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https://github.com/blakeblackshear/frigate.git
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* Initial work for adding OpenVino detector. Not functional * Load model and submit for inference. Sucessfully load model and initialize OpenVino engine with either CPU or GPU as device. Does not parse results for objects. * Detection working with ssdlite_mobilenetv2 FP16 model * Add OpenVIno support and model to docker image * Add documentation for OpenVino detector configuration * Adds support for ARM32/ARM64 and the Myriad X hardware - Use custom-built openvino wheel for all platforms - Add libusb build without udev for NCS2 support * Add documentation around Intel CPU requirements and NCS2 setup * Print all available output tensors * Update documentation for config parameters
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import logging
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import numpy as np
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import openvino.runtime as ov
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from frigate.detectors.detection_api import DetectionApi
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logger = logging.getLogger(__name__)
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class OvDetector(DetectionApi):
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def __init__(self, det_device=None, model_config=None, num_threads=1):
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self.ov_core = ov.Core()
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self.ov_model = self.ov_core.read_model(model_config.path)
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self.interpreter = self.ov_core.compile_model(
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model=self.ov_model, device_name=det_device
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)
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logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
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self.output_indexes = 0
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while True:
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try:
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tensor_shape = self.interpreter.output(self.output_indexes).shape
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logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
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self.output_indexes += 1
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except:
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logger.info(f"Model has {self.output_indexes} Output Tensors")
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break
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def detect_raw(self, tensor_input):
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infer_request = self.interpreter.create_infer_request()
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infer_request.infer([tensor_input])
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results = infer_request.get_output_tensor()
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detections = np.zeros((20, 6), np.float32)
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i = 0
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for object_detected in results.data[0, 0, :]:
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if object_detected[0] != -1:
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logger.debug(object_detected)
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if object_detected[2] < 0.1 or i == 20:
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break
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detections[i] = [
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object_detected[1], # Label ID
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float(object_detected[2]), # Confidence
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object_detected[4], # y_min
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object_detected[3], # x_min
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object_detected[6], # y_max
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object_detected[5], # x_max
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]
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i += 1
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return detections
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