import os, sys import random import warnings os.system("python -m pip install -e sam-hq") os.system("python -m pip install -e GroundingDINO") os.system("pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel") os.system("wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth") os.system("wget https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example0.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example1.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example2.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example3.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example4.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example5.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example6.png") os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example7.png") sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) sys.path.append(os.path.join(os.getcwd(), "sam-hq")) warnings.filterwarnings("ignore") import gradio as gr import argparse import numpy as np import torch import torchvision from PIL import Image, ImageDraw, ImageFont from scipy import ndimage # 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_vit_l, SamPredictor import numpy as np # 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) def draw_point(point, draw, r=10): show_point = [] for p in point: x,y = p draw.ellipse((x-r, y-r, x+r, y+r), fill='green') config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' ckpt_filenmae = "groundingdino_swint_ogc.pth" sam_checkpoint = 'sam_hq_vit_l.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 def run_grounded_sam(input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold, hq_token_only): global blip_processor, blip_model, groundingdino_model, sam_predictor # make dir os.makedirs(output_dir, exist_ok=True) # load image scribble = np.array(input_image["mask"]) image_pil = input_image["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 sam_predictor is None: # initialize SAM assert sam_checkpoint, 'sam_checkpoint is not found!' sam = build_sam_vit_l(checkpoint=sam_checkpoint) sam.to(device=device) sam_predictor = SamPredictor(sam) image = np.array(image_pil) sam_predictor.set_image(image) hq_token_only = (hq_token_only=='True') # str2bool if task_type == 'automatic': # use NMS to handle overlapped boxes print(f"Revise caption with number: {text_prompt}") if task_type == 'text' or task_type == 'automatic' or task_type == 'scribble_box': if task_type == 'scribble_box': scribble = scribble.transpose(2, 1, 0)[0] labeled_array, num_features = ndimage.label(scribble >= 255) centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1)) centers = np.array(centers) ### (x1, y1, x2, y2) x_min = centers[:, 0].min() x_max = centers[:, 0].max() y_min = centers[:, 1].min() y_max = centers[:, 1].max() bbox = np.array([x_min, y_min, x_max, y_max]) bbox = torch.tensor(bbox).unsqueeze(0) transformed_boxes = sam_predictor.transform.apply_boxes_torch(bbox, image.shape[:2]).to(device) else: 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, hq_token_only=hq_token_only, ) # masks: [1, 1, 512, 512] 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) if task_type == 'scribble_box': for box in bbox: draw_box(box, image_draw, None) else: 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 == 'scribble_point': scribble = scribble.transpose(2, 1, 0)[0] labeled_array, num_features = ndimage.label(scribble >= 255) centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1)) centers = np.array(centers) point_coords = centers point_labels = np.ones(point_coords.shape[0]) masks, _, _ = sam_predictor.predict( point_coords=point_coords, point_labels=point_labels, box=None, multimask_output=False, hq_token_only=hq_token_only, ) mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0)) mask_draw = ImageDraw.Draw(mask_image) for mask in masks: draw_mask(mask, mask_draw, random_color=True) image_draw = ImageDraw.Draw(image_pil) draw_point(point_coords,image_draw) image_pil = image_pil.convert('RGBA') image_pil.alpha_composite(mask_image) return [image_pil, mask_image] 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: gr.Markdown( """ # Segment Anything in High Quality [[`ArXiv`](https://arxiv.org/abs/2306.01567)] [[`Code`](https://github.com/SysCV/sam-hq)] Welcome to the SAM-HQ demo
You may select different prompt types to get the output mask of target instance. ## Usage You may check the instruction below, or check our github page about more details.
You may select an example image or upload your image to start, we support 4 prompt types: **text**: Send text prompt to identify the target instance in the `Text prompt` box. **scribble_point**: Click an point on the target instance. **scribble_box**: Click on two points, the top-left point and the bottom-right point to represent a bounding box of the target instance. **automatic**: Automaticly generate text prompt and the corresponding box input. In advanced options, we also support a hyper-paramter **hq_token_only**. False means use hq output to correct SAM output. True means use hq output only. Default: False. To achieve best visualization effect, for images contain multiple objects (like typical coco images), we suggest to set hq_token_only=False. For images contain single object, we suggest to set hq_token_only = True.
""") with gr.Row(): with gr.Column(): input_image = gr.Image( source='upload', type="pil", value="example1.png", tool="sketch",brush_radius=20) task_type = gr.Dropdown( ["text", "scribble_point", "scribble_box", "automatic"], value="text", label="task_type") text_prompt = gr.Textbox(label="Text Prompt", placeholder="butterfly .") run_button = gr.Button(label="Run") 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 ) hq_token_only = gr.Dropdown( [False, True], value=False, label="hq_token_only" ) with gr.Column(): gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" ).style(preview=True, grid=2, object_fit="scale-down") with gr.Row(): with gr.Column(): gr.Examples(["example0.png"], inputs=input_image) with gr.Column(): gr.Examples(["example1.png"], inputs=input_image) with gr.Column(): gr.Examples(["example2.png"], inputs=input_image) with gr.Column(): gr.Examples(["example3.png"], inputs=input_image) with gr.Column(): gr.Examples(["example4.png"], inputs=input_image) with gr.Column(): gr.Examples(["example5.png"], inputs=input_image) with gr.Column(): gr.Examples(["example6.png"], inputs=input_image) with gr.Column(): gr.Examples(["example7.png"], inputs=input_image) run_button.click(fn=run_grounded_sam, inputs=[ input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold, hq_token_only], outputs=gallery) block.launch(debug=args.debug, share=args.share, show_error=True)