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ffmpegcv-0.2.8
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ffmpegcv-0.2.8 |
| نام | ffmpegcv |
| نسخه کتابخانه | 0.2.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | chenxf |
| ایمیل نویسنده | cxf529125853@163.com |
| آدرس صفحه اصلی | https://pypi.org/project/ffmpegcv/ |
| آدرس اینترنتی | https://pypi.org/project/ffmpegcv/ |
| مجوز | - |
# 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 support ROI operations.You can **crop**, **resize** and **pad** the ROI.
In all, ffmpegcv is just similar to opencv api. But is faster* and with more codecs.
## Basic example
Read a video by CPU, and rewrite it by GPU.
```python
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)
```
## 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](https://docs.nvidia.com/video-technologies/video-codec-sdk/ffmpeg-with-nvidia-gpu/)
- Works in the **Google Colab** notebook without pain (no need to recompile ffmpeg).
- 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...*
## Video Reader
---
The ffmpegcv is just similar to opencv in api.
```python
# 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 ....
```python
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
```python
cap = ffmpegcv.VideoCapture(file, pix_fmt='rgb24')
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.
```python
cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480))
```
**Resize** the video to the given size.
```python
cap = ffmpegcv.VideoCapture(file, resize=(640, 480))
```
**Resize** and keep the aspect ratio with black border **padding**.
```python
cap = ffmpegcv.VideoCapture(file, resize=(640, 480), resize_keepratio=True)
```
**Crop** and then **resize** the video.
```python
cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480), resize=(512, 512))
```
## Video Writer
---
```python
# 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.
```python
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
```python
out = ffmpegcv.VideoWriter('outpy.avi', None, 10, pix_fmt='rgb24')
out.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
```
## Video Reader and Writer
---
```python
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)
```
نحوه نصب
نصب پکیج whl ffmpegcv-0.2.8:
pip install ffmpegcv-0.2.8.whl
نصب پکیج tar.gz ffmpegcv-0.2.8:
pip install ffmpegcv-0.2.8.tar.gz