how to get the annotated frame from sahi results?

[fatemehprhm]

Hi, im new in your wonderful open source library, so i have this problem when im trying to implement sahi in my code.
i've been using yolov8 and get its results like this:

self.rgb_image = bridge.imgmsg_to_cv2(msg, "bgr8")
self.model = YOLO('yolov8n.pt')
results = self.model(self.rgb_image, verbose=False)
annotated_frame = results[0].plot()
img_msg = bridge.cv2_to_imgmsg(annotated_frame) 
self.img_pub.publish(img_msg)
for r in results:
    boxes = r.boxes
    for box in boxes:
        self.inference_result = InferenceResult()
        b = box.xyxy[0].to('cpu').detach().numpy().copy()  # get box coordinates in (top, left, bottom, right) format
        distance = self.measure_distance(b)
        c = box.cls
        self.inference_result.class_name = self.model.names[int(c)]
        self.inference_result.left = int(b[0])
        self.inference_result.top = int(b[1])
        self.inference_result.right = int(b[2])
        self.inference_result.bottom = int(b[3])

is there anyway possible to use sahi in this process?
i implemented the code bellow but i dont know how should i get the annoted_frame in real time without a need to export it.

self.detection_model = AutoDetectionModel.from_pretrained(
        model_type='yolov8',
        model_path=yolov8_model_path,
        confidence_threshold=0.3,
        device="cpu", # or 'cuda:0'
        )
results = get_sliced_prediction(
            self.rgb_image,
            self.detection_model,
            slice_height = 256,
            slice_width = 256,
            overlap_height_ratio = 0.2,
            overlap_width_ratio = 0.2
        )
for r in results:
            bbox = r['bbox']
            self.inference_result = InferenceResult()
            distance = self.measure_distance(bbox)
            self.inference_result.class_name = r['category_name']
            self.inference_result.left = int(bbox[0])
            self.inference_result.top = int(bbox[1])
            self.inference_result.right = int(bbox[2]) + int(bbox[0])
            self.inference_result.bottom = int(bbox[3]) + int(bbox[1])
            self.inference_result.distance = distance
            self.yolov8_inference.yolov8_inference.append(self.inference_result)
            print(f'Distance from {self.model.names[int(c)]}: {distance}')

Many thanks

[fatemehprhm]

I managed to solve it by cv library of sahi as below:

from sahi.utils.cv import visualize_object_predictions,read_image_as_pil
import numpy as np
image = read_image_as_pil(results.image)

object_predictions = visualize_object_predictions(
    image=np.ascontiguousarray(image),
    object_prediction_list=results.object_prediction_list,
    rect_th=None,
    text_size=None,
    text_th=None,
    color=None,
    hide_labels=False,
    hide_conf=False,      
    export_format="png",
)
image = object_predictions["image"]
img_msg = bridge.cv2_to_imgmsg(image)  
self.img_pub.publish(img_msg)