add dump results to hf model demo

grounded_sam2_gd1.5_demo.py

@@ -22,11 +22,12 @@ from sam2.sam2_image_predictor import SAM2ImagePredictor
 Hyper parameters
 """
 API_TOKEN = "Your API token"
-TEXT_PROMPT = "car"
+TEXT_PROMPT = "car . building ."
 IMG_PATH = "notebooks/images/cars.jpg"
 SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
 SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
 GROUNDING_MODEL = DetectionModel.GDino1_5_Pro # DetectionModel.GDino1_6_Pro
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 OUTPUT_DIR = Path("outputs/grounded_sam2_gd1.5_demo")
 DUMP_JSON_RESULTS = True
 
@@ -79,7 +80,7 @@ Init SAM 2 Model and Predict Mask with Box Prompt
 
 # environment settings
 # use bfloat16
-torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+torch.autocast(device_type=DEVICE, 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)
@@ -89,7 +90,7 @@ if torch.cuda.get_device_properties(0).major >= 8:
 # build SAM2 image predictor
 sam2_checkpoint = SAM2_CHECKPOINT
 model_cfg = SAM2_MODEL_CONFIG
-sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
+sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=DEVICE)
 sam2_predictor = SAM2ImagePredictor(sam2_model)
 
 image = Image.open(img_path)
@@ -160,6 +161,8 @@ if DUMP_JSON_RESULTS:
 
     input_boxes = input_boxes.tolist()
     scores = scores.tolist()
+    # FIXME: class_names should be a list of strings without spaces
+    class_names = [class_name.strip() for class_name in class_names]
     # save the results in standard format
     results = {
         "image_path": img_path,

grounded_sam2_hf_model_demo.py

@@ -1,7 +1,11 @@
+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 supervision.draw.color import ColorPalette
 from utils.supervision_utils import CUSTOM_COLOR_MAP
 from PIL import Image
@@ -9,9 +13,24 @@ from sam2.build_sam import build_sam2
 from sam2.sam2_image_predictor import SAM2ImagePredictor
 from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection 
 
+"""
+Hyper parameters
+"""
+GROUNDING_MODEL = "IDEA-Research/grounding-dino-tiny"
+TEXT_PROMPT = "car. tire."
+IMG_PATH = "notebooks/images/truck.jpg"
+SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
+SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+OUTPUT_DIR = Path("outputs/grounded_sam2_hf_model_demo")
+DUMP_JSON_RESULTS = True
+
+# create output directory
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
 # environment settings
 # use bfloat16
-torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+torch.autocast(device_type=DEVICE, 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)
@@ -19,28 +38,27 @@ if torch.cuda.get_device_properties(0).major >= 8:
     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_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 from huggingface
-model_id = "IDEA-Research/grounding-dino-tiny"
-device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = GROUNDING_MODEL
 processor = AutoProcessor.from_pretrained(model_id)
-grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(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 = Image.open(img_path)
 
 sam2_predictor.set_image(np.array(image.convert("RGB")))
 
-inputs = processor(images=image, text=text, return_tensors="pt").to(device)
+inputs = processor(images=image, text=text, return_tensors="pt").to(DEVICE)
 with torch.no_grad():
     outputs = grounding_model(**inputs)
 
@@ -114,8 +132,44 @@ annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections
 
 label_annotator = sv.LabelAnnotator(color=ColorPalette.from_hex(CUSTOM_COLOR_MAP))
 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(color=ColorPalette.from_hex(CUSTOM_COLOR_MAP))
 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": image.width,
+        "img_height": image.height,
+    }
+    
+    with open(os.path.join(OUTPUT_DIR, "grounded_sam2_hf_model_demo_results.json"), "w") as f:
+        json.dump(results, f, indent=4)