182 lines
5.1 KiB
Python
182 lines
5.1 KiB
Python
# dds cloudapi for Grounding DINO 1.5
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from dds_cloudapi_sdk import Config
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from dds_cloudapi_sdk import Client
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from dds_cloudapi_sdk import DetectionTask
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from dds_cloudapi_sdk import TextPrompt
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from dds_cloudapi_sdk import DetectionModel
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from dds_cloudapi_sdk import DetectionTarget
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import os
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import cv2
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import json
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import torch
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import numpy as np
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import supervision as sv
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import pycocotools.mask as mask_util
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from pathlib import Path
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from PIL import Image
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from sam2.build_sam import build_sam2
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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"""
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Hyper parameters
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"""
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API_TOKEN = "Your API token"
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TEXT_PROMPT = "car"
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IMG_PATH = "notebooks/images/cars.jpg"
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SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
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SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
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GROUNDING_MODEL = DetectionModel.GDino1_5_Pro # DetectionModel.GDino1_6_Pro
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OUTPUT_DIR = Path("outputs/grounded_sam2_gd1.5_demo")
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DUMP_JSON_RESULTS = True
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# create output directory
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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"""
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Prompt Grounding DINO 1.5 with Text for Box Prompt Generation with Cloud API
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"""
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# Step 1: initialize the config
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token = API_TOKEN
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config = Config(token)
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# Step 2: initialize the client
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client = Client(config)
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# Step 3: run the task by DetectionTask class
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# image_url = "https://algosplt.oss-cn-shenzhen.aliyuncs.com/test_files/tasks/detection/iron_man.jpg"
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# if you are processing local image file, upload them to DDS server to get the image url
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img_path = IMG_PATH
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image_url = client.upload_file(img_path)
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task = DetectionTask(
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image_url=image_url,
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prompts=[TextPrompt(text=TEXT_PROMPT)],
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targets=[DetectionTarget.BBox], # detect bbox
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model=GROUNDING_MODEL, # detect with GroundingDino-1.5-Pro model
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)
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client.run_task(task)
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result = task.result
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objects = result.objects # the list of detected objects
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input_boxes = []
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confidences = []
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class_names = []
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for idx, obj in enumerate(objects):
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input_boxes.append(obj.bbox)
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confidences.append(obj.score)
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class_names.append(obj.category)
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input_boxes = np.array(input_boxes)
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"""
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Init SAM 2 Model and Predict Mask with Box Prompt
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"""
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# environment settings
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# use bfloat16
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torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
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if torch.cuda.get_device_properties(0).major >= 8:
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# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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# build SAM2 image predictor
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sam2_checkpoint = SAM2_CHECKPOINT
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model_cfg = SAM2_MODEL_CONFIG
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sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
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sam2_predictor = SAM2ImagePredictor(sam2_model)
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image = Image.open(img_path)
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sam2_predictor.set_image(np.array(image.convert("RGB")))
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masks, scores, logits = sam2_predictor.predict(
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point_coords=None,
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point_labels=None,
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box=input_boxes,
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multimask_output=False,
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)
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"""
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Post-process the output of the model to get the masks, scores, and logits for visualization
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"""
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# convert the shape to (n, H, W)
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if masks.ndim == 4:
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masks = masks.squeeze(1)
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"""
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Visualization the Predict Results
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"""
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class_ids = np.array(list(range(len(class_names))))
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labels = [
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f"{class_name} {confidence:.2f}"
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for class_name, confidence
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in zip(class_names, confidences)
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]
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"""
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Visualize image with supervision useful API
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"""
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img = cv2.imread(img_path)
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detections = sv.Detections(
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xyxy=input_boxes, # (n, 4)
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mask=masks.astype(bool), # (n, h, w)
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class_id=class_ids
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)
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box_annotator = sv.BoxAnnotator()
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annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections)
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label_annotator = sv.LabelAnnotator()
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annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "groundingdino_annotated_image.jpg"), annotated_frame)
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mask_annotator = sv.MaskAnnotator()
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annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "grounded_sam2_annotated_image_with_mask.jpg"), annotated_frame)
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"""
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Dump the results in standard format and save as json files
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"""
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def single_mask_to_rle(mask):
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rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
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rle["counts"] = rle["counts"].decode("utf-8")
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return rle
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if DUMP_JSON_RESULTS:
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# convert mask into rle format
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mask_rles = [single_mask_to_rle(mask) for mask in masks]
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input_boxes = input_boxes.tolist()
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scores = scores.tolist()
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# save the results in standard format
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results = {
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"image_path": img_path,
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"annotations" : [
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{
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"class_name": class_name,
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"bbox": box,
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"segmentation": mask_rle,
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"score": score,
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}
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for class_name, box, mask_rle, score in zip(class_names, input_boxes, mask_rles, scores)
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],
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"box_format": "xyxy",
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"img_width": image.width,
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"img_height": image.height,
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}
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with open(os.path.join(OUTPUT_DIR, "grounded_sam2_gd1.5_image_demo_results.json"), "w") as f:
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json.dump(results, f, indent=4)
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