Spaces:
Runtime error
Runtime error
File size: 5,865 Bytes
b18cfd3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
import utils
import cv2
import argparse
from torchvision.transforms import Compose
from midas.dpt_depth import DPTDepthModel
from midas.midas_net import MidasNet
from midas.midas_net_custom import MidasNet_small
from midas.transforms import Resize, NormalizeImage, PrepareForNet
def run(input_path, output_path, model_path, model_type="large", 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
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": #DPT-Hybrid
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode="minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode="upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "midas_v21_small":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode="upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
model.eval()
if optimize==True:
# rand_example = torch.rand(1, 3, net_h, net_w)
# model(rand_example)
# traced_script_module = torch.jit.trace(model, rand_example)
# model = traced_script_module
if 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):
print(" processing {} ({}/{})".format(img_name, ind + 1, num_images))
# input
img = utils.read_image(img_name)
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()
)
# output
filename = os.path.join(
output_path, os.path.splitext(os.path.basename(img_name))[0]
)
utils.write_depth(filename, prediction, bits=2)
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',
help='folder for output images'
)
parser.add_argument('-m', '--model_weights',
default=None,
help='path to the trained weights of model'
)
parser.add_argument('-t', '--model_type',
default='dpt_large',
help='model type: dpt_large, dpt_hybrid, midas_v21_large or midas_v21_small'
)
parser.add_argument('--optimize', dest='optimize', action='store_true')
parser.add_argument('--no-optimize', dest='optimize', action='store_false')
parser.set_defaults(optimize=True)
args = parser.parse_args()
default_models = {
"midas_v21_small": "weights/midas_v21_small-70d6b9c8.pt",
"midas_v21": "weights/midas_v21-f6b98070.pt",
"dpt_large": "weights/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "weights/dpt_hybrid-midas-501f0c75.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)
|