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| 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 == 'segment' 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 == 'detection': | |
| 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 == 'segment': | |
| 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", placeholder="Cannot be empty") | |
| inpaint_prompt = gr.Textbox(label="Inpaint Prompt", visible=True) | |
| 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]) | |
| DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). Thanks for their excellent work.' | |
| DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| gr.Markdown(DESCRIPTION) | |
| block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share) |