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import base64
from io import BytesIO
import os
from typing import Dict, List, Any
import cv2
import groundingdino
from groundingdino.util.inference import load_model, load_image, predict, annotate
import tempfile
# /app
HOME = os.getcwd()
# /opt/conda/lib/python3.9/site-packages/groundingdino
PACKAGE_HOME = os.path.dirname(groundingdino.__file__)
CONFIG_PATH = os.path.join(PACKAGE_HOME, "config", "GroundingDINO_SwinT_OGC.py")
class EndpointHandler():
def __init__(self, path):
# Preload all the elements you are going to need at inference.
self.model = load_model(CONFIG_PATH, os.path.join(path, "weights", "groundingdino_swint_ogc.pth"))
self.box_threshold = 0.35
self.text_threshold = 0.25
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
"""
inputs = data.pop("inputs")
image_base64 = inputs.pop("image")
prompt = inputs.pop("prompt")
image_data = base64.b64decode(image_base64)
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=True) as f:
f.write(image_data)
image_source, image = load_image(f.name)
boxes, logits, phrases = predict(
model=self.model,
image=image,
caption=prompt,
box_threshold=self.box_threshold,
text_threshold=self.text_threshold
)
annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)
_, annotated_image = cv2.imencode(".jpg", annotated_frame)
annotated_image_b64 = base64.b64encode(annotated_image).decode("utf-8")
num_found = boxes.size(0)
return [{
"image": annotated_image_b64,
"prompt": prompt,
"num_found": num_found,
}]
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