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Project description
FFMPEGCV is an alternative to OPENCV for video read and write.
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 format as you like.
- The ffmpegcv can resize video to specific size with/without padding.
In all, ffmpegcv is just similar to opencv api. But is faster and with more codecs.
Basic example
Read a video by GPU, and rewrite it.
vidin = ffmpegcv.VideoCaptureNV(vfile_in)
vidout = ffmpegcv.VideoWriter(vfile_out, 'h264', vidin.fps)
with vidin, vidout:
for frame in vidin:
cv2.imshow('image', frame)
vidout.write(frame)
Install
You need to download ffmpeg before you can use ffmpegcv
conda install ffmpeg
pip install ffmpegcv
GPU Accelation
- Support NVIDIA card only.
- Perfect in the Windows. That ffmpeg supports NVIDIA acceleration just by conda install.
- Struggle in the Linux. That ffmpeg didn't orginally support NVIDIA accelerate. Please re-compile the ffmpeg by yourself. See the link
- Infeasible in the MacOS. That ffmpeg didn't supports NVIDIA at all.
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
Use rgb24 instead of bgr24
cap = ffmpegcv.VideoCapture(file, pix_fmt='rgb24')
ret, frame = cap.read()
plt.imshow(frame)
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.avi', None, 10)
out.write(frame1)
out.write(frame2)
out.release()
# more pythonic
with ffmpegcv.VideoWriter('outpy.avi', 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.avi', None, 10)
out_gpu0 = ffmpegcv.VideoWriterNV('outpy.avi', 'h264', 10) #NVIDIA GPU0
out_gpu1 = ffmpegcv.VideoWriterNV('outpy.avi', 'hevc', 10, gpu=1) #NVIDIA GPU1
Input image is rgb24 instead of bgr24
out = ffmpegcv.VideoWriter('outpy.avi', 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)
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