wehub-resource-sync
300 lines
11 KiB
Python
300 lines
11 KiB
Python
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)
|
|
|
|
|