update docs and add back benchmark

README.md

@@ -1,14 +1,13 @@
 # Frigate - Realtime Object Detection for IP Cameras
-**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
-
 Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
 
-- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
-- Allows you to define specific regions (squares) in the image to look for objects
-- No motion detection (for now)
-- Object detection with Tensorflow runs in a separate thread
+Use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
+
+- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
+- Uses a very low overhead motion detection to determine where to run object detection
+- Object detection with Tensorflow runs in a separate process
 - Object info is published over MQTT for integration into HomeAssistant as a binary sensor
-- An endpoint is available to view an MJPEG stream for debugging
+- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
 
 ![Diagram](diagram.png)
 
@@ -22,12 +21,16 @@ Build the container with
 docker build -t frigate .
 ```
 
-The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
+Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+- CPU Model: `/cpu_model.tflite`
+- EdgeTPU Model: `/edgetpu_model.tflite`
+- Labels: `/labelmap.txt`
 
 Run the container with
-```
+```bash
 docker run --rm \
 --privileged \
+--shm-size=512m \ # should work for a 2-3 cameras
 -v /dev/bus/usb:/dev/bus/usb \
 -v <path_to_config_dir>:/config:ro \
 -v /etc/localtime:/etc/localtime:ro \
@@ -37,11 +40,12 @@ frigate:latest
 ```
 
 Example docker-compose:
-```
+```yaml
   frigate:
     container_name: frigate
     restart: unless-stopped
     privileged: true
+    shm_size: '1g' # should work for 5-7 cameras
     image: frigate:latest
     volumes:
       - /dev/bus/usb:/dev/bus/usb
@@ -57,6 +61,8 @@ A `config.yml` file must exist in the `config` directory. See example [here](con
 
 Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best snapshot for any object type with at `http://localhost:5000/<camera_name>/<object_name>/best.jpg`
 
+Debug info is available at `http://localhost:5000/debug/stats`
+
 ## Integration with HomeAssistant
 ```
 camera:
@@ -93,30 +99,34 @@ automation:
             photo:
               - url: http://<ip>:5000/<camera_name>/person/best.jpg
                 caption: A person was detected.
+
+sensor:
+  - platform: rest
+    name: Frigate Debug
+    resource: http://localhost:5000/debug/stats
+    scan_interval: 5
+    json_attributes:
+      - back
+      - coral
+    value_template: 'OK'  
+  - platform: template
+    sensors:
+      back_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["fps"] }}'
+        unit_of_measurement: 'FPS'
+      back_skipped_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["skipped_fps"] }}'
+        unit_of_measurement: 'FPS'
+      back_detection_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["detection_fps"] }}'
+        unit_of_measurement: 'FPS'
+      frigate_coral_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["fps"] }}'
+        unit_of_measurement: 'FPS'
+      frigate_coral_inference:
+        value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["inference_speed"] }}' 
+        unit_of_measurement: 'ms'
 ```
 
 ## Tips
 - Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed
-
-## Future improvements
-- [x] Remove motion detection for now
-- [x] Try running object detection in a thread rather than a process
-- [x] Implement min person size again
-- [x] Switch to a config file
-- [x] Handle multiple cameras in the same container
-- [ ] Attempt to figure out coral symlinking
-- [ ] Add object list to config with min scores for mqtt
-- [ ] Move mjpeg encoding to a separate process
-- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
-- [ ] See if motion detection is even worth running
-- [ ] Scan for people across entire image rather than specfic regions
-- [ ] Dynamically resize detection area and follow people
-- [ ] Add ability to turn detection on and off via MQTT
-- [ ] Output movie clips of people for notifications, etc.
-- [ ] Integrate with homeassistant push camera
-- [ ] Merge bounding boxes that span multiple regions
-- [ ] Implement mode to save labeled objects for training
-- [ ] 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
-- [ ] Send video over a socket and use JSMPEG
-- [x] Look into neural compute stick