import os import sys import warnings import random import time import logging from typing import Dict, List, Tuple, Union, Optional # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler()] ) logger = logging.getLogger(__name__) # Download model weights only if they don't exist if not os.path.exists("groundingdino_swint_ogc.pth"): os.system("wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth") if not os.path.exists("sam_hq_vit_l.pth"): os.system("wget https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth") # Add paths sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) sys.path.append(os.path.join(os.getcwd(), "sam-hq")) warnings.filterwarnings("ignore") import numpy as np import torch import torchvision import gradio as gr import argparse from PIL import Image, ImageDraw, ImageFont from scipy import ndimage # Grounding DINO import GroundingDINO.groundingdino.datasets.transforms as T from GroundingDINO.groundingdino.models import build_model from GroundingDINO.groundingdino.util.slconfig import SLConfig from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # segment anything from segment_anything import build_sam_vit_l, SamPredictor # BLIP from transformers import BlipProcessor, BlipForConditionalGeneration # Constants CONFIG_FILE = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' GROUNDINGDINO_CHECKPOINT = "groundingdino_swint_ogc.pth" SAM_CHECKPOINT = 'sam_hq_vit_l.pth' OUTPUT_DIR = "outputs" # Global variables for model caching _models = { 'groundingdino': None, 'sam_predictor': None, 'blip_processor': None, 'blip_model': None } # Enable GPU if available with proper error handling try: device = 'cuda' if torch.cuda.is_available() else 'cpu' logger.info(f"Using device: {device}") except Exception as e: logger.warning(f"Error detecting GPU, falling back to CPU: {e}") device = 'cpu' class ModelManager: """Manages model loading, unloading, and provides error handling""" @staticmethod def load_model(model_name: str) -> None: """Load a model if not already loaded""" try: if model_name == 'groundingdino' and _models['groundingdino'] is None: logger.info("Loading GroundingDINO model...") start_time = time.time() if not os.path.exists(GROUNDINGDINO_CHECKPOINT): raise FileNotFoundError(f"GroundingDINO checkpoint not found at {GROUNDINGDINO_CHECKPOINT}") args = SLConfig.fromfile(CONFIG_FILE) args.device = device model = build_model(args) checkpoint = torch.load(GROUNDINGDINO_CHECKPOINT, map_location="cpu") load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) logger.info(f"GroundingDINO load result: {load_res}") _ = model.eval() _models['groundingdino'] = model logger.info(f"GroundingDINO model loaded in {time.time() - start_time:.2f} seconds") elif model_name == 'sam' and _models['sam_predictor'] is None: logger.info("Loading SAM-HQ model...") start_time = time.time() if not os.path.exists(SAM_CHECKPOINT): raise FileNotFoundError(f"SAM checkpoint not found at {SAM_CHECKPOINT}") sam = build_sam_vit_l(checkpoint=SAM_CHECKPOINT) sam.to(device=device) _models['sam_predictor'] = SamPredictor(sam) logger.info(f"SAM-HQ model loaded in {time.time() - start_time:.2f} seconds") elif model_name == 'blip' and (_models['blip_processor'] is None or _models['blip_model'] is None): logger.info("Loading BLIP model...") start_time = time.time() _models['blip_processor'] = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") _models['blip_model'] = BlipForConditionalGeneration.from_pretrained( "Salesforce/blip-image-captioning-large", torch_dtype=torch.float16 ).to(device) logger.info(f"BLIP model loaded in {time.time() - start_time:.2f} seconds") except Exception as e: logger.error(f"Error loading {model_name} model: {e}") raise RuntimeError(f"Failed to load {model_name} model: {e}") @staticmethod def get_model(model_name: str): """Get a model, loading it if necessary""" if model_name not in _models or _models[model_name] is None: ModelManager.load_model(model_name) return _models[model_name] @staticmethod def unload_model(model_name: str) -> None: """Unload a model to free memory""" if model_name in _models and _models[model_name] is not None: logger.info(f"Unloading {model_name} model") _models[model_name] = None if device == 'cuda': torch.cuda.empty_cache() # def generate_caption(raw_image: Image.Image) -> str: # """Generate image caption using BLIP""" # try: # blip_processor = ModelManager.get_model('blip_processor') # blip_model = ModelManager.get_model('blip_model') # inputs = blip_processor(raw_image, return_tensors="pt").to(device, torch.float16) # out = blip_model.generate(**inputs) # caption = blip_processor.decode(out[0], skip_special_tokens=True) # logger.info(f"Generated caption: {caption}") # return caption # except Exception as e: # logger.error(f"Error generating caption: {e}") # return "Failed to generate caption." def transform_image(image_pil: Image.Image) -> torch.Tensor: """Transform PIL image for GroundingDINO""" transform = T.Compose([ T.RandomResize([800], max_size=1333), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) image, _ = transform(image_pil, None) # 3, h, w return image def get_grounding_output( image: torch.Tensor, caption: str, box_threshold: float, text_threshold: float, with_logits: bool = True ) -> Tuple[torch.Tensor, torch.Tensor, List[str]]: """Run GroundingDINO to get bounding boxes from text prompt""" try: model = ModelManager.get_model('groundingdino') # Format caption caption = caption.lower().strip() if not caption.endswith("."): caption = caption + "." with torch.no_grad(): outputs = model(image[None], captions=[caption]) logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) # Filter output logits_filt = logits.clone() boxes_filt = boxes.clone() filt_mask = logits_filt.max(dim=1)[0] > box_threshold logits_filt = logits_filt[filt_mask] # num_filt, 256 boxes_filt = boxes_filt[filt_mask] # num_filt, 4 # Get phrases tokenizer = model.tokenizer tokenized = tokenizer(caption) pred_phrases = [] scores = [] for logit, box in zip(logits_filt, boxes_filt): pred_phrase = get_phrases_from_posmap( logit > text_threshold, tokenized, tokenizer) if with_logits: pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) scores.append(logit.max().item()) return boxes_filt, torch.Tensor(scores), pred_phrases except Exception as e: logger.error(f"Error in grounding output: {e}") # Return empty results instead of crashing return torch.Tensor([]), torch.Tensor([]), [] def draw_mask(mask: np.ndarray, draw: ImageDraw.Draw) -> None: """Draw mask on image""" # if random_color: # color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153) # else: # color = (30, 144, 255, 153) color = (255, 255, 255, 255) nonzero_coords = np.transpose(np.nonzero(mask)) for coord in nonzero_coords: draw.point(coord[::-1], fill=color) def draw_box(box: torch.Tensor, draw: ImageDraw.Draw, label: Optional[str]) -> None: """Draw bounding box on image""" color = tuple(np.random.randint(0, 255, size=3).tolist()) draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=2) if label: font = ImageFont.load_default() if hasattr(font, "getbbox"): bbox = draw.textbbox((box[0], box[1]), str(label), font) else: w, h = draw.textsize(str(label), font) bbox = (box[0], box[1], w + box[0], box[1] + h) draw.rectangle(bbox, fill=color) draw.text((box[0], box[1]), str(label), fill="white") # def draw_point(point: np.ndarray, draw: ImageDraw.Draw, r: int = 10) -> None: # """Draw points on image""" # for p in point: # x, y = p # draw.ellipse((x-r, y-r, x+r, y+r), fill='green') # def process_scribble_points(scribble: np.ndarray) -> np.ndarray: # """Process scribble mask to get point coordinates""" # # Transpose to get the correct orientation # scribble = scribble.transpose(2, 1, 0)[0] # # Label connected components # labeled_array, num_features = ndimage.label(scribble >= 255) # if num_features == 0: # logger.warning("No points detected in scribble") # return np.array([]) # # Get center of mass for each component # centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features + 1)) # return np.array(centers) # def process_scribble_box(scribble: np.ndarray) -> torch.Tensor: # """Process scribble mask to get bounding box""" # # Get point coordinates first # centers = process_scribble_points(scribble) # if len(centers) < 2: # logger.warning("Not enough points for bounding box, need at least 2") # # Return a default small box in the center if not enough points # return torch.tensor([[0.4, 0.4, 0.6, 0.6]]) # # Define bounding box from scribble centers: (x_min, y_min, x_max, y_max) # x_min = centers[:, 0].min() # x_max = centers[:, 0].max() # y_min = centers[:, 1].min() # y_max = centers[:, 1].max() # bbox = np.array([x_min, y_min, x_max, y_max]) # return