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https://github.com/blakeblackshear/frigate.git
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154 lines
6.5 KiB
Markdown
154 lines
6.5 KiB
Markdown
# Frigate - Realtime Object Detection for RTSP Cameras
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Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
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- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
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- Allows you to define specific regions (squares) in the image to look for motion/objects
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- Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles looking for objects when there is no motion
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- Object detection with Tensorflow runs in a separate process per region
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- Detected objects are placed on a shared mp.Queue and aggregated into a list of recently detected objects in a separate thread
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- A person score is calculated as the sum of all scores/5
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- Motion and object info is published over MQTT for integration into HomeAssistant or others
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- An endpoint is available to view an MJPEG stream for debugging
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## Example video
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You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
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[](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
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## Getting Started
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Build the container with
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```
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docker build -t frigate .
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```
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Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
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Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
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Run the container with
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```
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docker run --rm \
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-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
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-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
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-v <path_to_config_dir>:/config:ro \
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-p 5000:5000 \
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-e RTSP_URL='<rtsp_url>' \
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-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>' \
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-e MQTT_HOST='your.mqtthost.com' \
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-e MQTT_USER='username' \
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-e MQTT_PASS='password' \
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-e MQTT_TOPIC_PREFIX='cameras/1' \
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-e DEBUG='0' \
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frigate:latest
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```
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Example docker-compose:
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```
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frigate:
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container_name: frigate
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restart: unless-stopped
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image: frigate:latest
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volumes:
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- <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro
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- <path_to_labelmap.pbtext>:/label_map.pbtext:ro
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- <path_to_config>:/config
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ports:
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- "127.0.0.1:5000:5000"
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environment:
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RTSP_URL: "<rtsp_url>"
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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>"
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MQTT_HOST: "your.mqtthost.com"
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MQTT_USER: "username" #optional
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MQTT_PASS: "password" #optional
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MQTT_TOPIC_PREFIX: "cameras/1"
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DEBUG: "0"
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```
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Here is an example `REGIONS` env variable:
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`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`
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First region broken down (all are required):
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- `350` - size of the square (350px by 350px)
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- `0` - x coordinate of upper left corner (top left of image is 0,0)
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- `300` - y coordinate of upper left corner (top left of image is 0,0)
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- `5000` - minimum person bounding box size (width*height for bounding box of identified person)
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- `200` - minimum number of changed pixels to trigger motion
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- `mask-0-300.bmp` - a bmp file with the masked regions as pure black, must be the same size as the region
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Mask files go in the `/config` directory.
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Access the mjpeg stream at http://localhost:5000
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## Integration with HomeAssistant
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```
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camera:
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- name: Camera Last Person
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platform: generic
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still_image_url: http://<ip>:5000/best_person.jpg
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binary_sensor:
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- name: Camera Motion
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platform: mqtt
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state_topic: "cameras/1/motion"
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device_class: motion
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availability_topic: "cameras/1/available"
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sensor:
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- name: Camera Person Score
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platform: mqtt
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state_topic: "cameras/1/objects"
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value_template: '{{ value_json.person }}'
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unit_of_measurement: '%'
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availability_topic: "cameras/1/available"
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```
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## Tips
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- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
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- Use SSDLite models to reduce CPU usage
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## Future improvements
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- [x] Remove motion detection for now
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- [ ] Try running object detection in a thread rather than a process
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- [x] Implement min person size again
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- [ ] Switch to a config file
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- [ ] Handle multiple cameras in the same container
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- [ ] Simplify motion detection (check entire image against mask)
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- [ ] See if motion detection is even worth running
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- [ ] Scan for people across entire image rather than specfic regions
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- [ ] Dynamically resize detection area and follow people
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- [ ] Add ability to turn detection on and off via MQTT
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- [ ] MQTT motion occasionally gets stuck ON
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- [ ] Output movie clips of people for notifications, etc.
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- [ ] Integrate with homeassistant push camera
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- [ ] Merge bounding boxes that span multiple regions
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- [ ] Allow motion regions to be different than object detection regions
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- [ ] Implement mode to save labeled objects for training
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- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
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- [ ] Look into GPU accelerated decoding of RTSP stream
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- [ ] Send video over a socket and use JSMPEG
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- [x] Look into neural compute stick
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## Building Tensorflow from source for CPU optimizations
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https://www.tensorflow.org/install/source#docker_linux_builds
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used `tensorflow/tensorflow:1.12.0-devel-py3`
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## Optimizing the graph (cant say I saw much difference in CPU usage)
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https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#optimizing-for-deployment
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```
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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
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bazel build tensorflow/tools/graph_transforms:transform_graph
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bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
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--in_graph=/frozen_inference_graph.pb \
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--out_graph=/lab/optimized_inception_graph.pb \
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--inputs='image_tensor' \
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--outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
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--transforms='
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strip_unused_nodes(type=float, shape="1,300,300,3")
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remove_nodes(op=Identity, op=CheckNumerics)
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fold_constants(ignore_errors=true)
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fold_batch_norms
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fold_old_batch_norms'
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``` |