def MiDaS_small(pretrained=True, **kwargs):
""" # This docstring shows up in hub.help()
MiDaS small model for monocular depth estimation on resource-constrained devices
pretrained (bool): load pretrained weights into model
"""
model = MidasNet_small(None,
features=64,
backbone="efficientnet_lite3",
exportable=True,
non_negative=True,
blocks={'expand': True})
if pretrained:
checkpoint = (
"https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt"
)
state_dict = torch.hub.load_state_dict_from_url(
checkpoint,
map_location=torch.device('cpu'),
progress=True,
check_hash=True)
model.load_state_dict(state_dict)
return model
def __init__(self, model_type, model_path, optimize):
print("initialize")
# select device
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: %s" % self.device)
# load network
if model_type == "large":
self.model = MidasNet(model_path, non_negative=True)
self.net_w, self.net_h = 384, 384
elif model_type == "small":
self.model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
self.net_w, self.net_h = 256, 256
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
self.transform = Compose(
[
Resize(
self.net_w,
self.net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
]
)
self.model.eval()
self.optimize = optimize
if self.optimize:
rand_example = torch.rand(1, 3, self.net_h, self.net_w)
self.model(rand_example)
traced_script_module = torch.jit.trace(self.model, rand_example)
self.model = traced_script_module
if self.device == torch.device("cuda"):
self.model = self.model.to(memory_format=torch.channels_last)
self.model = self.model.half()
self.model.to(self.device)
def init_model(transform):
parser = argparse.ArgumentParser()
parser.add_argument('-mw', '--model_weights',
default='model-f6b98070.pt',
help='path to the trained weights of model'
)
parser.add_argument('-mt', '--model_type',
default='large',
help='model type: large or 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, unknown = parser.parse_known_args()
# set torch options
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
print("initialize")
# select device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: %s" % device)
# load network
if args.model_type == "large":
model_path = "../MiDaS/"+args.model_weights
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
elif args.model_type == "small":
if "small" not in args.model_weights:
args.model_weights = "model-small-70d6b9c8.pt"
model_path = "../MiDaS/"+args.model_weights
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
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="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
]
)
model.eval()
if args.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)
return (model, transform, device, args.optimize), args
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 == "large":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
elif model_type == "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
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="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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())
prediction /= 1000
# output
filename = os.path.join(
output_path,
os.path.splitext(os.path.basename(img_name))[0])
utils.write_depth(filename, prediction, bits=2)
print(prediction)
print(prediction.shape)
print("finished")
class DepthPredictor:
def __init__(self, model_type, model_path, optimize):
print("initialize")
# select device
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: %s" % self.device)
# load network
if model_type == "large":
self.model = MidasNet(model_path, non_negative=True)
self.net_w, self.net_h = 384, 384
elif model_type == "small":
self.model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
self.net_w, self.net_h = 256, 256
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
self.transform = Compose(
[
Resize(
self.net_w,
self.net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
]
)
self.model.eval()
self.optimize = optimize
if self.optimize:
rand_example = torch.rand(1, 3, self.net_h, self.net_w)
self.model(rand_example)
traced_script_module = torch.jit.trace(self.model, rand_example)
