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Running
on
T4
| 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 | |
| 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, with_logits=True, cpu_only=False): | |
| 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"].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_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("--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 | |
| # 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")) | |
| # run model | |
| boxes_filt, pred_phrases = get_grounding_output( | |
| model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only | |
| ) | |
| # 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")) | |