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Changes to pass lint checks
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@ -140,12 +140,13 @@ class OvDetector(DetectionApi):
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scores = np.max(output_data[:, 4:], axis=1)
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scores = np.max(output_data[:, 4:], axis=1)
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if len(scores) == 0:
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if len(scores) == 0:
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return np.zeros((20, 6), np.float32)
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return np.zeros((20, 6), np.float32)
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scores = np.expand_dims(scores, axis=1)
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scores = np.expand_dims(scores, axis=1)
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dets = np.concatenate((output_data, scores), axis=1) # add scores to the last column
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# add scores to the last column
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dets = dets[dets[:,-1] > 0.5,:] # filter out lines with scores below threshold
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dets = np.concatenate((output_data, scores), axis=1)
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# filter out lines with scores below threshold
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ordered = dets[dets[:, -1].argsort()[::-1]][:20] # limit to top 20 scores, descending order
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dets = dets[dets[:, -1] > 0.3, :]
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# limit to top 20 scores, descending order
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ordered = dets[dets[:, -1].argsort()[::-1]][:20]
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detections = np.zeros((20, 6), np.float32)
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detections = np.zeros((20, 6), np.float32)
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i = 0
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i = 0
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@ -170,9 +171,11 @@ class OvDetector(DetectionApi):
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elif self.ov_model_type == ModelTypeEnum.yolov5:
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elif self.ov_model_type == ModelTypeEnum.yolov5:
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out_tensor = infer_request.get_output_tensor()
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out_tensor = infer_request.get_output_tensor()
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output_data = out_tensor.data[0]
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output_data = out_tensor.data[0]
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conf_mask = (output_data[:, 4] >= 0.5).squeeze()
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# filter out lines with scores below threshold
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conf_mask = (output_data[:, 4] >= 0.3).squeeze()
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output_data = output_data[conf_mask]
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output_data = output_data[conf_mask]
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ordered = output_data[output_data[:, 4].argsort()[::-1]][:20] # limit to top 20 scores, descending order
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# limit to top 20 scores, descending order
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ordered = output_data[output_data[:, 4].argsort()[::-1]][:20]
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detections = np.zeros((20, 6), np.float32)
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detections = np.zeros((20, 6), np.float32)
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i = 0
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i = 0
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