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| import numpy | |
| import sahi.predict | |
| import sahi.utils | |
| from PIL import Image | |
| TEMP_DIR = "temp" | |
| def sahi_yolov8m_inference( | |
| image, | |
| detection_model, | |
| slice_height=512, | |
| slice_width=512, | |
| overlap_height_ratio=0.1, | |
| overlap_width_ratio=0.1, | |
| image_size=1024, | |
| postprocess_match_threshold=0.75, | |
| ): | |
| # sliced inference | |
| prediction_result = sahi.predict.get_sliced_prediction( | |
| image=image, | |
| detection_model=detection_model, | |
| image_size = image_size, | |
| slice_height=slice_height, | |
| slice_width=slice_width, | |
| overlap_height_ratio=overlap_height_ratio, | |
| overlap_width_ratio=overlap_width_ratio, | |
| postprocess_match_threshold=postprocess_match_threshold, | |
| ) | |
| visual_result = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result.object_prediction_list, | |
| ) | |
| output = Image.fromarray(visual_result["image"]) | |
| return output |