import os, sys import random import warnings os.system("python -m pip install -e segment_anything") os.system("python -m pip install -e GroundingDINO") os.system("pip install --upgrade diffusers[torch]") os.system("pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel") os.system("wget https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/demo1.jpg") os.system("wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth") os.system("wget https://huggingface.co/spaces/mrtlive/segment-anything-model/resolve/main/sam_vit_h_4b8939.pth") sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) sys.path.append(os.path.join(os.getcwd(), "segment_anything")) warnings.filterwarnings("ignore") import gradio as gr import argparse import numpy as np import torch import torchvision 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.slconfig import SLConfig from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # segment anything from segment_anything import build_sam, SamPredictor import numpy as np # diffusers import torch from diffusers import StableDiffusionInpaintPipeline # BLIP from transformers import BlipProcessor, BlipForConditionalGeneration def generate_caption(processor, blip_model, raw_image): # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to( "cuda", 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 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): 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 draw_mask(mask, draw, random_color=False): if random_color: color = (random.randint(0, 255), random.randint( 0, 255), random.randint(0, 255), 153) else: color = (30, 144, 255, 153) nonzero_coords = np.transpose(np.nonzero(mask)) for coord in nonzero_coords: draw.point(coord[::-1], fill=color) def draw_box(box, draw, label): # random color 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") draw.text((box[0], box[1]), label) config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' ckpt_repo_id = "ShilongLiu/GroundingDINO" ckpt_filenmae = "groundingdino_swint_ogc.pth" sam_checkpoint = 'sam_vit_h_4b8939.pth' output_dir = "outputs" device = 'cuda' if torch.cuda.is_available() else 'cpu' blip_processor = None blip_model = None groundingdino_model = None sam_predictor = None inpaint_pipeline = None def run_grounded_sam(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode): global blip_processor, blip_model, groundingdino_model, sam_predictor, inpaint_pipeline # make dir os.makedirs(output_dir, exist_ok=True) # load image image_pil = input_image.convert("RGB") transformed_image = transform_image(image_pil) if groundingdino_model is None: groundingdino_model = load_model( config_file, ckpt_filenmae, device=device) 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("cuda") text_prompt = generate_caption(blip_processor, blip_model, image_pil) 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 ) size = image_pil.size # 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() # nms 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") if task_type == 'seg' or task_type == 'inpainting' or task_type == 'automatic': if sam_predictor is None: # initialize SAM assert sam_checkpoint, 'sam_checkpoint is not found!' sam = build_sam(checkpoint=sam_checkpoint) sam.to(device=device) sam_predictor = SamPredictor(sam) image = np.array(image_pil) sam_predictor.set_image(image) if task_type == 'automatic': # use NMS to handle overlapped 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, ) # masks: [1, 1, 512, 512] if task_type == 'det': image_draw = ImageDraw.Draw(image_pil) for box, label in zip(boxes_filt, pred_phrases): draw_box(box, image_draw, label) return [image_pil] elif task_type == 'seg' or task_type == 'automatic': mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0)) mask_draw = ImageDraw.Draw(mask_image) for mask in masks: draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True) image_draw = ImageDraw.Draw(image_pil) for box, label in zip(boxes_filt, pred_phrases): draw_box(box, image_draw, label) if task_type == 'automatic': image_draw.text((10, 10), text_prompt, fill='black') image_pil = image_pil.convert('RGBA') image_pil.alpha_composite(mask_image) return [image_pil, mask_image] elif task_type == 'inpainting': assert inpaint_prompt, 'inpaint_prompt is not found!' # inpainting pipeline if inpaint_mode == 'merge': masks = torch.sum(masks, dim=0).unsqueeze(0) masks = torch.where(masks > 0, True, False) # simply choose the first mask, which will be refine in the future release mask = masks[0][0].cpu().numpy() mask_pil = Image.fromarray(mask) if inpaint_pipeline is None: inpaint_pipeline = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16 ) inpaint_pipeline = inpaint_pipeline.to("cuda") image = inpaint_pipeline(prompt=inpaint_prompt, image=image_pil.resize( (512, 512)), mask_image=mask_pil.resize((512, 512))).images[0] image = image.resize(size) return [image, mask_pil] else: print("task_type:{} error!".format(task_type)) 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='path to the SAM checkpoint') args = parser.parse_args() print(args) block = gr.Blocks() if not args.no_gradio_queue: block = block.queue() with block: with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", value="demo1.jpg") task_type = gr.Dropdown( ["det", "seg", "inpainting", "automatic"], value="seg", label="task_type") text_prompt = gr.Textbox(label="Text Prompt", placeholder="bear . beach .") inpaint_prompt = gr.Textbox(label="Inpaint Prompt", placeholder="A dinosaur, detailed, 4K.") run_button = gr.Button() 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 ) inpaint_mode = gr.Dropdown( ["merge", "first"], value="merge", label="inpaint_mode") with gr.Column(): gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" )#.style(preview=True, grid=2, object_fit="scale-down") run_button.click(fn=run_grounded_sam, inputs=[ input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode], outputs=gallery) block.launch(debug=args.debug, show_error=True)