quoccuongle/Grounded-SAM-2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update DINO-X api usage to dds v2

Browse Source
This commit is contained in:
rentainhe
2025-04-20 00:38:38 +08:00
parent 8238557f52
commit 3c5a4136d4
1 changed files with 34 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

57
grounded_sam2_dinox_demo.py
View File

@@ -1,9 +1,7 @@
# dds cloudapi for Grounding DINO 1.5
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.types import DetectionTarget
from dds_cloudapi_sdk import TextPrompt
from dds_cloudapi_sdk.tasks.v2_task import V2Task
import os
import cv2
@@ -27,6 +25,7 @@ IMG_PATH = "notebooks/images/cars.jpg"
SAM2_CHECKPOINT = "./checkpoints/sam2.1_hiera_large.pt"
SAM2_MODEL_CONFIG = "configs/sam2.1/sam2.1_hiera_l.yaml"
BOX_THRESHOLD = 0.2
IOU_THRESHOLD = 0.8
WITH_SLICE_INFERENCE = False
SLICE_WH = (480, 480)
OVERLAP_RATIO = (0.2, 0.2)
@@ -48,7 +47,7 @@ config = Config(token)
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"
# infer_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
classes = [x.strip().lower() for x in TEXT_PROMPT.split('.') if x]
@@ -62,13 +61,18 @@ if WITH_SLICE_INFERENCE:
with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmpfile:
temp_filename = tmpfile.name
cv2.imwrite(temp_filename, image_slice)
image_url = client.upload_file(temp_filename)
task = DinoxTask(
image_url=image_url,
prompts=[TextPrompt(text=TEXT_PROMPT)],
bbox_threshold=0.25,
targets=[DetectionTarget.BBox],
)
infer_image_url = client.upload_file(temp_filename)
task = V2Task(api_path="/v2/task/dinox/detection", api_body={
"model": "DINO-X-1.0",
"image": infer_image_url,
"prompt": {
"type":"text",
"text":TEXT_PROMPT
},
"targets": ["bbox", "mask"],
"bbox_threshold": BOX_THRESHOLD,
"iou_threshold": IOU_THRESHOLD,
})
client.run_task(task)
result = task.result
# detele the tempfile
@@ -77,7 +81,7 @@ if WITH_SLICE_INFERENCE:
input_boxes = []
confidences = []
class_ids = []
objects = result.objects
objects = result["objects"]
for idx, obj in enumerate(objects):
input_boxes.append(obj.bbox)
confidences.append(obj.score)
@@ -102,19 +106,26 @@ if WITH_SLICE_INFERENCE:
class_ids = detections.class_id
input_boxes = detections.xyxy
else:
image_url = client.upload_file(IMG_PATH)
infer_image_url = client.upload_file(IMG_PATH)
task = DinoxTask(
image_url=image_url,
prompts=[TextPrompt(text=TEXT_PROMPT)],
bbox_threshold=0.25,
targets=[DetectionTarget.BBox],
task = V2Task(
api_path="/v2/task/dinox/detection",
api_body={
"model": "DINO-X-1.0",
"image": infer_image_url,
"prompt": {
"type":"text",
"text":TEXT_PROMPT
},
"targets": ["bbox", "mask"],
"bbox_threshold": BOX_THRESHOLD,
"iou_threshold": IOU_THRESHOLD,
}
)
client.run_task(task)
result = task.result
objects = result.objects # the list of detected objects
objects = result["objects"] # the list of detected objects
input_boxes = []
@@ -123,9 +134,9 @@ else:
class_ids = []
for idx, obj in enumerate(objects):
input_boxes.append(obj.bbox)
confidences.append(obj.score)
cls_name = obj.category.lower().strip()
input_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])