import subprocess, os, sys sys.path.insert(0, './GroundingDINO') result = subprocess.run(['pip', 'list'], check=True) print(f'pip list result = {result}') if not os.path.exists('./sam_vit_h_4b8939.pth'): result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True) print(f'wget sam_vit_h_4b8939.pth result = {result}') import gradio as gr import argparse import copy import numpy as np import torch 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 import box_ops 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 cv2 import numpy as np import matplotlib.pyplot as plt # diffusers import PIL import requests import torch from io import BytesIO from diffusers import StableDiffusionInpaintPipeline from huggingface_hub import hf_hub_download def get_device(): from numba import cuda if cuda.is_available(): device = cuda.get_current_device() else: device = 'cpu' return device def load_model_hf(model_config_path, repo_id, filename, device='cpu'): args = SLConfig.fromfile(model_config_path) model = build_model(args) args.device = device cache_file = hf_hub_download(repo_id=repo_id, filename=filename) checkpoint = torch.load(cache_file, map_location='cpu') log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) print("Model loaded from {} \n => {}".format(cache_file, log)) _ = model.eval() return model def plot_boxes_to_image(image_pil, tgt): H, W = tgt["size"] boxes = tgt["boxes"] labels = tgt["labels"] assert len(boxes) == len(labels), "boxes and labels must have same length" draw = ImageDraw.Draw(image_pil) mask = Image.new("L", image_pil.size, 0) mask_draw = ImageDraw.Draw(mask) # draw boxes and masks for box, label in zip(boxes, labels): # from 0..1 to 0..W, 0..H box = box * torch.Tensor([W, H, W, H]) # from xywh to xyxy box[:2] -= box[2:] / 2 box[2:] += box[:2] # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) # draw x0, y0, x1, y1 = box x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) draw.rectangle([x0, y0, x1, y1], outline=color, width=6) # draw.text((x0, y0), str(label), fill=color) font = ImageFont.load_default() if hasattr(font, "getbbox"): bbox = draw.textbbox((x0, y0), str(label), font) else: w, h = draw.textsize(str(label), font) bbox = (x0, y0, w + x0, y0 + h) # bbox = draw.textbbox((x0, y0), str(label)) draw.rectangle(bbox, fill=color) draw.text((x0, y0), str(label), fill="white") mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) return image_pil, mask def load_image(image_path): # # load image # image_pil = Image.open(image_path).convert("RGB") # load image image_pil = image_path 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, 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, 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) 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" device = get_device() def run_grounded_sam(image_path, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold): assert text_prompt, 'text_prompt is not found!' # make dir os.makedirs(output_dir, exist_ok=True) # load image image_pil, image = load_image(image_path.convert("RGB")) # load model model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) # 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 ) size = image_pil.size if task_type == 'seg' or task_type == 'inpainting': # initialize SAM predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint)) image = np.array(image_path) predictor.set_image(image) 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]) masks, _, _ = predictor.predict_torch( point_coords = None, point_labels = None, boxes = transformed_boxes, multimask_output = False, ) # masks: [1, 1, 512, 512] if task_type == 'det': pred_dict = { "boxes": boxes_filt, "size": [size[1], size[0]], # H,W "labels": pred_phrases, } # import ipdb; ipdb.set_trace() image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0] image_path = os.path.join(output_dir, "grounding_dino_output.jpg") image_with_box.save(image_path) image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) return image_result elif task_type == 'seg': assert sam_checkpoint, 'sam_checkpoint is not found!' # 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') image_path = os.path.join(output_dir, "grounding_dino_output.jpg") plt.savefig(image_path, bbox_inches="tight") image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) return image_result elif task_type == 'inpainting': assert inpaint_prompt, 'inpaint_prompt is not found!' # inpainting pipeline mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release mask_pil = Image.fromarray(mask) image_pil = Image.fromarray(image) pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", # torch_dtype=torch.float16 ) pipe = pipe.to(device) image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0] image_path = os.path.join(output_dir, "grounded_sam_inpainting_output.jpg") image.save(image_path) image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) return image_result else: print("task_type:{} error!".format(task_type)) def change_task_type(task_type): if task_type == "inpainting": return gr.Textbox.update(visible=True) else: return gr.Textbox.update(visible=False) 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") args = parser.parse_args() print(f'args = {args}') block = gr.Blocks().queue() with block: with gr.Row(): with gr.Column(): input_image = gr.Image(source='upload', type="pil") task_type = gr.Radio(["detection", "segment", "inpainting"], value="detection", label='Task type:',interactive=True, visible=True) text_prompt = gr.Textbox(label="Detection Prompt") inpaint_prompt = gr.Textbox(label="Inpaint Prompt", visible=False) 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 ) with gr.Column(): gallery = gr.outputs.Image( type="pil", ).style(full_width=True, full_height=True) run_button.click(fn=run_grounded_sam, inputs=[ input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold], outputs=[gallery]) task_type.change(fn=change_task_type, inputs=[task_type], outputs=[inpaint_prompt]) block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share)