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@ -31,6 +31,7 @@ Run the container with
docker run --rm \ docker run --rm \
-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \ -v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \ -v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
-v <path_to_config_dir>:/config:ro \
-p 5000:5000 \ -p 5000:5000 \
-e RTSP_URL='<rtsp_url>' \ -e RTSP_URL='<rtsp_url>' \
-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \ -e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \
@ -60,6 +61,19 @@ Example docker-compose:
DEBUG: "0" DEBUG: "0"
``` ```
Here is an example `REGIONS` env variable:
`350,0,300,5000,200,mask-0-300.bmp:400,350,250,2000,200,mask-350-250.bmp:400,750,250,2000,200,mask-750-250.bmp`
First region broken down (all are required):
- `350` - size of the square (350px by 350px)
- `0` - x coordinate of upper left corner (top left of image is 0,0)
- `300` - y coordinate of upper left corner (top left of image is 0,0)
- `5000` - minimum person bounding box size (width*height for bounding box of identified person)
- `200` - minimum number of changed pixels to trigger motion
- `mask-0-300.bmp` - a bmp file with the masked regions as pure black, must be the same size as the region
Mask files go in the `/config` directory.
Access the mjpeg stream at http://localhost:5000 Access the mjpeg stream at http://localhost:5000
## Integration with HomeAssistant ## Integration with HomeAssistant
@ -102,6 +116,7 @@ sensor:
- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about - [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
- [ ] Look into GPU accelerated decoding of RTSP stream - [ ] Look into GPU accelerated decoding of RTSP stream
- [ ] Send video over a socket and use JSMPEG - [ ] Send video over a socket and use JSMPEG
- [ ] Look into neural compute stick
## Building Tensorflow from source for CPU optimizations ## Building Tensorflow from source for CPU optimizations
https://www.tensorflow.org/install/source#docker_linux_builds https://www.tensorflow.org/install/source#docker_linux_builds