sahi-yolox / utils.py
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