import argparse
import os
import sys
import numpy as np
import json
import torch
from PIL import Image
sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
# Grounding DINO
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
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 sam_model_registry
import cv2
import numpy as np
import matplotlib.pyplot as plt
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, bert_base_uncased_path, device):
args = SLConfig.fromfile(model_config_path)
args.device = device
args.bert_base_uncased_path = bert_base_uncased_path
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)
def save_mask_data(output_dir, mask_list, box_list, label_list):
value = 0 # 0 for background
mask_img = torch.zeros(mask_list.shape[-2:])
for idx, mask in enumerate(mask_list):
mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1
plt.figure(figsize=(10, 10))
plt.imshow(mask_img.numpy())
plt.axis('off')
plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0)
json_data = [{
'value': value,
'label': 'background'
}]
for label, box in zip(label_list, box_list):
value += 1
name, logit = label.split('(')
logit = logit[:-1] # the last is ')'
json_data.append({
'value': value,
'label': name,
'logit': float(logit),
'box': box.numpy().tolist(),
})
with open(os.path.join(output_dir, 'mask.json'), 'w') as f:
json.dump(json_data, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True)
parser.add_argument("--config", type=str, default="./GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py", help="path to config file")
parser.add_argument(
"--grounded_checkpoint", type=str, default="./groundingdino_swint_ogc.pth", help="path to checkpoint file"
)
parser.add_argument(
"--sam_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h"
)
parser.add_argument(
"--sam_checkpoint", type=str, default="./sam_vit_h_4b8939.pth", help="path to sam checkpoint file"
)
parser.add_argument(
"--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file"
)
parser.add_argument(
"--use_sam_hq", action="store_true", help="using sam-hq for prediction"
)
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", 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")
parser.add_argument("--bert_base_uncased_path", type=str, required=False, help="bert_base_uncased model path, 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_version = args.sam_version
sam_checkpoint = args.sam_checkpoint
sam_hq_checkpoint = args.sam_hq_checkpoint
use_sam_hq = args.use_sam_hq
image_path = args.input_image
text_prompt = args.text_prompt
output_dir = args.output_dir
box_threshold = args.box_threshold
text_threshold = args.text_threshold
device = args.device
bert_base_uncased_path = args.bert_base_uncased_path
# 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, bert_base_uncased_path, 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, text_prompt, box_threshold, text_threshold, device=device
)
# initialize SAM
if use_sam_hq:
predictor = SamPredictor(sam_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device))
else:
predictor = SamPredictor(sam_model_registry[sam_version](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,
)
# 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", dpi=300, pad_inches=0.0
)
save_mask_data(output_dir, masks, boxes_filt, pred_phrases)
运行时报错
C:\Users\29386\.conda\envs\grounded_sam\python.exe C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py --grounded_checkpoint weights/groundingdino_swint_ogc.pth --sam_checkpoint weights/sam_vit_h_4b8939.pth --input_image assets/demo1.jpg --output_dir outputs --text_prompt cat
C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\timm\models\layers\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3610.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
final text_encoder_type: bert-base-uncased
C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\29386\.cache\huggingface\hub\models--bert-base-uncased. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
warnings.warn(message)
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py:47: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
Traceback (most recent call last):
File "C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py", line 187, in <module>
model = load_model(config_file, grounded_checkpoint, bert_base_uncased_path, device=device)
File "C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py", line 47, in load_model
checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 1065, in load
with _open_file_like(f, 'rb') as opened_file:
File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 468, in _open_file_like
return _open_file(name_or_buffer, mode)
File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 449, in __init__
super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'weights/groundingdino_swint_ogc.pth'
如何在windows系统上解决问题
最新发布
针对Android应用在Nexus 5x和LeEco-2设备上显示不正常的问题，可以通过获取设备的屏幕尺寸和密度来确定合适的资源文件夹。提供的方法能够检测设备属于small、normal、large还是xlarge类别，以及对应的ldpi、mdpi、hdpi、xhdpi、xxhdpi或xxxhdpi密度，从而帮助调整UI设计。
扫一扫
971
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
