import numpy as np import torch import torchvision from scipy import ndimage # BLIP from transformers import BlipProcessor, BlipForConditionalGeneration # SAM from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator # GroundingDINO from groundingdino.datasets import transforms as T from groundingdino.models import build_model from groundingdino.util.slconfig import SLConfig from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap def load_grounding_dino_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 generate_caption(processor, blip_model, raw_image, device): # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to(device, torch.float16) out = blip_model.generate(**inputs) caption = processor.decode(out[0], skip_special_tokens=True) return caption def transform_image(image_pil): 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(model, image, caption, box_threshold, text_threshold, with_logits=True): caption = caption.lower() caption = caption.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) 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 = [] scores = [] 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) scores.append(logit.max().item()) return boxes_filt, torch.Tensor(scores), pred_phrases def run_grounded_sam(input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold, scribble_mode, sam, groundingdino_model, sam_predictor=None, sam_automask_generator=None, device="cuda"): global blip_processor, blip_model, inpaint_pipeline # load image image = input_image["image"] scribble = input_image["mask"] size = image.size # w, h if sam_predictor is None: sam_predictor = SamPredictor(sam) sam_automask_generator = SamAutomaticMaskGenerator(sam) image_pil = image.convert("RGB") image = np.array(image_pil) if task_type == 'scribble': sam_predictor.set_image(image) scribble = scribble.convert("RGB") scribble = np.array(scribble) scribble = scribble.transpose(2, 1, 0)[0] # 将连通域进行标记 labeled_array, num_features = ndimage.label(scribble >= 255) # 计算每个连通域的质心 centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1)) centers = np.array(centers) point_coords = torch.from_numpy(centers) point_coords = sam_predictor.transform.apply_coords_torch(point_coords, image.shape[:2]) point_coords = point_coords.unsqueeze(0).to(device) point_labels = torch.from_numpy(np.array([1] * len(centers))).unsqueeze(0).to(device) if scribble_mode == 'split': point_coords = point_coords.permute(1, 0, 2) point_labels = point_labels.permute(1, 0) masks, _, _ = sam_predictor.predict_torch( point_coords=point_coords if len(point_coords) > 0 else None, point_labels=point_labels if len(point_coords) > 0 else None, mask_input = None, boxes = None, multimask_output = False, ) elif task_type == 'automask': masks = sam_automask_generator.generate(image) else: transformed_image = transform_image(image_pil) if task_type == 'automatic': # generate caption and tags # use Tag2Text can generate better captions # https://huggingface.co/spaces/xinyu1205/Tag2Text # but there are some bugs... blip_processor = blip_processor or BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") blip_model = blip_model or BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to(device) text_prompt = generate_caption(blip_processor, blip_model, image_pil, device) print(f"Caption: {text_prompt}") # run grounding dino model boxes_filt, scores, pred_phrases = get_grounding_output( groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold ) # process boxes 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() if task_type == 'seg' or task_type == 'inpainting' or task_type == 'automatic': sam_predictor.set_image(image) if task_type == 'automatic': # use NMS to handle overlapped boxes print(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] print(f"After NMS: {boxes_filt.shape[0]} boxes") print(f"Revise caption with number: {text_prompt}") 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, ) return masks else: print("task_type:{} error!".format(task_type))