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from GroundingDINO.groundingdino.datasets.transforms import Compose, RandomResize, ToTensor, Normalize |
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from io import BytesIO |
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import os |
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import copy |
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import numpy as np |
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import json |
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import torch |
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from PIL import Image, ImageDraw, ImageFont |
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import GroundingDINO.groundingdino.datasets.transforms as T |
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from GroundingDINO.groundingdino.models import build_model |
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from GroundingDINO.groundingdino.util import box_ops |
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from GroundingDINO.groundingdino.util.slconfig import SLConfig |
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from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap |
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from segment_anything import ( |
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build_sam, |
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build_sam_hq, |
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SamPredictor |
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) |
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import cv2 |
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import numpy as np |
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import matplotlib.pyplot as plt |
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def load_model(model_config_path, model_checkpoint_path, device): |
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args = SLConfig.fromfile(model_config_path) |
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args.device = device |
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model = build_model(args) |
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checkpoint = torch.load(model_checkpoint_path, map_location="cpu") |
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load_res = model.load_state_dict( |
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clean_state_dict(checkpoint["model"]), strict=False) |
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print(load_res) |
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_ = model.eval() |
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return model |
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def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): |
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caption = caption.lower() |
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caption = caption.strip() |
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if not caption.endswith("."): |
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caption = caption + "." |
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model = model.to(device) |
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image = image.to(device) |
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with torch.no_grad(): |
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outputs = model(image[None], captions=[caption]) |
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logits = outputs["pred_logits"].cpu().sigmoid()[0] |
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boxes = outputs["pred_boxes"].cpu()[0] |
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logits.shape[0] |
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logits_filt = logits.clone() |
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boxes_filt = boxes.clone() |
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filt_mask = logits_filt.max(dim=1)[0] > box_threshold |
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logits_filt = logits_filt[filt_mask] |
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boxes_filt = boxes_filt[filt_mask] |
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logits_filt.shape[0] |
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return boxes_filt |
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def grounded_sam_demo(input_pil, config_file, grounded_checkpoint, sam_checkpoint, |
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text_prompt, box_threshold=0.3, text_threshold=0.25, |
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device="cuda"): |
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transform = Compose([ |
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RandomResize([800], max_size=1333), |
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ToTensor(), |
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Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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]) |
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if input_pil.mode != "RGB": |
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input_pil = input_pil.convert("RGB") |
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image, _ = transform(input_pil, None) |
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model = load_model(config_file, grounded_checkpoint, device=device) |
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boxes_filt = get_grounding_output( |
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model, image, text_prompt, box_threshold, text_threshold, device=device) |
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predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device)) |
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image = cv2.cvtColor(np.array(input_pil), cv2.COLOR_RGB2BGR) |
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predictor.set_image(image) |
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size = input_pil.size |
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H, W = size[1], size[0] |
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for i in range(boxes_filt.size(0)): |
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boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) |
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boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 |
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boxes_filt[i][2:] += boxes_filt[i][:2] |
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boxes_filt = boxes_filt.cpu() |
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transformed_boxes = predictor.transform.apply_boxes_torch( |
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boxes_filt, image.shape[:2]).to(device) |
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masks, _, _ = predictor.predict_torch( |
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point_coords=None, |
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point_labels=None, |
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boxes=transformed_boxes.to(device), |
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multimask_output=False, |
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) |
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value = 0 |
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mask_img = torch.zeros(masks.shape[-2:]) |
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for idx, mask in enumerate(masks): |
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mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 |
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fig = plt.figure(figsize=(10, 10)) |
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plt.imshow(mask_img.numpy()) |
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plt.axis('off') |
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buf = BytesIO() |
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plt.savefig(buf, format='png', bbox_inches="tight", |
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dpi=300, pad_inches=0.0) |
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buf.seek(0) |
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out_pil = Image.open(buf) |
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return out_pil |
