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| import numpy | |
| import sahi.predict | |
| import sahi.utils | |
| from PIL import Image | |
| TEMP_DIR = "temp" | |
| def sahi_mmdet_inference( | |
| image, | |
| detection_model, | |
| slice_height=512, | |
| slice_width=512, | |
| overlap_height_ratio=0.2, | |
| overlap_width_ratio=0.2, | |
| image_size=640, | |
| postprocess_type="GREEDYNMM", | |
| postprocess_match_metric="IOS", | |
| postprocess_match_threshold=0.5, | |
| postprocess_class_agnostic=False, | |
| ): | |
| # standard inference | |
| detection_model.image_size = image_size | |
| prediction_result_1 = sahi.predict.get_prediction( | |
| image=image, detection_model=detection_model | |
| ) | |
| visual_result_1 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_1.object_prediction_list, | |
| ) | |
| output_1 = Image.fromarray(visual_result_1["image"]) | |
| # sliced inference | |
| prediction_result_2 = sahi.predict.get_sliced_prediction( | |
| image=image, | |
| detection_model=detection_model, | |
| slice_height=slice_height, | |
| slice_width=slice_width, | |
| overlap_height_ratio=overlap_height_ratio, | |
| overlap_width_ratio=overlap_width_ratio, | |
| postprocess_type=postprocess_type, | |
| postprocess_match_metric=postprocess_match_metric, | |
| postprocess_match_threshold=postprocess_match_threshold, | |
| postprocess_class_agnostic=postprocess_class_agnostic, | |
| ) | |
| visual_result_2 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_2.object_prediction_list, | |
| ) | |
| output_2 = Image.fromarray(visual_result_2["image"]) | |
| return output_1, output_2 | |