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from ultralyticsplus import YOLO
from typing import List, Dict, Any
from sahi import ObjectPrediction
DEFAULT_CONFIG = {'conf': 0.25, 'iou': 0.45, 'agnostic_nms': False, 'max_det': 1000}
class EndpointHandler():
def __init__(self, path=""):
self.model = YOLO('ultralyticsplus/yolov8s')
def __call__(self, data: str) -> List[ObjectPrediction]:
"""
data args:
image: image path to segment
config: (conf - NMS confidence threshold,
iou - NMS IoU threshold,
agnostic_nms - NMS class-agnostic: True / False,
max_det - maximum number of detections per image)
Return:
object_predictions
"""
config = DEFAULT_CONFIG
# Set model parameters
self.model.overrides['conf'] = config.get('conf')
self.model.overrides['iou'] = config.get('iou')
self.model.overrides['agnostic_nms'] = config.get('agnostic_nms')
self.model.overrides['max_det'] = config.get('max_det')
# perform inference
inputs = data.pop("inputs", data)
result = self.model.predict(inputs['image'])[0]
names = self.model.model.names
boxes = result.boxes
object_predictions = []
if boxes is not None:
det_ind = 0
for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
object_prediction = ObjectPrediction(
bbox=xyxy.tolist(),
category_name=names[int(cls)],
category_id=int(cls),
score=conf,
)
object_predictions.append(object_prediction)
det_ind += 1
return object_predictions
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