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"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
import cv2
import argparse
import util.io
from torchvision.transforms import Compose
from dpt.models import DPTDepthModel
from dpt.midas_net import MidasNet_large
from dpt.transforms import Resize, NormalizeImage, PrepareForNet
#from util.misc import visualize_attention
def run(input_path, output_path, model_path, model_type="dpt_hybrid", optimize=True):
"""Run MonoDepthNN to compute depth maps.
Args:
input_path (str): path to input folder
output_path (str): path to output folder
model_path (str): path to saved model
"""
print("initialize")
# select device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: %s" % device)
# load network
if model_type == "dpt_large": # DPT-Large
net_w = net_h = 384
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
enable_attention_hooks=False,
)
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
net_w = net_h = 384
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
enable_attention_hooks=False,
)
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid_kitti":
net_w = 1216
net_h = 352
model = DPTDepthModel(
path=model_path,
scale=0.00006016,
shift=0.00579,
invert=True,
backbone="vitb_rn50_384",
non_negative=True,
enable_attention_hooks=False,
)
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid_nyu":
net_w = 640
net_h = 480
model = DPTDepthModel(
path=model_path,
scale=0.000305,
shift=0.1378,
invert=True,
backbone="vitb_rn50_384",
non_negative=True,
enable_attention_hooks=False,
)
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21": # Convolutional model
net_w = net_h = 384
model = MidasNet_large(model_path, non_negative=True)
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
assert (
False
), f"model_type '{model_type}' not implemented, use: --model_type [dpt_large|dpt_hybrid|dpt_hybrid_kitti|dpt_hybrid_nyu|midas_v21]"
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="minimal",
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
model.eval()
if optimize == True and device == torch.device("cuda"):
model = model.to(memory_format=torch.channels_last)
model = model.half()
model.to(device)
# get input
img_names = glob.glob(os.path.join(input_path, "*"))
num_images = len(img_names)
# create output folder
os.makedirs(output_path, exist_ok=True)
print("start processing")
for ind, img_name in enumerate(img_names):
if os.path.isdir(img_name):
continue
print(" processing {} ({}/{})".format(img_name, ind + 1, num_images))
# input
img = util.io.read_image(img_name)
if args.kitti_crop is True:
height, width, _ = img.shape
top = height - 352
left = (width - 1216) // 2
img = img[top : top + 352, left : left + 1216, :]
img_input = transform({"image": img})["image"]
# compute
with torch.no_grad():
sample = torch.from_numpy(img_input).to(device).unsqueeze(0)
if optimize == True and device == torch.device("cuda"):
sample = sample.to(memory_format=torch.channels_last)
sample = sample.half()
prediction = model.forward(sample)
prediction = (
torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img.shape[:2],
mode="bicubic",
align_corners=False,
)
.squeeze()
.cpu()
.numpy()
)
if model_type == "dpt_hybrid_kitti":
prediction *= 256
if model_type == "dpt_hybrid_nyu":
prediction *= 1000.0
filename = os.path.join(
output_path, os.path.splitext(os.path.basename(img_name))[0]
)
util.io.write_depth(filename, prediction, bits=2, absolute_depth=args.absolute_depth)
print("finished")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-i", "--input_path", default="input", help="folder with input images"
)
parser.add_argument(
"-o",
"--output_path",
default="output_monodepth",
help="folder for output images",
)
parser.add_argument(
"-m", "--model_weights", default=None, help="path to model weights"
)
parser.add_argument(
"-t",
"--model_type",
default="dpt_hybrid",
help="model type [dpt_large|dpt_hybrid|midas_v21]",
)
parser.add_argument("--kitti_crop", dest="kitti_crop", action="store_true")
parser.add_argument("--absolute_depth", dest="absolute_depth", action="store_true")
parser.add_argument("--optimize", dest="optimize", action="store_true")
parser.add_argument("--no-optimize", dest="optimize", action="store_false")
parser.set_defaults(optimize=True)
parser.set_defaults(kitti_crop=False)
parser.set_defaults(absolute_depth=False)
args = parser.parse_args()
default_models = {
"midas_v21": "weights/midas_v21-f6b98070.pt",
"dpt_large": "weights/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "weights/dpt_hybrid-midas-501f0c75.pt",
"dpt_hybrid_kitti": "weights/dpt_hybrid_kitti-cb926ef4.pt",
"dpt_hybrid_nyu": "weights/dpt_hybrid_nyu-2ce69ec7.pt",
}
if args.model_weights is None:
args.model_weights = default_models[args.model_type]
# set torch options
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# compute depth maps
run(
args.input_path,
args.output_path,
args.model_weights,
args.model_type,
args.optimize,
)
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