self.model = traced_script_module
if self.device == torch.device("cuda"):
self.model = self.model.to(memory_format=torch.channels_last)
self.model = self.model.half()
self.model.to(self.device)
def process_video(self, filename, dir_name):
cap = cv2.VideoCapture(filename)
fps = cap.get(cv2.CAP_PROP_FPS)
count = 0
while cap.isOpened():
ret, frame = cap.read()
if ret:
if len(frame.shape) == 2:
img = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) / 255.0
prediction = self.process_images(img)
# output
out_filename = os.path.join(
dir_name, str(count / 30)[0]
)
utils.write_depth(out_filename, prediction, bits=2)
count += fps
cap.set(1, count)
else:
cap.release()
break
def process_images(self, img):
img_input = self.transform({"image": img})["image"]
# compute
with torch.no_grad():
sample = torch.from_numpy(img_input).to(self.device).unsqueeze(0)
if self.optimize and self.device == torch.device("cuda"):
sample = sample.to(memory_format=torch.channels_last)
sample = sample.half()
prediction = self.model.forward(sample)
prediction = (
torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img.shape[:2],
mode="bicubic",
align_corners=False,
)
.squeeze()
.cpu()
.numpy()
)
return prediction
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Setup AdaBins model
self.adabins_nyu_infer_helper = InferenceHelper(dataset='nyu',
device=self.device)
self.adabins_kitti_infer_helper = InferenceHelper(dataset='kitti',
device=self.device)
# Setup DiverseDepth model
class DiverseDepthArgs:
def __init__(self):
self.resume = False
self.cfg_file = "lib/configs/resnext50_32x4d_diversedepth_regression_vircam"
self.load_ckpt = "pretrained/DiverseDepth.pth"
diverse_depth_args = DiverseDepthArgs()
merge_cfg_from_file(diverse_depth_args)
self.diverse_depth_model = RelDepthModel()
self.diverse_depth_model.eval()
# load checkpoint
load_ckpt(diverse_depth_args, self.diverse_depth_model)
# TODO: update this - see how `device` argument should be processsed
if self.device == "cuda":
self.diverse_depth_model.cuda()
self.diverse_depth_model = torch.nn.DataParallel(
self.diverse_depth_model)
# Setup MiDaS model
self.midas_model_path = "./pretrained/MiDaS_f6b98070.pt"
midas_model_type = "large"
# load network
if midas_model_type == "large":
self.midas_model = MidasNet(self.midas_model_path,
non_negative=True)
self.midas_net_w, self.midas_net_h = 384, 384
elif midas_model_type == "small":
self.midas_model = MidasNet_small(self.midas_model_path,
features=64,
backbone="efficientnet_lite3",
exportable=True,
non_negative=True,
blocks={'expand': True})
self.midas_net_w, self.midas_net_h = 256, 256
self.midas_transform = Compose([
Resize(
self.midas_net_w,
self.midas_net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
self.midas_model.eval()
self.midas_optimize = True
if self.midas_optimize == True:
rand_example = torch.rand(1, 3, self.midas_net_h, self.midas_net_w)
self.midas_model(rand_example)
traced_script_module = torch.jit.trace(self.midas_model,
rand_example)
self.midas_model = traced_script_module
if self.device == "cuda":
self.midas_model = self.midas_model.to(
memory_format=torch.channels_last)
self.midas_model = self.midas_model.half()
self.midas_model.to(torch.device(self.device))
# Setup SGDepth model
self.sgdepth_model = InferenceEngine.SgDepthInference()
# Setup monodepth2 model
self.monodepth2_model_path = "pretrained/monodepth2_mono+stereo_640x192"
monodepth2_device = torch.device(self.device)
encoder_path = os.path.join(self.monodepth2_model_path, "encoder.pth")
depth_decoder_path = os.path.join(self.monodepth2_model_path,
"depth.pth")
# LOADING PRETRAINED MODEL
print(" Loading Monodepth2 pretrained encoder")
self.monodepth2_encoder = networks.ResnetEncoder(18, False)
loaded_dict_enc = torch.load(encoder_path,
map_location=monodepth2_device)
# extract the height and width of image that this model was trained with
self.feed_height = loaded_dict_enc['height']
self.feed_width = loaded_dict_enc['width']
filtered_dict_enc = {
k: v
for k, v in loaded_dict_enc.items()
if k in self.monodepth2_encoder.state_dict()
}
self.monodepth2_encoder.load_state_dict(filtered_dict_enc)
