import argparse import os import copy import numpy as np import json import torch from PIL import Image, ImageDraw, ImageFont # Grounding DINO import GroundingDINO.groundingdino.datasets.transforms as T from GroundingDINO.groundingdino.models import build_model from GroundingDINO.groundingdino.util import box_ops 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 ( sam_model_registry, sam_hq_model_registry, SamPredictor ) import cv2 import numpy as np import matplotlib.pyplot as plt def load_image(image_path): # load image image_pil = Image.open(image_path).convert("RGB") # load image 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_pil, image def load_model(model_config_path, model_checkpoint_path, device): args = SLConfig.fromfile(model_config_path) args.device = device model = build_model(args) checkpoint = torch.load(model_checkpoint_path, map_location="cpu") load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) print(load_res) _ = model.eval() return model def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): caption = caption.lower() caption = caption.strip() if not caption.endswith("."): caption = caption + "." model = model.to(device) image = image.to(device) 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) logits.shape[0] # 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 logits_filt.shape[0] # get phrase tokenlizer = model.tokenizer tokenized = tokenlizer(caption) # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) if with_logits: pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) return boxes_filt, pred_phrases def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30/255, 144/255, 255/255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_box(box, ax, label): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) ax.text(x0, y0, label) def save_mask_data(output_dir, mask_list, box_list, label_list): value = 0 # 0 for background mask_img = torch.zeros(mask_list.shape[-2:]) for idx, mask in enumerate(mask_list): mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 plt.figure(figsize=(10, 10)) plt.imshow(mask_img.numpy()) plt.axis('off') plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0) json_data = [{ 'value': value, 'label': 'background' }] for label, box in zip(label_list, box_list): value += 1 name, logit = label.split('(') logit = logit[:-1] # the last is ')' json_data.append({ 'value': value, 'label': name, 'logit': float(logit), 'box': box.numpy().tolist(), }) with open(os.path.join(output_dir, 'mask.json'), 'w') as f: json.dump(json_data, f) if __name__ == "__main__": parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True) parser.add_argument("--config", type=str, required=True, help="path to config file") parser.add_argument( "--grounded_checkpoint", type=str, required=True, help="path to checkpoint file" ) parser.add_argument( "--sam_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h" ) parser.add_argument( "--sam_checkpoint", type=str, required=False, help="path to sam checkpoint file" ) parser.add_argument( "--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file" ) parser.add_argument( "--use_sam_hq", action="store_true", help="using sam-hq for prediction" ) parser.add_argument("--input_image", type=str, required=True, help="path to image file") parser.add_argument("--text_prompt", type=str, required=True, help="text prompt") parser.add_argument( "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory" ) parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False") args = parser.parse_args() # cfg config_file = args.config # change the path of the model config file grounded_checkpoint = args.grounded_checkpoint # change the path of the model sam_version = args.sam_version sam_checkpoint = args.sam_checkpoint sam_hq_checkpoint = args.sam_hq_checkpoint use_sam_hq = args.use_sam_hq image_path = args.input_image text_prompt = args.text_prompt output_dir = args.output_dir box_threshold = args.box_threshold text_threshold = args.text_threshold device = args.device # make dir os.makedirs(output_dir, exist_ok=True) # load image image_pil, image = load_image(image_path) # load model model = load_model(config_file, grounded_checkpoint, device=device) # visualize raw image image_pil.save(os.path.join(output_dir, "raw_image.jpg")) # run grounding dino model boxes_filt, pred_phrases = get_grounding_output( model, image, text_prompt, box_threshold, text_threshold, device=device ) # initialize SAM if use_sam_hq: predictor = SamPredictor(sam_hq_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device)) else: predictor = SamPredictor(sam_model_registry[sam_version](checkpoint=sam_checkpoint).to(device)) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) predictor.set_image(image) size = image_pil.size H, W = size[1], size[0] 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] boxes_filt = boxes_filt.cpu() transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device) masks, _, _ = predictor.predict_torch( point_coords = None, point_labels = None, boxes = transformed_boxes.to(device), multimask_output = False, ) # draw output image plt.figure(figsize=(10, 10)) plt.imshow(image) for mask in masks: show_mask(mask.cpu().numpy(), plt.gca(), random_color=True) for box, label in zip(boxes_filt, pred_phrases): show_box(box.numpy(), plt.gca(), label) plt.axis('off') plt.savefig( os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight", dpi=300, pad_inches=0.0 ) save_mask_data(output_dir, masks, boxes_filt, pred_phrases)