chore: import upstream snapshot with attribution

2026-07-13 12:10:44 +08:00
commit e083d8f5d9
2876 changed files with 508589 additions and 0 deletions
@@ -0,0 +1,105 @@
+import io
+import logging
+from typing import Literal
+
+import numpy as np
+import requests
+from PIL import Image
+from pydantic import ConfigDict, Field
+
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "deepstack"
+
+
+class DeepstackDetectorConfig(BaseDetectorConfig):
+    """DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended."""
+
+    model_config = ConfigDict(
+        title="DeepStack",
+    )
+
+    type: Literal[DETECTOR_KEY]
+    api_url: str = Field(
+        default="http://localhost:80/v1/vision/detection",
+        title="DeepStack API URL",
+        description="The URL of the DeepStack API.",
+    )
+    api_timeout: float = Field(
+        default=0.1,
+        title="DeepStack API timeout (in seconds)",
+        description="Maximum time allowed for a DeepStack API request.",
+    )
+    api_key: str = Field(
+        default="",
+        title="DeepStack API key (if required)",
+        description="Optional API key for authenticated DeepStack services.",
+    )
+
+
+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.session = requests.Session()
+
+    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)
+        self.w, self.h = image.size
+        with io.BytesIO() as output:
+            image.save(output, format="JPEG")
+            image_bytes = output.getvalue()
+        data = {"api_key": self.api_key}
+
+        try:
+            response = self.session.post(
+                self.api_url,
+                data=data,
+                files={"image": image_bytes},
+                timeout=self.api_timeout,
+            )
+        except requests.exceptions.RequestException as ex:
+            logger.error("Error calling deepstack API: %s", ex)
+            return np.zeros((20, 6), np.float32)
+
+        response_json = response.json()
+        detections = np.zeros((20, 6), np.float32)
+        if response_json.get("predictions") is None:
+            logger.debug(f"Error in parsing response json: {response_json}")
+            return detections
+
+        for i, detection in enumerate(response_json.get("predictions")):
+            logger.debug(f"Response: {detection}")
+            if detection["confidence"] < 0.4:
+                logger.debug("Break due to confidence < 0.4")
+                break
+            label = self.get_label_index(detection["label"])
+            if label < 0:
+                logger.debug("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