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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 |