support dump results in local demo

grounded_sam2_local_demo.py

@@ -1,35 +1,55 @@
+import os
 import cv2
+import json
 import torch
 import numpy as np
 import supervision as sv
+import pycocotools.mask as mask_util
+from pathlib import Path
 from torchvision.ops import box_convert
 from sam2.build_sam import build_sam2
 from sam2.sam2_image_predictor import SAM2ImagePredictor
 from grounding_dino.groundingdino.util.inference import load_model, load_image, predict
 
+"""
+Hyper parameters
+"""
+TEXT_PROMPT = "car. tire."
+IMG_PATH = "notebooks/images/truck.jpg"
+SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
+SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
+GROUNDING_DINO_CONFIG = "grounding_dino/groundingdino/config/GroundingDINO_SwinT_OGC.py"
+GROUNDING_DINO_CHECKPOINT = "gdino_checkpoints/groundingdino_swint_ogc.pth"
+BOX_THRESHOLD = 0.35
+TEXT_THRESHOLD = 0.25
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+OUTPUT_DIR = Path("outputs/grounded_sam2_local_demo")
+DUMP_JSON_RESULTS = True
+
+# create output directory
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
 # environment settings
 # use bfloat16
 
 # 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_checkpoint = SAM2_CHECKPOINT
+model_cfg = SAM2_MODEL_CONFIG
+sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=DEVICE)
 sam2_predictor = SAM2ImagePredictor(sam2_model)
 
 # build grounding dino model
-model_id = "IDEA-Research/grounding-dino-tiny"
-device = "cuda" if torch.cuda.is_available() else "cpu"
 grounding_model = load_model(
-    model_config_path="grounding_dino/groundingdino/config/GroundingDINO_SwinT_OGC.py", 
-    model_checkpoint_path="gdino_checkpoints/groundingdino_swint_ogc.pth",
-    device=device
+    model_config_path=GROUNDING_DINO_CONFIG, 
+    model_checkpoint_path=GROUNDING_DINO_CHECKPOINT,
+    device=DEVICE
 )
 
 
 # setup the input image and text prompt for SAM 2 and Grounding DINO
 # VERY important: text queries need to be lowercased + end with a dot
-text = "car. tire."
-img_path = 'notebooks/images/truck.jpg'
+text = TEXT_PROMPT
+img_path = IMG_PATH
 
 image_source, image = load_image(img_path)
 
@@ -39,8 +59,8 @@ boxes, confidences, labels = predict(
     model=grounding_model,
     image=image,
     caption=text,
-    box_threshold=0.35,
-    text_threshold=0.25
+    box_threshold=BOX_THRESHOLD,
+    text_threshold=TEXT_THRESHOLD,
 )
 
 # process the box prompt for SAM 2
@@ -98,8 +118,43 @@ 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)
+cv2.imwrite(os.path.join(OUTPUT_DIR, "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)
+cv2.imwrite(os.path.join(OUTPUT_DIR, "grounded_sam2_annotated_image_with_mask.jpg"), annotated_frame)
+
+"""
+Dump the results in standard format and save as json files
+"""
+
+def single_mask_to_rle(mask):
+    rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+    rle["counts"] = rle["counts"].decode("utf-8")
+    return rle
+
+if DUMP_JSON_RESULTS:
+    # convert mask into rle format
+    mask_rles = [single_mask_to_rle(mask) for mask in masks]
+
+    input_boxes = input_boxes.tolist()
+    scores = scores.tolist()
+    # save the results in standard format
+    results = {
+        "image_path": img_path,
+        "annotations" : [
+            {
+                "class_name": class_name,
+                "bbox": box,
+                "segmentation": mask_rle,
+                "score": score,
+            }
+            for class_name, box, mask_rle, score in zip(class_names, input_boxes, mask_rles, scores)
+        ],
+        "box_format": "xyxy",
+        "img_width": w,
+        "img_height": h,
+    }
+    
+    with open(os.path.join(OUTPUT_DIR, "grounded_sam2_local_image_demo_results.json"), "w") as f:
+        json.dump(results, f, indent=4)