generated from dopt-python/py311
src/dopt_sensor_anomalies/detection.py aktualisiert
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@ -141,9 +141,9 @@ def measure_length(
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)
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x_min = max(min(np.min(c[:, 0, 0]) for c in filtered_cnts) - 20, 0)
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x_max = max(np.max(c[:, 0, 0]) for c in filtered_cnts) + 20
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x_max = min(max(np.max(c[:, 0, 0]) for c in filtered_cnts) + 20, orig.shape[1])
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y_min = max(min(np.min(c[:, 0, 1]) for c in filtered_cnts) - 20, 0)
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y_max = max(np.max(c[:, 0, 1]) for c in filtered_cnts) + 20
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y_max = min(max(np.max(c[:, 0, 1]) for c in filtered_cnts) + 20, orig.shape[0])
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rightmost_x_third = max(filtered_cnts[2][:, 0, 0])
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leftmost_x_fourth = min(filtered_cnts[3][:, 0, 0])
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@ -176,7 +176,7 @@ def infer_image(
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output = model(input_tensor)
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anomaly_score = output.pred_score.item()
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anomaly_label = output.pred_label.item()
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anomaly_label = 1 if anomaly_score >= .2 else 0
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anomaly_map = output.anomaly_map.squeeze().cpu().numpy()
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img_np = np.array(pil_image)
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