add toCUDA

chenxinfeng4 · Apr 5, 2024 · 130c07f · 130c07f
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diff --git a/README.md b/README.md
@@ -8,16 +8,18 @@
 
 English Version | [中文版本](./README_CN.md) | [Resume 开发者简历 陈昕枫](https://gitee.com/lilab/chenxinfeng-cv/blob/master/README.md)
 
-The ffmpegcv provide Video Reader and Video Witer with ffmpeg backbone, which are faster and powerful than cv2.
+The ffmpegcv provide Video Reader and Video Witer with ffmpeg backbone, which are faster and powerful than cv2. Integrating ffmpegcv into your deeplearning pipeline is very smooth.
 
 - 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 support **Stream reading** (IP Camera) in low latency.
-- The ffmpegcv can support ROI operations.You can **crop**, **resize** and **pad** the ROI.
+- The ffmpegcv supports much more video **codecs** v.s. open-cv.
+- The ffmpegcv supports **RGB** & BGR & GRAY format as you like.
+- The ffmpegcv supports fp32 CHW & HWC format short to cuda.
+- The ffmpegcv supports **Stream reading** (IP Camera) in low latency.
+- The ffmpegcv supports ROI operations.You can **crop**, **resize** and **pad** the ROI.
+- The ffmpegcv supports shortcut to CUDA memory copy.
 
-In all, ffmpegcv is just similar to opencv api. But is has more codecs and does't require opencv installed.
+In all, ffmpegcv is just similar to opencv api. But is has more codecs and does't require opencv installed. It's great for deeplearning pipeline.
 
 ## Functions:
 - `VideoWriter`: Write a video file.
@@ -28,6 +30,7 @@ In all, ffmpegcv is just similar to opencv api. But is has more codecs and does'
 - `VideoCaptureStream`: Read a RTP/RTSP/RTMP/HTTP stream.
 - `VideoCaptureStreamRT`: Read a RTSP stream (IP Camera) in real time low latency as possible.
 - `noblock`: Read/Write a video file in background using mulitprocssing.
+- `toCUDA`: Translate a video/stream as CHW/HWC-float32 format into CUDA device, >2x faster.
 
 ## Install
 You need to download ffmpeg before you can use ffmpegcv.
@@ -38,15 +41,16 @@ You need to download ffmpeg before you can use ffmpegcv.
  #1D. CONDA: conda install ffmpeg=6.0.0     #don't use the default 4.x.x version
  
  #2. python
- pip install ffmpegcv
+ pip install ffmpegcv                                      #stable verison
+ pip install git+https://github.com/chenxinfeng4/ffmpegcv  #latest verison
 ```
 
 ## 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.
-
+- You are interested in deeplearning pipeline.
 ## Basic example
 Read a video by CPU, and rewrite it by GPU.
 ```python
@@ -67,6 +71,17 @@ cap = ffmpegcv.VideoCaptureCAM(0)
 cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")
 ```
 
+Deeplearning pipeline.
+```python
+# video -> crop -> resize -> RGB -> CUDA:CHW float32 -> model
+cap = ffmpegcv.toCUDA(
+    ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12', resize=(W,H)),
+    tensor_format='CHW')
+
+for frame_CHW_cuda in cap:
+    frame_CHW_cuda = (frame_CHW_cuda - mean) / std
+    result = model(frame_CHW_cuda)
+```
 ## Cross platform
 
 The ffmpegcv is based on Python+FFmpeg, it can cross platform among `Windows, Linux, Mac, X86, Arm`systems.
@@ -172,6 +187,36 @@ cap = ffmpegcv.VideoCapture(file, resize=(640, 480), resize_keepratio=True)
 cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480), resize=(512, 512))
 ```
 
