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import argparse | |
import os | |
import numpy as np | |
import torch | |
from PIL import Image, ImageDraw, ImageFont | |
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 | |
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, device="cpu"): | |
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 | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser("Grounding DINO example", 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("--input_image", type=str, required=True, help="path to image file") | |
parser.add_argument("--text_prompt", 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("--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 | |
image_path = args.input_image | |
text_prompt = args.text_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 model | |
boxes_filt, pred_phrases = get_grounding_output( | |
model, image, text_prompt, box_threshold, text_threshold, device=device | |
) | |
# 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, "grounding_dino_output.jpg")) |