Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity

update readme

Browse Source
This commit is contained in:
blakeblackshear 2019-02-09 07:23:54 -06:00
parent 071fcf6ea2
commit 3e4256602f
1 changed files with 33 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
README.md
View File

@ -33,10 +33,38 @@ Access the mjpeg stream at http://localhost:5000
## Tips ## Tips
- Lower the framerate of the RTSP feed on the camera to what you want to reduce the CPU usage for capturing the feed - Lower the framerate of the RTSP feed on the camera to what you want to reduce the CPU usage for capturing the feed
- Use SSDLite models
## Future improvements ## Future improvements
- MQTT messages when detected objects change - [ ] Look for a subset of object types
- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected - [ ] Try and simplify the tensorflow model to just look for the objects we care about
- Parallel processing to increase FPS - [ ] MQTT messages when detected objects change
- Look into GPU accelerated decoding of RTSP stream - [ ] Implement basic motion detection with opencv and only look for objects in the regions with detected motion
- Send video over a socket and use JSMPEG - [ ] Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
- [x] Parallel processing to increase FPS
- [ ] Look into GPU accelerated decoding of RTSP stream
- [ ] Send video over a socket and use JSMPEG
## Building Tensorflow from source for CPU optimizations
https://www.tensorflow.org/install/source#docker_linux_builds
used `tensorflow/tensorflow:1.12.0-devel-py3`
## Optimizing the graph (cant say I saw much difference in CPU usage)
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#optimizing-for-deployment
```
docker run -it -v ${PWD}:/lab -v ${PWD}/../back_camera_model/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb:/frozen_inference_graph.pb:ro tensorflow/tensorflow:1.12.0-devel-py3 bash
bazel build tensorflow/tools/graph_transforms:transform_graph
bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
--in_graph=/frozen_inference_graph.pb \
--out_graph=/lab/optimized_inception_graph.pb \
--inputs='image_tensor' \
--outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
--transforms='
strip_unused_nodes(type=float, shape="1,300,300,3")
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms'
```