Initial support for rockchip boards (#8382)

* initial support for rockchip boards

* Apply suggestions from code review

apply requested changes

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* requested changes

* rewrite dockerfile

* adjust targets

* Update .github/workflows/ci.yml

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* Update docs/docs/configuration/object_detectors.md

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* Update docs/docs/configuration/object_detectors.md

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* add information to docs

* Update docs/docs/configuration/object_detectors.md

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* format rknn.py

* apply changes from isort and ruff

---------

Co-authored-by: MarcA711 <>
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

.github/workflows/ci.yml

@@ -79,6 +79,16 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
+      - name: Build and push RockChip build
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          platforms: linux/arm64
+          targets: rk
+          files: docker/rockchip/rk.hcl
+          set: |
+            rk.tags=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}-rk
+            *.cache-from=type=gha
   jetson_jp4_build:
     runs-on: ubuntu-latest
     name: Jetson Jetpack 4

CODEOWNERS

@@ -2,3 +2,5 @@
 /docker/tensorrt/ @madsciencetist @NateMeyer
 /docker/tensorrt/*arm64* @madsciencetist
 /docker/tensorrt/*jetson* @madsciencetist
+
+/docker/rockchip/ @MarcA711

docker/rockchip/Dockerfile

@@ -0,0 +1,24 @@
+# syntax=docker/dockerfile:1.6
+
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
+ARG DEBIAN_FRONTEND=noninteractive
+
+FROM wheels as rk-wheels
+COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
+COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
+RUN sed -i "/https/d" /requirements-wheels.txt
+RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
+
+FROM wget as rk-libs
+RUN wget -qO librknnrt.so https://github.com/MarcA711/rknpu2/raw/master/runtime/RK3588/Linux/librknn_api/aarch64/librknnrt.so
+
+FROM deps AS rk-deps
+ARG TARGETARCH
+
+RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \ 
+    pip3 install -U /deps/rk-wheels/*.whl 
+
+WORKDIR /opt/frigate/
+COPY --from=rootfs / /
+COPY --from=rk-libs /rootfs/librknnrt.so /usr/lib/
+COPY docker/rockchip/yolov8n-320x320.rknn /models/

docker/rockchip/requirements-wheels-rk.txt

@@ -0,0 +1,2 @@
+hide-warnings == 0.17
+rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/raw/master/rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.5.2-cp39-cp39-linux_aarch64.whl

docker/rockchip/rk.hcl

@@ -0,0 +1,34 @@
+target wget {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/arm64"]
+  target = "wget"
+}
+
+target wheels {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/arm64"]
+  target = "wheels"
+}
+
+target deps {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/arm64"]
+  target = "deps"
+}
+
+target rootfs {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/arm64"]
+  target = "rootfs"
+}
+
+target rk {
+  dockerfile = "docker/rockchip/Dockerfile"
+  contexts = {
+    wget = "target:wget",
+    wheels = "target:wheels",
+    deps = "target:deps",
+    rootfs = "target:rootfs"
+  }
+  platforms = ["linux/arm64"]
+}

docker/rockchip/rk.mk

@@ -0,0 +1,10 @@
+BOARDS += rk
+
+local-rk: version
+	docker buildx bake --load --file=docker/rockchip/rk.hcl --set rk.tags=frigate:latest-rk rk
+
+build-rk: version
+	docker buildx bake --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk
+
+push-rk: build-rk
+	docker buildx bake --push --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk

docs/docs/configuration/object_detectors.md

@@ -5,7 +5,7 @@ title: Object Detectors
 
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, and `rknn`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
 
 ## CPU Detector (not recommended)
 
@@ -291,3 +291,38 @@ To verify that the integration is working correctly, start Frigate and observe t
 
 
 # Community Supported Detectors
+
+## Rockchip RKNN-Toolkit-Lite2
+
+This detector is only available if one of the following Rockchip SoCs is used:
+- RK3566/RK3568
+- RK3588/RK3588S
+- RV1103/RV1106
+- RK3562
+
+These SoCs come with a NPU that will highly speed up detection.
+
+### Setup
+
+RKNN support is provided using the `-rk` suffix for the docker image. Moreover, privileged mode must be enabled by adding the `--privileged` flag to your docker run command or `privileged: true` to your `docker-compose.yml` file.
+
+### Configuration
+
+This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for one). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
+```yaml
+detectors:                            # required
+  rknn:                               # required
+    type: rknn                        # required
+
+model:                                # required
+  # path to .rknn model file
+  path: /models/yolov8n-320x320.rknn
+  # width and height of detection frames
+  width: 320
+  height: 320
+  # pixel format of detection frame
+  # default value is rgb but yolov models usually use bgr format
+  input_pixel_format: bgr             # required
+  # shape of detection frame
+  input_tensor: nhwc
+```

