mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
import io
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import logging
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import numpy as np
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import requests
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from PIL import Image
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from pydantic import Field
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from typing_extensions import Literal
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "deepstack"
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class DeepstackDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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api_url: str = Field(
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default="http://localhost:80/v1/vision/detection", title="DeepStack API URL"
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)
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api_timeout: float = Field(default=0.1, title="DeepStack API timeout (in seconds)")
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api_key: str = Field(default="", title="DeepStack API key (if required)")
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class DeepStack(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: DeepstackDetectorConfig):
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self.api_url = detector_config.api_url
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self.api_timeout = detector_config.api_timeout
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self.api_key = detector_config.api_key
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self.labels = detector_config.model.merged_labelmap
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def get_label_index(self, label_value):
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if label_value.lower() == "truck":
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label_value = "car"
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for index, value in self.labels.items():
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if value == label_value.lower():
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return index
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return -1
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def detect_raw(self, tensor_input):
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image_data = np.squeeze(tensor_input).astype(np.uint8)
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image = Image.fromarray(image_data)
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self.w, self.h = image.size
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with io.BytesIO() as output:
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image.save(output, format="JPEG")
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image_bytes = output.getvalue()
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data = {"api_key": self.api_key}
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try:
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response = requests.post(
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self.api_url,
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data=data,
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files={"image": image_bytes},
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timeout=self.api_timeout,
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)
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except requests.exceptions.RequestException:
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logger.error("Error calling deepstack API")
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return np.zeros((20, 6), np.float32)
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response_json = response.json()
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detections = np.zeros((20, 6), np.float32)
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if response_json.get("predictions") is None:
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logger.debug(f"Error in parsing response json: {response_json}")
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return detections
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for i, detection in enumerate(response_json.get("predictions")):
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logger.debug(f"Response: {detection}")
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if detection["confidence"] < 0.4:
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logger.debug("Break due to confidence < 0.4")
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break
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label = self.get_label_index(detection["label"])
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if label < 0:
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logger.debug("Break due to unknown label")
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break
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detections[i] = [
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label,
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float(detection["confidence"]),
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detection["y_min"] / self.h,
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detection["x_min"] / self.w,
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detection["y_max"] / self.h,
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detection["x_max"] / self.w,
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]
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return detections
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