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- import argparse
- import os
- import sys
- import numpy as np
- import json
- import torch
- from PIL import Image
- sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
- sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
- # 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 (
- 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, bert_base_uncased_path, device):
- args = SLConfig.fromfile(model_config_path)
- args.device = device
- args.bert_base_uncased_path = bert_base_uncased_path
- 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")
- parser.add_argument("--bert_base_uncased_path", type=str, required=False, help="bert_base_uncased model path, 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
- bert_base_uncased_path = args.bert_base_uncased_path
- # 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, bert_base_uncased_path, 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)
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