+## toCUDA device
+---
+The ffmpegcv can translate the video/stream from HWC-uint8 cpu to CHW-float32 in CUDA device. It significantly reduce your cpu load, and get >2x faster than your manually convertion.
+
+Prepare your environment. The cuda environment is required. The `pycuda` package is required. The `pytorch` package is non-essential.
+> nvcc --version      # check you've installed NVIDIA CUDA Compiler
+> pip install pycuda  # install the pycuda, make sure
+
+```python
+# Read a video file to CUDA device, original
+cap = ffmpegcv.VideoCaptureNV(file, pix_fmt='rgb24')
+ret, frame_HWC_CPU = cap.read()
+frame_CHW_CUDA = torch.from_numpy(frame_HWC_CPU).permute(2, 0, 1).cuda().contiguous().float()    # 120fps, 1200% CPU load
+
+# speed up
+cap = toCUDA(ffmpegcv.VideoCapture(file, pix_fmt='yuv420p')) #must, yuv420p for cpu codec
+cap = toCUDA(ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12'))  #must, nv12 for gpu codec
+
+ret, frame_CHW_pycuda = cap.read()     #380fps, 200% CPU load, [pycuda array]
+ret, frame_CHW_pycudamem = cap.read_cudamem()  #same as [pycuda mem_alloc]
+ret, frame_CHW_CUDA = cap.read_torch()  #same as [pytorch tensor]
+
+frame_CHW_pycuda[:] = (frame_CHW_pycuda - mean) / std  #normalize
+```
+
+Why `toCUDA` is faster in your deeplearning pipeline?
+> 1. The ffmpeg uses the cpu to convert video pix_fmt from original YUV to RGB24, which is slow. The ffmpegcv use the cuda to accelerate pix_fmt convertion.
+> 2. Use `yuv420p` or `nv12` can save the cpu load and reduce the memory copy from CPU to GPU.
+> 2. The ffmpeg stores the image as HWC format. The ffmpegcv can use HWC & CHW format to accelerate the video reading.
+
 ## Video Writer
 ---
 ```python

diff --git a/README_CN.md b/README_CN.md
@@ -8,7 +8,7 @@
 
 [English Version](./README.md) | 中文版本 | [Resume 开发者简历 陈昕枫](https://gitee.com/lilab/chenxinfeng-cv/blob/master/README.md)
 
-ffmpegcv提供了基于ffmpeg的视频读取器和视频编写器，比cv2更快和更强大。
+ffmpegcv提供了基于ffmpeg的视频读取器和视频编写器，比cv2更快和更强大。适合深度学习的视频处理。
 
 - ffmpegcv与open-cv具有**兼容**的API。
 - ffmpegcv可以使用**GPU加速**编码和解码。
@@ -17,6 +17,7 @@ ffmpegcv提供了基于ffmpeg的视频读取器和视频编写器，比cv2更快
 - ffmpegcv支持网络**流视频读取** (网线监控相机)。
 - ffmpegcv支持ROI（感兴趣区域）操作，可以对ROI进行**裁剪**、**调整大小**和**填充**。
 总之，ffmpegcv与opencv的API非常相似。但它具有更多的编码器，并且不需要安装opencv。
+- ffmpegcv支持导出图像帧到CUDA设备。
 
 ## 功能：
 - `VideoWriter`：写入视频文件。
@@ -27,6 +28,7 @@ ffmpegcv提供了基于ffmpeg的视频读取器和视频编写器，比cv2更快
 - `VideoCaptureStream`：读取RTP/RTSP/RTMP/HTTP流。
 - `VideoCaptureStreamRT`: 读取RTSP流 (网线监控相机)，实时、低延迟。
 - `noblock`：在后台读取视频文件（更快）,使用多进程。
+- `toCUDA`：将图像帧导出到CUDA设备，以 CHW/HWC-float32 格式存储，超过2倍性能提升。
 
 ## 安装
 在使用ffmpegcv之前，您需要下载`ffmpeg`。
@@ -37,7 +39,8 @@ ffmpegcv提供了基于ffmpeg的视频读取器和视频编写器，比cv2更快
  #1D. CONDA: conda install ffmpeg=6.0.0
  
  #2. python
- pip install ffmpegcv
+ pip install ffmpegcv                                      #stable verison
+ pip install git+https://github.com/chenxinfeng4/ffmpegcv  #latest verison
  ```
 
