|
@@ -33,10 +33,38 @@ Access the mjpeg stream at http://localhost:5000
|
|
|
|
|
|
## Tips
|
|
|
- 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
|
|
|
-- MQTT messages when detected objects change
|
|
|
-- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
|
|
|
-- Parallel processing to increase FPS
|
|
|
-- Look into GPU accelerated decoding of RTSP stream
|
|
|
-- Send video over a socket and use JSMPEG
|
|
|
+- [ ] Look for a subset of object types
|
|
|
+- [ ] Try and simplify the tensorflow model to just look for the objects we care about
|
|
|
+- [ ] MQTT messages when detected objects change
|
|
|
+- [ ] Implement basic motion detection with opencv and only look for objects in the regions with detected motion
|
|
|
+- [ ] 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'
|
|
|
+```
|