docs/docs/frigate/hardware.md

@@ -95,6 +95,16 @@ Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powe
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
+#### Rockchip SoC
+
+Frigate supports SBCs with the following Rockchip SoCs:
+- RK3566/RK3568
+- RK3588/RK3588S
+- RV1103/RV1106
+- RK3562
+
+Using the yolov8n model and an Orange Pi 5 Plus with RK3588 SoC inference speeds vary between 25-40 ms.
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -95,6 +95,7 @@ The following community supported builds are available:
 
 `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
 `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
+`ghcr.io/blakeblackshear/frigate:stable-rk` - Frigate build for SBCs with Rockchip SoC
 
 :::
 

frigate/detectors/plugins/rknn.py

@@ -0,0 +1,113 @@
+import logging
+from typing import Literal
+
+import cv2
+import cv2.dnn
+import numpy as np
+from hide_warnings import hide_warnings
+from pydantic import Field
+from rknnlite.api import RKNNLite
+
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "rknn"
+
+
+class RknnDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+    score_thresh: float = Field(
+        default=0.5, ge=0, le=1, title="Minimal confidence for detection."
+    )
+    nms_thresh: float = Field(
+        default=0.45, ge=0, le=1, title="IoU threshold for non-maximum suppression."
+    )
+
+
+class Rknn(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, config: RknnDetectorConfig):
+        self.height = config.model.height
+        self.width = config.model.width
+        self.score_thresh = config.score_thresh
+        self.nms_thresh = config.nms_thresh
+
+        self.model_path = config.model.path or "/models/yolov8n-320x320.rknn"
+
+        self.rknn = RKNNLite(verbose=False)
+        if self.rknn.load_rknn(self.model_path) != 0:
+            logger.error("Error initializing rknn model.")
+        if self.rknn.init_runtime() != 0:
+            logger.error("Error initializing rknn runtime.")
+
+    def __del__(self):
+        self.rknn.release()
+
+    def postprocess(self, results):
+        """
+        Processes yolov8 output.
+
+        Args:
+        results: array with shape: (1, 84, n, 1) where n depends on yolov8 model size (for 320x320 model n=2100)
+
+        Returns:
+        detections: array with shape (20, 6) with 20 rows of (class, confidence, y_min, x_min, y_max, x_max)
+        """
+
+        results = np.transpose(results[0, :, :, 0])  # array shape (2100, 84)
+        classes = np.argmax(
+            results[:, 4:], axis=1
+        )  # array shape (2100,); index of class with max confidence of each row
+        scores = np.max(
+            results[:, 4:], axis=1
+        )  # array shape (2100,); max confidence of each row
+
+        # array shape (2100, 4); bounding box of each row
+        boxes = np.transpose(
+            np.vstack(
+                (
+                    results[:, 0] - 0.5 * results[:, 2],
+                    results[:, 1] - 0.5 * results[:, 3],
+                    results[:, 2],
+                    results[:, 3],
+                )
+            )
+        )
+
+        # indices of rows with confidence > SCORE_THRESH with Non-maximum Suppression (NMS)
+        result_boxes = cv2.dnn.NMSBoxes(
+            boxes, scores, self.score_thresh, self.nms_thresh, 0.5
+        )
+
+        detections = np.zeros((20, 6), np.float32)
+
+        for i in range(len(result_boxes)):
+            if i >= 20:
+                break
+
+            index = result_boxes[i]
+            detections[i] = [
+                classes[index],
+                scores[index],
+                (boxes[index][1]) / self.height,
+                (boxes[index][0]) / self.width,
+                (boxes[index][1] + boxes[index][3]) / self.height,
+                (boxes[index][0] + boxes[index][2]) / self.width,
+            ]
+
+        return detections
+
+    @hide_warnings
+    def inference(self, tensor_input):
+        return self.rknn.inference(inputs=tensor_input)
+
+    def detect_raw(self, tensor_input):
+        output = self.inference(
+            [
+                tensor_input,
+            ]
+        )
+        return self.postprocess(output[0])