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import argparse
import os
import sys
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import groundingdino.datasets.transforms as T
from groundingdino.models import build_model
from groundingdino.util import box_ops
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
from groundingdino.util.vl_utils import create_positive_map_from_span
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
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, cpu_only=False):
args = SLConfig.fromfile(model_config_path)
args.device = "cuda" if not cpu_only else "cpu"
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=None, with_logits=True, cpu_only=False, token_spans=None):
assert text_threshold is not None or token_spans is not None, "text_threshould and token_spans should not be None at the same time!"
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
device = "cuda" if not cpu_only else "cpu"
model = model.to(device)
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
logits = outputs["pred_logits"].sigmoid()[0] # (nq, 256)
boxes = outputs["pred_boxes"][0] # (nq, 4)
# filter output
if token_spans is None:
logits_filt = logits.cpu().clone()
boxes_filt = boxes.cpu().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
# 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)
else:
# given-phrase mode
positive_maps = create_positive_map_from_span(
model.tokenizer(text_prompt),
token_span=token_spans
).to(image.device) # n_phrase, 256
logits_for_phrases = positive_maps @ logits.T # n_phrase, nq
all_logits = []
all_phrases = []
all_boxes = []
for (token_span, logit_phr) in zip(token_spans, logits_for_phrases):
# get phrase
phrase = ' '.join([caption[_s:_e] for (_s, _e) in token_span])
# get mask
filt_mask = logit_phr > box_threshold
# filt box
all_boxes.append(boxes[filt_mask])
# filt logits
all_logits.append(logit_phr[filt_mask])
if with_logits:
logit_phr_num = logit_phr[filt_mask]
all_phrases.extend([phrase + f"({str(logit.item())[:4]})" for logit in logit_phr_num])
else:
all_phrases.extend([phrase for _ in range(len(filt_mask))])
boxes_filt = torch.cat(all_boxes, dim=0).cpu()
pred_phrases = all_phrases
return boxes_filt, pred_phrases
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
parser.add_argument(
"--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
)
parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text 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("--token_spans", type=str, default=None, help=
"The positions of start and end positions of phrases of interest. \
For example, a caption is 'a cat and a dog', \
if you would like to detect 'cat', the token_spans should be '[[[2, 5]], ]', since 'a cat and a dog'[2:5] is 'cat'. \
if you would like to detect 'a cat', the token_spans should be '[[[0, 1], [2, 5]], ]', since 'a cat and a dog'[0:1] is 'a', and 'a cat and a dog'[2:5] is 'cat'. \
")
parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
args = parser.parse_args()
# cfg
config_file = args.config_file # change the path of the model config file
checkpoint_path = args.checkpoint_path # change the path of the model
image_path = args.image_path
text_prompt = args.text_prompt
output_dir = args.output_dir
box_threshold = args.box_threshold
text_threshold = args.text_threshold
token_spans = args.token_spans
# 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, checkpoint_path, cpu_only=args.cpu_only)
# visualize raw image
image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
# set the text_threshold to None if token_spans is set.
if token_spans is not None:
text_threshold = None
print("Using token_spans. Set the text_threshold to None.")
# run model
boxes_filt, pred_phrases = get_grounding_output(
model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only, token_spans=eval(f"{token_spans}")
)
# visualize pred
size = image_pil.size
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_with_box.save(os.path.join(output_dir, "pred.jpg"))
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