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Detection working with ssdlite_mobilenetv2 FP16 model
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@ -9,49 +9,38 @@ logger = logging.getLogger(__name__)
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class OvDetector(DetectionApi):
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def __init__(self, det_device=None, model_path=None, num_threads=1):
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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_path)
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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().shape}")
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logger.info(f"Model Output Shape: {self.interpreter.output().shape}")
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logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
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logger.info(f"Model Output Shape: {self.interpreter.output(0).shape}")
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def detect_raw(self, tensor_input):
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tensor_transpose = np.reshape(tensor_input, self.interpreter.input().shape)
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infer_request = self.interpreter.create_infer_request()
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results = infer_request.infer([tensor_transpose])
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infer_request.infer([tensor_input])
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# class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
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# scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
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# count = int(
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# self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
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# # )
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# class_ids = results[0, 0, :, 1]
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# # class_ids = [0, 0, 1, 1]
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# print(class_ids)
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# scores = results
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# print(scores)
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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["detection_out"][0, 0, :]:
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# if object_detected[2] < 0.1 or i == 20:
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# break
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# detections.append(
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# [
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# object_detected[1],
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# float(object_detected[2]),
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# object_detected[3],
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# object_detected[4],
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# object_detected[5],
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# object_detected[6],
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# ]
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# )
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# i += 1
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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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