vierundvi / grounded_sam_osx_demo.py
mart9992's picture
m
2cd560a
import torchvision.transforms as transforms
from torch.nn.parallel.data_parallel import DataParallel
import torch.backends.cudnn as cudnn
import argparse
import json
import torch
from PIL import Image
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
# 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 build_sam, SamPredictor
# OSX
import sys
sys.path.insert(0, 'grounded-sam-osx')
from osx import get_model
from config import cfg
from utils.preprocessing import load_img, process_bbox, generate_patch_image
from utils.human_models import smpl_x
os.environ["PYOPENGL_PLATFORM"] = "egl"
from utils.vis import render_mesh, save_obj
cudnn.benchmark = True
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]
if 'person' in label.lower() or 'human' in label.lower():
color = 'green'
else:
color = 'blue'
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor=color, facecolor=(0, 0, 0, 0), lw=2))
ax.text(x0, y0-5, label, fontsize=5, color='white',bbox={'facecolor': color, 'alpha': 0.7, 'pad': 1, 'edgecolor': 'none'})
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)
def bbox_resize(bbox, scale=1.0):
center = (bbox[2:] + bbox[:2]) / 2
new_size = (bbox[2:] - bbox[:2]) * scale
new_bbox = torch.cat((center - new_size / 2, center + new_size / 2))
return new_bbox
def mesh_recovery(original_img, bboxes):
transform = transforms.ToTensor()
original_img_height, original_img_width = original_img.shape[:2]
vis_img = original_img.copy()
for bbox in bboxes: # [x1, y1, x2, y2]
bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]] # xyxy -> xyhw
bbox = process_bbox(bbox, original_img_width, original_img_height)
img, img2bb_trans, bb2img_trans = generate_patch_image(original_img, bbox, 1.0, 0.0, False, cfg.input_img_shape)
img = transform(img.astype(np.float32)) / 255
img = img.cuda()[None, :, :, :]
# forward
inputs = {'img': img}
with torch.no_grad():
out = model(inputs, 'test')
mesh = out['smplx_mesh_cam'].detach().cpu().numpy()[0]
# # save mesh
# save_obj(mesh, smpl_x.face, 'output.obj')
focal = [cfg.focal[0] / cfg.input_body_shape[1] * bbox[2], cfg.focal[1] / cfg.input_body_shape[0] * bbox[3]]
princpt = [cfg.princpt[0] / cfg.input_body_shape[1] * bbox[2] + bbox[0],
cfg.princpt[1] / cfg.input_body_shape[0] * bbox[3] + bbox[1]]
rendered_img, _ = render_mesh(vis_img[:, :, ::-1], mesh, smpl_x.face, {'focal': focal, 'princpt': princpt})
vis_img = rendered_img.copy()
return rendered_img
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(
"--osx_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
sam_checkpoint = args.sam_checkpoint
osx_checkpoint = args.osx_checkpoint
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
# 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, text_prompt, box_threshold, text_threshold, device=device
)
# initialize SAM
sam = build_sam(checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)
# initialize OSX
model = get_model()
model = DataParallel(model).cuda()
ckpt = torch.load(osx_checkpoint)
model.load_state_dict(ckpt['network'], strict=False)
model.eval()
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,
multimask_output=False,
)
# scale up the human bboxes
boxes_human = []
for i, label in enumerate(pred_phrases):
if 'person' in label.lower() or 'human' in label.lower():
boxes_filt[i] = bbox_resize(boxes_filt[i], scale=1.1)
boxes_human.append(boxes_filt[i])
# predict and visualize 3d human mesh
for i, label in enumerate(pred_phrases):
if 'person' in label.lower() or 'man' in label.lower():
boxes_human.append(boxes_filt[i])
rendered_img = mesh_recovery(image, boxes_human)
cv2.imwrite(os.path.join(output_dir, "grounded_sam_osx_output.jpg"), rendered_img)
# draw output image
fig, (plt1, plt2) = plt.subplots(ncols=2, figsize=(10, 20), gridspec_kw={'wspace':0, 'hspace':0})
plt1.imshow(image)
for mask in masks:
show_mask(mask.cpu().numpy(), plt1, random_color=True)
for box, label in zip(boxes_filt, pred_phrases):
show_box(box.numpy(), plt1, label)
rendered_img = cv2.imread(os.path.join(output_dir, "grounded_sam_osx_output.jpg"))
plt2.imshow(rendered_img)
for box, label in zip(boxes_filt, pred_phrases):
show_box(box.numpy(), plt2, label)
plt1.axis('off')
plt2.axis('off')
plt.savefig(
os.path.join(output_dir, "grounded_sam_osx_output.jpg"),
bbox_inches="tight", dpi=300, pad_inches=0.0
)
save_mask_data(output_dir, masks, boxes_filt, pred_phrases)