nervn / grounded_sam_osx_demo.py

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	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)