 ## 何时选择 `ffmpegcv` 而不是 `opencv`：
@@ -67,6 +70,18 @@ cap = ffmpegcv.VideoCaptureCAM(0)
 cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")
 ```
 
+深度学习流水线
+```python
+# video -> crop -> resize -> RGB -> CUDA:CHW float32 -> model
+cap = ffmpegcv.toCUDA(
+    ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12', resize=(W,H)),
+    tensor_format='CHW')
+
+for frame_CHW_cuda in cap:
+    frame_CHW_cuda = (frame_CHW_cuda - mean) / std
+    result = model(frame_CHW_cuda)
+```
+
 ## GPU加速
 - 仅支持NVIDIA显卡，在 x86_64 上测试。
 - 原生支持**Windows**, **Linux**, **Anaconda**。
@@ -167,6 +182,37 @@ cap = ffmpegcv.VideoCapture(file, resize=(640, 480), resize_keepratio=True)
 cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480), resize=(512, 512))
 ```
 
+## toCUDA 将图像帧快速导出到CUDA设备
+---
+ffmpegcv 可以将 HWC-uint8 cpu 中的视频/流转换为 CUDA 设备中的 CHW-float32。它可以显著减少你的 CPU 负载，并比你的手动转换快 2 倍以上。
+
+准备环境。你需要具备 cuda 环境，并且安装 pycuda 包。注意，pytorch 包是非必须的。 
+> nvcc --version # 检查你是否已经安装了 NVIDIA CUDA 编译器
+> pip install pycuda # 安装 pycuda
+
+```python
+# 读取视频到CUDA设备，加速前
+cap = ffmpegcv.VideoCaptureNV(file, pix_fmt='rgb24')
+ret, frame_HWC_CPU = cap.read()
+frame_CHW_CUDA = torch.from_numpy(frame_HWC_CPU).permute(2, 0, 1).cuda().contiguous().float()    # 120fps, 1200% CPU 使用率
+
+# 加速后
+cap = toCUDA(ffmpegcv.VideoCapture(file, pix_fmt='yuv420p')) #必须设置, yuv420p 针对 cpu
+cap = toCUDA(ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12'))  #必须设置,  nv12 针对 gpu
+
+ret, frame_CHW_pycuda = cap.read()     #380fps, 200% CPU load, [pycuda array]
+ret, frame_CHW_pycudamem = cap.read_cudamem()  #same as [pycuda mem_alloc]
+ret, frame_CHW_CUDA = cap.read_torch()  #same as [pytorch tensor]
+
+frame_CHW_pycuda[:] = (frame_CHW_pycuda - mean) / std  #归一化
+```
+
+为什么在深度学习流水线中使用 toCUDA 会更快？
+
+> 1. ffmpeg 使用 CPU 将视频像素格式从原始 YUV 转换为 RGB24，这个过程很慢。`toCUDA` 使用 cuda 加速像素格式转换。
+> 2. 使用 yuv420p 或 nv12 可以节省 CPU 负载并减少从 CPU 到 GPU 的内存复制。
+> 3. ffmpeg 将图像存储为 HWC 格式。ffmpegcv 可以使用 HWC 和 CHW 格式来加速视频存储。
+
 ## 视频写入器
 ---
 ```python

diff --git a/ffmpegcv/__init__.py b/ffmpegcv/__init__.py
@@ -517,3 +517,11 @@ def VideoCapturePannels(
     )
 
 VideoReaderPannels = VideoCapturePannels
+
+
+def toCUDA(vid:FFmpegReader, gpu:int=0, tensor_format:str='chw') -> FFmpegReader:
+    """
+    Convert frames to CUDA tensor float32 in 'chw' or 'hwc' format.
+    """
+    from ffmpegcv.ffmpeg_reader_cuda import FFmpegReaderCUDA
+    return FFmpegReaderCUDA(vid, gpu, tensor_format)