        vidout.write(frame)

Camera Reader

Experimental feature. The ffmpegcv offers Camera reader. Which is consistent with VideoFiler reader.

The VideoCaptureCAM aims to support ROI operations. The Opencv will be general fascinating than ffmpegcv in camera read. I recommand the opencv in most camera reading case.
The ffmpegcv can use name to retrieve the camera device. Use ffmpegcv.VideoCaptureCAM("Integrated Camera") is readable than cv2.VideoCaptureCAM(0).
The VideoCaptureCAM will be laggy and dropping frames if your post-process takes long time. The VideoCaptureCAM will buffer the recent frames.
The VideoCaptureCAM is continously working on background even if you didn't read it. Please release it in time.
Works perfect in Windows, not-perfect in Linux and macOS.

import cv2
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()

# ffmpegcv, in Windows&Linux
import ffmpegcv
cap = ffmpegcv.VideoCaptureCAM(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()

# ffmpegcv use by camera name, in Windows&Linux
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")

# ffmpegcv use camera path if multiple cameras conflict
cap = ffmpegcv.VideoCaptureCAM('@device_pnp_\\\\?\\usb#vid_2304&'
    'pid_oot#media#0001#{65e8773d-8f56-11d0-a3b9-00a0c9223196}'
    '\\global')

# ffmpegcv use camera with ROI operations
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera", crop_xywh=(0, 0, 640, 480), resize=(512, 512), resize_keepratio=True)

List all camera devices

from ffmpegcv.ffmpeg_reader_camera import query_camera_devices

devices = query_camera_devices()
print(devices)

{0: ('Integrated Camera', '@device_pnp_\\?\usb#vid_2304&pid_oot#media#0001#{65e8773d-8f56-11d0-a3b9-00a0c9223196}\global'),
1: ('OBS Virtual Camera', '@device_sw_{860BB310-5D01-11D0-BD3B-00A0C911CE86}\{A3FCE0F5-3493-419F-958A-ABA1250EC20B}')}

Set the camera resolution, fps, vcodec/pixel-format

from ffmpegcv.ffmpeg_reader_camera import query_camera_options

options = query_camera_options(0)  # or query_camera_options("Integrated Camera") 
print(options)
cap = ffmpegcv.VideoCaptureCAM(0, **options[-1])

[{'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (1280, 720), 'camfps': 60.0002}, {'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (640, 480), 'camfps': 60.0002}, {'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (1920, 1080), 'camfps': 60.0002}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (1280, 720), 'camfps': 10}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (640, 480), 'camfps': 30}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (1920, 1080), 'camfps': 5}]

Known issues

The VideoCaptureCAM didn't give a smooth experience in macOS. You must specify all the camera parameters. And the query_camera_options woun't give any suggestion. That's because the ffmpeg cannot list device options using mac native avfoundation.

# The macOS requires full argument.
cap = ffmpegcv.VideoCaptureCAM('FaceTime HD Camera', camsize_wh=(1280,720), camfps=30, campix_fmt='nv12')

The VideoCaptureCAM cann't list the FPS in linux. Because the ffmpeg cound't query the device's FPS using linux native v4l2 module. However, it's just OK to let the FPS empty.

Stream Reader

Experimental feature. The ffmpegcv offers Stream reader. Which is consistent with VideoFiler reader, and more similiar to the camera. Becareful when using it.

Support RTSP, RTP, RTMP, HTTP, HTTPS streams.
The VideoCaptureStream will be laggy and dropping frames if your post-process takes long time. The VideoCaptureCAM will buffer the recent frames.
The VideoCaptureStream is continously working on background even if you didn't read it. Please release it in time.
It's still experimental. Recommand you to use opencv.

# opencv
import cv2
stream_url = 'http://devimages.apple.com.edgekey.net/streaming/examples/bipbop_4x3/gear2/prog_index.m3u8'
cap = cv2.VideoCapture(stream_url, cv2.CAP_FFMPEG)

if not cap.isOpened():
    print('Cannot open the stream')
    exit(-1)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

# ffmpegcv
import ffmpegcv
cap = ffmpegcv.VideoCaptureStream(stream_url)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

Noblock

A proxy to automatic prepare frames in backgroud, which does not block when reading&writing current frame (multiprocessing). This make your python program more efficient in CPU usage. Up to 2x boost.

ffmpegcv.VideoCapture(*args) -> ffmpegcv.noblock(ffmpegcv.VideoCapture, *args)

ffmpegcv.VideoWriter(*args) -> ffmpegcv.noblock(ffmpegcv.VideoWriter, *args)

#Proxy any VideoCapture&VideoWriter args and kargs
vid_noblock = ffmpegcv.noblock(ffmpegcv.VideoCapture, vfile, pix_fmt='rbg24')

# this is fast
def cpu_tense(): time.sleep(0.01)
for _ in tqdm.trange(1000):
    ret, img = vid_noblock.read() #current img is already buffered, take no time
    cpu_tense()                   #meanwhile, the next img is buffering in background

# this is slow
vid = ffmpegcv.VideoCapture(vfile, pix_fmt='rbg24')
for _ in tqdm.trange(1000):
    ret, img = vid.read()         #this read will block cpu, take time
    cpu_tense()

folkien/ffmpegcv