Spaces:
Running
on
T4
Running
on
T4
| import argparse | |
| import os | |
| 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 | |
| def load_image(image_path): | |
| # load image | |
| image_pil = Image.open(image_path).convert("RGB") # load image | |
| 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) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True) | |
| parser.add_argument("--config", type=str, required=True, help="path to config file") | |
| parser.add_argument( | |
| "--grounded_checkpoint", type=str, required=True, help="path to checkpoint file" | |
| ) | |
| parser.add_argument( | |
| "--sam_checkpoint", type=str, required=True, help="path to checkpoint file" | |
| ) | |
| parser.add_argument("--input_image", type=str, required=True, help="path to image file") | |
| parser.add_argument("--det_prompt", type=str, required=True, help="text prompt") | |
| parser.add_argument("--inpaint_prompt", type=str, required=True, help="inpaint prompt") | |
| parser.add_argument( | |
| "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory" | |
| ) | |
| parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") | |
| parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") | |
| parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False") | |
| args = parser.parse_args() | |
| # cfg | |
| config_file = args.config # change the path of the model config file | |
| grounded_checkpoint = args.grounded_checkpoint # change the path of the model | |
| sam_checkpoint = args.sam_checkpoint | |
| image_path = args.input_image | |
| det_prompt = args.det_prompt | |
| inpaint_prompt = args.inpaint_prompt | |
| output_dir = args.output_dir | |
| box_threshold = args.box_threshold | |
| text_threshold = args.box_threshold | |
| device = args.device | |
| # make dir | |
| os.makedirs(output_dir, exist_ok=True) | |
| # load image | |
| image_pil, image = load_image(image_path) | |
| # load model | |
| model = load_model(config_file, grounded_checkpoint, device=device) | |
| # 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, det_prompt, box_threshold, text_threshold, device=device | |
| ) | |
| # initialize SAM | |
| predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint)) | |
| image = cv2.imread(image_path) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| predictor.set_image(image) | |
| size = image_pil.size | |
| 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] | |
| # 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("cuda") | |
| # prompt = "A sofa, high quality, detailed" | |
| image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0] | |
| image.save(os.path.join(output_dir, "grounded_sam_inpainting_output.jpg")) | |
| # 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') | |
| # plt.savefig(os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight") | |