torch.tensor(bbox).unsqueeze(0) def run_grounded_sam( input_image # text_prompt: str, # task_type: str, # box_threshold: float, # text_threshold: float, # iou_threshold: float, # hq_token_only ) -> List[Image.Image]: """Main function to run GroundingDINO and SAM-HQ""" try: # Create output directory os.makedirs(OUTPUT_DIR, exist_ok=True) text_prompt = 'car' task_type = 'text' box_threshold = 0.3 text_threshold = 0.25 iou_threshold = 0.8 hq_token_only = True # Process input image if isinstance(input_image, dict): # Input from gradio sketch component scribble = np.array(input_image["mask"]) image_pil = input_image["image"].convert("RGB") else: # Direct image input image_pil = input_image.convert("RGB") if input_image else None scribble = None if image_pil is None: logger.error("No input image provided") return [Image.new('RGB', (400, 300), color='gray')] # # Prepare for scribble tasks # if task_type == 'scribble_box' or task_type == 'scribble_point': # if scribble is None: # logger.warning(f"No scribble provided for {task_type} task") # scribble = np.zeros((image_pil.height, image_pil.width, 3), dtype=np.uint8) # Transform image for GroundingDINO transformed_image = transform_image(image_pil) # Load models as needed ModelManager.load_model('groundingdino') size = image_pil.size H, W = size[1], size[0] # Run GroundingDINO with provided text boxes_filt, scores, pred_phrases = get_grounding_output( transformed_image, text_prompt, box_threshold, text_threshold ) # # Process based on task type # if task_type == 'automatic': # # Generate caption with BLIP # ModelManager.load_model('blip') # text_prompt = generate_caption(image_pil) # logger.info(f"Automatic caption: {text_prompt}") # # Run GroundingDINO # boxes_filt, scores, pred_phrases = get_grounding_output( # transformed_image, text_prompt, box_threshold, text_threshold # ) # elif task_type == 'text': # if not text_prompt: # logger.warning("No text prompt provided for 'text' task") # return [image_pil, Image.new('RGBA', size, color=(0, 0, 0, 0))] # # Run GroundingDINO with provided text # boxes_filt, scores, pred_phrases = get_grounding_output( # transformed_image, text_prompt, box_threshold, text_threshold # ) # elif task_type == 'scribble_box': # # No need for GroundingDINO, get box from scribble # boxes_filt = process_scribble_box(scribble) # scores = torch.ones(boxes_filt.size(0)) # pred_phrases = ["scribble_box"] * boxes_filt.size(0) # elif task_type == 'scribble_point': # # Will handle differently with SAM # point_coords = process_scribble_points(scribble) # if len(point_coords) == 0: # logger.warning("No points detected in scribble") # return [image_pil, Image.new('RGBA', size, color=(0, 0, 0, 0))] # boxes_filt = None # Not needed for point-based segmentation # else: # logger.error(f"Unknown task type: {task_type}") # return [image_pil, Image.new('RGBA', size, color=(0, 0, 0, 0))] # Process boxes if present (not for scribble_point) if boxes_filt is not None: # Scale boxes to image dimensions for i in range(boxes_filt.size(0)): boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 boxes_filt[i][2:] += boxes_filt[i][:2] # Apply non-maximum suppression if we have multiple boxes if boxes_filt.size(0) > 1: logger.info(f"Before NMS: {boxes_filt.shape[0]} boxes") nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist() boxes_filt = boxes_filt[nms_idx] pred_phrases = [pred_phrases[idx] for idx in nms_idx] logger.info(f"After NMS: {boxes_filt.shape[0]} boxes") # Load SAM model ModelManager.load_model('sam') sam_predictor = ModelManager.get_model('sam_predictor') # Set image for SAM image = np.array(image_pil) sam_predictor.set_image(image) # # Convert string to boolean # if isinstance(hq_token_only, str): # hq_token_only = (hq_token_only.lower() == 'true') # Run SAM # Use boxes for these task types if boxes_filt.size(0) == 0: logger.warning("No boxes detected") return [image_pil, Image.new('RGBA', size, color=(0, 0, 0, 0))] transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device) masks, _, _ = sam_predictor.predict_torch( point_coords=None, point_labels=None, boxes=transformed_boxes, multimask_output=False, hq_token_only=hq_token_only, ) # elif task_type == 'scribble_point': # # Use points for this task type # point_labels = np.ones(point_coords.shape[0]) # masks, _, _ = sam_predictor.predict( # point_coords=point_coords, # point_labels=point_labels, # box=None, # multimask_output=False, # hq_token_only=hq_token_only, # ) # Create mask image mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0)) mask_draw = ImageDraw.Draw(mask_image) # Draw masks if task_type == 'text': # for mask in masks: # draw_mask(mask, mask_draw, random_color=True) # else: for mask in masks: draw_mask(mask[0].cpu().numpy(), mask_draw) # Draw boxes and points on original image image_draw = ImageDraw.Draw(image_pil) for box, label in zip(boxes_filt, pred_phrases): draw_box(box, image_draw, label) # if task_type == 'scribble_box': # for box in boxes_filt: # draw_box(box, image_draw, None) # elif task_type in ['text', 'automatic']: # for box, label in zip(boxes_filt, pred_phrases): # draw_box(box, image_draw, label) # elif task_type == 'scribble_point': # draw_point(point_coords, image_draw) # Add caption text for automatic mode # if task_type == 'automatic': # image_draw.text((10, 10), text_prompt, fill='black') # Combine original image with mask # image_pil = image_pil.convert('RGBA') # image_pil.alpha_composite(mask_image) # return [image_pil, mask_image] return [mask_image] except Exception as e: logger.error(f"Error in run_grounded_sam: {e}") # Return original image on error if isinstance(input_image, dict) and "image" in input_image: return [input_image["image"], Image.new('RGBA', input_image["image"].size, color=(0, 0, 0, 0))] elif isinstance(input_image, Image.Image): return [input_image, Image.new('RGBA', input_image.size, color=(0, 0, 0, 0))] else: return [Image.new('RGB', (400, 300), color='gray'), Image.new('RGBA', (400, 300), color=(0, 0, 0, 0))] def create_ui(): """Create Gradio UI for CarViz demo""" with gr.Blocks(title="CarViz Demo") as block: gr.Markdown(""" # CarViz """) with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="image") # input_image = gr.ImageMask( # sources=["upload", "clipboard"], # transforms=[], # layers=False, # format="pil", # label="base image", # show_label=True # ) # task_type = gr.Dropdown( # ["automatic", "scribble_point", "scribble_box", "text"], # value="automatic", # label="Task Type" # ) # text_prompt = gr.Textbox(label="Text Prompt", placeholder="bench .") # hq_token_only = gr.Dropdown( # [False, True], value=False, label="hq_token_only" # ) run_button = gr.Button(value='Run') # with gr.Accordion("Advanced options", open=False): # box_threshold = gr.Slider( # label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.001 # ) # text_threshold = gr.Slider( # label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 # ) # iou_threshold = gr.Slider( # label="IOU Threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.001 # ) with gr.Column(): gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" ) # # Update visibility of text prompt based on task type # def update_text_prompt_visibility(task): # return gr.update(visible=(task == "text")) # task_type.change( # fn=update_text_prompt_visibility, # inputs=[task_type], # outputs=[text_prompt] # ) # Run button run_button.click( fn=run_grounded_sam, inputs=[ input_image # , text_prompt, task_type, # box_threshold, text_threshold, iou_threshold, hq_token_only ], outputs=gallery ) return block if __name__ == "__main__": parser = argparse.ArgumentParser("Grounded SAM demo", add_help=True) parser.add_argument("--debug", action="store_true", help="using debug mode") parser.add_argument("--share", action="store_true", help="share the app") parser.add_argument('--no-gradio-queue', action="store_true", help="disable gradio queue") parser.add_argument('--port', type=int, default=7860, help="port to run the app") parser.add_argument('--host', type=str, default="0.0.0.0", help="host to run the app") args = parser.parse_args() logger.info(f"Starting CarViz demo with args: {args}") # Check for model files if not os.path.exists(GROUNDINGDINO_CHECKPOINT): logger.warning(f"GroundingDINO checkpoint not found at {GROUNDINGDINO_CHECKPOINT}") if not os.path.exists(SAM_CHECKPOINT): logger.warning(f"SAM-HQ checkpoint not found at {SAM_CHECKPOINT}") # Create app block = create_ui() if not args.no_gradio_queue: block = block.queue() # Launch app try: block.launch( debug=args.debug, share=args.share, show_error=True, server_name=args.host, server_port=args.port ) except Exception as e: logger.error(f"Error launching app: {e}")