Add Deepstack/CodeProject-AI.Server detector plugin

docs/docs/configuration/detectors.md

@@ -256,3 +256,24 @@ model:
   width: 416
   height: 416
 ```
+
+
+## Deepstack / CodeProject AI.Server Detector
+The Deepstack/CodeProject AI.Server detector plugin for Frigate allows you to integrate Deeppstack or CodeProject.AI object detection capabilities into Frigate video surveillance system. CodeProject.AI and DeepStack is an open-source AI platforms that can be run on various devices, such as Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
+
+### Setup
+To get started with CodeProject.AI, visit their official website at [www.codeproject.com](https://www.codeproject.com/Articles/5322557/CodeProject-AI-Server-AI-the-easy-way) and follow the instructions to download and install the AI server on your preferred device. Detailed setup instructions for CodeProject.AI are outside the scope of the Frigate documentation.
+
+To integrate CodeProject.AI with Frigate, you'll need to make the following changes to your Frigate configuration file:
+
+```yaml
+detectors:
+  deepstack:
+    api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
+    type: deepstack
+    api_timeout: 0.1 # seconds
+```
+
+Replace `<your_codeproject_ai_server_ip>` and `<port>` with the IP address and port of your CodeProject.AI server. 
+
+To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly.

frigate/detectors/plugins/deepstack.py

@@ -0,0 +1,78 @@
+import logging
+import numpy as np
+import requests
+import io
+
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig
+from typing import Literal
+from pydantic import Extra, Field
+from PIL import Image
+
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "deepstack"
+
+
+class DeepstackDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+    api_url: str = Field(
+        default="http://localhost:80/v1/vision/detection", title="DeepStack API URL"
+    )
+    api_timeout: float = Field(default=0.1, title="DeepStack API timeout (in seconds)")
+    api_key: str = Field(default="", title="DeepStack API key (if required)")
+
+
+class DeepStack(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, detector_config: DeepstackDetectorConfig):
+        self.api_url = detector_config.api_url
+        self.api_timeout = detector_config.api_timeout
+        self.api_key = detector_config.api_key
+        self.labels = detector_config.model.merged_labelmap
+
+        self.h = detector_config.model.height
+        self.w = detector_config.model.width
+
+    def get_label_index(self, label_value):
+        if label_value.lower() == "truck":
+            label_value = "car"
+        for index, value in self.labels.items():
+            if value == label_value.lower():
+                return index
+        return -1
+
+    def detect_raw(self, tensor_input):
+        image_data = np.squeeze(tensor_input).astype(np.uint8)
+        image = Image.fromarray(image_data)
+        with io.BytesIO() as output:
+            image.save(output, format="JPEG")
+            image_bytes = output.getvalue()
+        data = {"api_key": self.api_key}
+        response = requests.post(
+            self.api_url, files={"image": image_bytes}, timeout=self.api_timeout
+        )
+        response_json = response.json()
+        detections = np.zeros((20, 6), np.float32)
+
+        for i, detection in enumerate(response_json["predictions"]):
+            logger.debug(f"Response: {detection}")
+            if detection["confidence"] < 0.4:
+                logger.debug(f"Break due to confidence < 0.4")
+                break
+            label = self.get_label_index(detection["label"])
+            if label < 0:
+                logger.debug(f"Break due to unknown label")
+                break
+            detections[i] = [
+                label,
+                float(detection["confidence"]),
+                detection["y_min"] / self.h,
+                detection["x_min"] / self.w,
+                detection["y_max"] / self.h,
+                detection["x_max"] / self.w,
+            ]
+
+        return detections