demo.py

# dds cloudapi for DINO-X
from dds_cloudapi_sdk import Config
from dds_cloudapi_sdk import Client
from dds_cloudapi_sdk.tasks.dinox import DinoxTask
from dds_cloudapi_sdk.tasks.detection import DetectionTask
from dds_cloudapi_sdk import TextPrompt
from dds_cloudapi_sdk import DetectionModel
from dds_cloudapi_sdk import DetectionTarget

# using supervision for visualization
import cv2
import numpy as np
import supervision as sv
import pycocotools.mask as mask_utils

"""
Hyper Parameters
"""
API_TOKEN = "Your API token"
IMG_PATH = "./assets/demo.png"
TEXT_PROMPT = "wheel . eye . helmet . mouse . mouth . vehicle . steering wheel . ear . nose"

"""
Prompting DINO-X with Text for Box and Mask Generation with Cloud API
"""

# Step 1: initialize the config
token = API_TOKEN
config = Config(token)

# Step 2: initialize the client
client = Client(config)

# Step 3: Run DINO-X task
# if you are processing local image file, upload them to DDS server to get the image url
image_url = client.upload_file(IMG_PATH)

task = DinoxTask(
    image_url=image_url,
    prompts=[TextPrompt(text=TEXT_PROMPT)]
)
client.run_task(task)
predictions = task.result.objects

"""
Visualization
"""

# decode the prediction results

classes = [x.strip().lower() for x in TEXT_PROMPT.split('.') if x]
class_name_to_id = {name: id for id, name in enumerate(classes)}
class_id_to_name = {id: name for name, id in class_name_to_id.items()}

boxes = []
confidences = []
class_names = []
class_ids = []

for idx, obj in enumerate(predictions):
    boxes.append(obj.bbox)
    confidences.append(obj.score)
    cls_name = obj.category.lower().strip()
    class_names.append(cls_name)
    class_ids.append(class_name_to_id[cls_name])

boxes = np.array(boxes)
class_ids = np.array(class_ids)
labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(class_names, confidences)
]

img = cv2.imread(IMG_PATH)
detections = sv.Detections(
    xyxy = boxes,
    class_id = class_ids
)

box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections)

label_annotator = sv.LabelAnnotator()
annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
cv2.imwrite("annotated_demo_image.jpg", annotated_frame)