[Update] Add synaptics ci workflow

.github/workflows/ci.yml

@@ -173,6 +173,31 @@ jobs:
           set: |
             rk.tags=${{ steps.setup.outputs.image-name }}-rk
             *.cache-from=type=gha
+  synaptics_build:
+    runs-on: ubuntu-22.04-arm
+    name: Synaptics Build
+    needs:
+      - arm64_build
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v5
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push Synaptics build
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: synaptics
+          files: docker/synaptics/synaptics.hcl
+          set: |
+            synaptics.tags=${{ steps.setup.outputs.image-name }}-synaptics
+            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:

frigate/detectors/plugins/synaptics.py

@@ -1,16 +1,19 @@
-import os
 import logging
-
-from typing_extensions import Literal
+import os
 
 import numpy as np
 from synap import Network
-from synap.types import Shape, Layout
-from synap.preprocessor import Preprocessor
 from synap.postprocessor import Detector
+from synap.preprocessor import Preprocessor
+from synap.types import Layout, Shape
+from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum, InputTensorEnum
+from frigate.detectors.detector_config import (
+    BaseDetectorConfig,
+    InputTensorEnum,
+    ModelTypeEnum,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -20,6 +23,7 @@ DETECTOR_KEY = "synaptics"
 class SynapDetectorConfig(BaseDetectorConfig):
     type: Literal[DETECTOR_KEY]
 
+
 class SynapDetector(DetectionApi):
     type_key = DETECTOR_KEY
 
@@ -37,7 +41,7 @@ class SynapDetector(DetectionApi):
         except Exception as e:
             logger.error(f"Failed to init Synap NPU: {e}")
             raise
-        
+
         self.width = detector_config.model.width
         self.height = detector_config.model.height
         self.model_type = detector_config.model.model_type
@@ -52,28 +56,28 @@ class SynapDetector(DetectionApi):
     def detect_raw(self, tensor_input: np.ndarray):
         # It has only been testing for pre-converted mobilenet80 .tflite -> .synap model currently
         layout = Layout.nhwc  # default layout
+        detections = np.zeros((20, 6), np.float32)
+
         if self.input_tensor_layout == InputTensorEnum.nhwc:
             layout = Layout.nhwc
-        
-        postprocess_data = self.preprocessor.assign(self.network.inputs, tensor_input, Shape(tensor_input.shape), layout)
+
+        postprocess_data = self.preprocessor.assign(
+            self.network.inputs, tensor_input, Shape(tensor_input.shape), layout
+        )
         output_tensor_obj = self.network.predict()
         output = self.detector.process(output_tensor_obj, postprocess_data)
 
         if self.model_type == ModelTypeEnum.ssd:
-            detections = np.zeros((20, 6), np.float32)
-
             for i, item in enumerate(output.items):
                 if i == 20:
                     break
 
                 bb = item.bounding_box
-                
                 # Convert corner coordinates to normalized [0,1] range
                 x1 = bb.origin.x / self.width  # Top-left X
                 y1 = bb.origin.y / self.height  # Top-left Y
                 x2 = (bb.origin.x + bb.size.x) / self.width  # Bottom-right X
                 y2 = (bb.origin.y + bb.size.y) / self.height  # Bottom-right Y
-                                
                 detections[i] = [
                     item.class_index,
                     float(item.confidence),
@@ -82,9 +86,6 @@ class SynapDetector(DetectionApi):
                     y2,
                     x2,
                 ]
-                
-            return detections
         else:
-            print(f"Unsupported model type: {self.model_type}")
-            return np.zeros((20, 6), np.float32)
-
+            logger.error(f"Unsupported model type: {self.model_type}")
+        return detections