132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
# dds cloudapi for Grounding DINO 1.5
|
|
from dds_cloudapi_sdk import Config
|
|
from dds_cloudapi_sdk import Client
|
|
from dds_cloudapi_sdk import DetectionTask
|
|
from dds_cloudapi_sdk import TextPrompt
|
|
from dds_cloudapi_sdk import DetectionModel
|
|
from dds_cloudapi_sdk import DetectionTarget
|
|
|
|
import cv2
|
|
import torch
|
|
import numpy as np
|
|
import supervision as sv
|
|
from PIL import Image
|
|
from sam2.build_sam import build_sam2
|
|
from sam2.sam2_image_predictor import SAM2ImagePredictor
|
|
|
|
"""
|
|
Prompt Grounding DINO 1.5 with Text for Box Prompt Generation with Cloud API
|
|
"""
|
|
# Step 1: initialize the config
|
|
token = "Your API token"
|
|
config = Config(token)
|
|
|
|
# Step 2: initialize the client
|
|
client = Client(config)
|
|
|
|
# Step 3: run the task by DetectionTask class
|
|
# image_url = "https://algosplt.oss-cn-shenzhen.aliyuncs.com/test_files/tasks/detection/iron_man.jpg"
|
|
# if you are processing local image file, upload them to DDS server to get the image url
|
|
img_path = "notebooks/images/cars.jpg"
|
|
image_url = client.upload_file(img_path)
|
|
|
|
task = DetectionTask(
|
|
image_url=image_url,
|
|
prompts=[TextPrompt(text="car")],
|
|
targets=[DetectionTarget.BBox], # detect bbox
|
|
model=DetectionModel.GDino1_5_Pro, # detect with GroundingDino-1.5-Pro model
|
|
)
|
|
|
|
client.run_task(task)
|
|
result = task.result
|
|
|
|
objects = result.objects # the list of detected objects
|
|
|
|
|
|
input_boxes = []
|
|
confidences = []
|
|
class_names = []
|
|
|
|
for idx, obj in enumerate(objects):
|
|
input_boxes.append(obj.bbox)
|
|
confidences.append(obj.score)
|
|
class_names.append(obj.category)
|
|
|
|
input_boxes = np.array(input_boxes)
|
|
|
|
"""
|
|
Init SAM 2 Model and Predict Mask with Box Prompt
|
|
"""
|
|
|
|
# environment settings
|
|
# use bfloat16
|
|
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
|
|
|
|
if torch.cuda.get_device_properties(0).major >= 8:
|
|
# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
torch.backends.cudnn.allow_tf32 = True
|
|
|
|
# build SAM2 image predictor
|
|
sam2_checkpoint = "./checkpoints/sam2_hiera_large.pt"
|
|
model_cfg = "sam2_hiera_l.yaml"
|
|
sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
|
|
sam2_predictor = SAM2ImagePredictor(sam2_model)
|
|
|
|
image = Image.open(img_path)
|
|
|
|
sam2_predictor.set_image(np.array(image.convert("RGB")))
|
|
|
|
masks, scores, logits = sam2_predictor.predict(
|
|
point_coords=None,
|
|
point_labels=None,
|
|
box=input_boxes,
|
|
multimask_output=False,
|
|
)
|
|
|
|
|
|
"""
|
|
Post-process the output of the model to get the masks, scores, and logits for visualization
|
|
"""
|
|
# convert the shape to (n, H, W)
|
|
if masks.ndim == 3:
|
|
masks = masks[None]
|
|
scores = scores[None]
|
|
logits = logits[None]
|
|
elif masks.ndim == 4:
|
|
masks = masks.squeeze(1)
|
|
|
|
|
|
"""
|
|
Visualization the Predict Results
|
|
"""
|
|
|
|
class_ids = np.array(list(range(len(class_names))))
|
|
|
|
labels = [
|
|
f"{class_name} {confidence:.2f}"
|
|
for class_name, confidence
|
|
in zip(class_names, confidences)
|
|
]
|
|
|
|
"""
|
|
Visualize image with supervision useful API
|
|
"""
|
|
img = cv2.imread(img_path)
|
|
detections = sv.Detections(
|
|
xyxy=input_boxes, # (n, 4)
|
|
mask=masks.astype(bool), # (n, h, w)
|
|
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("groundingdino_annotated_image.jpg", annotated_frame)
|
|
|
|
mask_annotator = sv.MaskAnnotator()
|
|
annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
|
|
cv2.imwrite("grounded_sam2_annotated_image_with_mask.jpg", annotated_frame)
|