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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("--cache_dir", type=str, default=None, help="save your huggingface large model cache")
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("--inpaint_mode", type=str, default="first", help="inpaint mode")
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
cache_dir=args.cache_dir
box_threshold = args.box_threshold
text_threshold = args.text_threshold
inpaint_mode = args.inpaint_mode
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).to(device))
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]).to(device)
masks, _, _ = predictor.predict_torch(
point_coords = None,
point_labels = None,
boxes = transformed_boxes.to(device),
multimask_output = False,
)
# masks: [1, 1, 512, 512]
# 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")
# inpainting pipeline
if inpaint_mode == 'merge':
masks = torch.sum(masks, dim=0).unsqueeze(0)
masks = torch.where(masks > 0, True, False)
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,cache_dir=cache_dir
)
pipe = pipe.to("cuda")
image_pil = image_pil.resize((512, 512))
mask_pil = mask_pil.resize((512, 512))
# prompt = "A sofa, high quality, detailed"
image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]
image = image.resize(size)
image.save(os.path.join(output_dir, "grounded_sam_inpainting_output.jpg"))
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