File size: 1,640 Bytes
14919b5 da92703 14919b5 5fb42c3 da92703 14919b5 fe75ca6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | from typing import Dict, List, Any
from ultralytics import YOLO
import os
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
self.model = YOLO(os.path.join(path, 'yolov8_2023-07-19_yolov8m.pt'))
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# Get the prediction
result = self.model(data['inputs'])
# Get the original image with channel shifted
img = result[0].orig_img[:,:,::-1]
H, W, _ = img.shape
annotated = img.copy()
# Modify crop so that it is square
try:
x1, y1, x2, y2 = result[0].boxes.xyxy.numpy().astype('int')[0]
if result[0].boxes.conf[0].item() < 0.75: # if low in confidence
x1, y1, x2, y2 = 0, 0, W, H
else:
annotated = result[0].plot(labels=False, conf=False)[:,:,::-1]
except: # in case there is no detection
x1, y1, x2, y2 = 0, 0, W, H
h, w = y2-y1, x2-x1
offset = abs(h-w) // 2
if h > w:
x1 = max(x1 - offset, 0)
x2 = min(x2 + offset, W)
else:
y1 = max(y1 - offset, 0)
y2 = min(y2 + offset, H)
new_image = img[y1:y2, x1:x2]
# Return the annotated original image with the square cropped
return annotated.tolist(), new_image.tolist()
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