self.monodepth2_encoder.to(monodepth2_device)
self.monodepth2_encoder.eval()
print(" Loading pretrained decoder")
self.monodepth2_depth_decoder = networks.DepthDecoder(
num_ch_enc=self.monodepth2_encoder.num_ch_enc, scales=range(4))
loaded_dict = torch.load(depth_decoder_path,
map_location=monodepth2_device)
self.monodepth2_depth_decoder.load_state_dict(loaded_dict)
self.monodepth2_depth_decoder.to(monodepth2_device)
self.monodepth2_depth_decoder.eval()
class InferenceEngine:
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Setup AdaBins model
self.adabins_nyu_infer_helper = InferenceHelper(dataset='nyu',
device=self.device)
self.adabins_kitti_infer_helper = InferenceHelper(dataset='kitti',
device=self.device)
# Setup DiverseDepth model
class DiverseDepthArgs:
def __init__(self):
self.resume = False
self.cfg_file = "lib/configs/resnext50_32x4d_diversedepth_regression_vircam"
self.load_ckpt = "pretrained/DiverseDepth.pth"
diverse_depth_args = DiverseDepthArgs()
merge_cfg_from_file(diverse_depth_args)
self.diverse_depth_model = RelDepthModel()
self.diverse_depth_model.eval()
# load checkpoint
load_ckpt(diverse_depth_args, self.diverse_depth_model)
# TODO: update this - see how `device` argument should be processsed
if self.device == "cuda":
self.diverse_depth_model.cuda()
self.diverse_depth_model = torch.nn.DataParallel(
self.diverse_depth_model)
# Setup MiDaS model
self.midas_model_path = "./pretrained/MiDaS_f6b98070.pt"
midas_model_type = "large"
# load network
if midas_model_type == "large":
self.midas_model = MidasNet(self.midas_model_path,
non_negative=True)
self.midas_net_w, self.midas_net_h = 384, 384
elif midas_model_type == "small":
self.midas_model = MidasNet_small(self.midas_model_path,
features=64,
backbone="efficientnet_lite3",
exportable=True,
non_negative=True,
blocks={'expand': True})
self.midas_net_w, self.midas_net_h = 256, 256
self.midas_transform = Compose([
Resize(
self.midas_net_w,
self.midas_net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
self.midas_model.eval()
self.midas_optimize = True
if self.midas_optimize == True:
rand_example = torch.rand(1, 3, self.midas_net_h, self.midas_net_w)
self.midas_model(rand_example)
traced_script_module = torch.jit.trace(self.midas_model,
rand_example)
self.midas_model = traced_script_module
if self.device == "cuda":
self.midas_model = self.midas_model.to(
memory_format=torch.channels_last)
self.midas_model = self.midas_model.half()
self.midas_model.to(torch.device(self.device))
# Setup SGDepth model
self.sgdepth_model = InferenceEngine.SgDepthInference()
# Setup monodepth2 model
self.monodepth2_model_path = "pretrained/monodepth2_mono+stereo_640x192"
monodepth2_device = torch.device(self.device)
encoder_path = os.path.join(self.monodepth2_model_path, "encoder.pth")
depth_decoder_path = os.path.join(self.monodepth2_model_path,
"depth.pth")
# LOADING PRETRAINED MODEL
print(" Loading Monodepth2 pretrained encoder")
self.monodepth2_encoder = networks.ResnetEncoder(18, False)
loaded_dict_enc = torch.load(encoder_path,
map_location=monodepth2_device)
# extract the height and width of image that this model was trained with
self.feed_height = loaded_dict_enc['height']
self.feed_width = loaded_dict_enc['width']
filtered_dict_enc = {
k: v
for k, v in loaded_dict_enc.items()
if k in self.monodepth2_encoder.state_dict()
}
self.monodepth2_encoder.load_state_dict(filtered_dict_enc)
self.monodepth2_encoder.to(monodepth2_device)
self.monodepth2_encoder.eval()
print(" Loading pretrained decoder")
self.monodepth2_depth_decoder = networks.DepthDecoder(
num_ch_enc=self.monodepth2_encoder.num_ch_enc, scales=range(4))
loaded_dict = torch.load(depth_decoder_path,
map_location=monodepth2_device)
self.monodepth2_depth_decoder.load_state_dict(loaded_dict)
self.monodepth2_depth_decoder.to(monodepth2_device)
self.monodepth2_depth_decoder.eval()
def adabins_nyu_predict(self, image):
_, predicted_depth = self.adabins_nyu_infer_helper.predict_pil(image)
predicted_depth = predicted_depth.squeeze()
predicted_depth = cv2.resize(predicted_depth,
(image.width, image.height))
return predicted_depth
def adabins_kitti_predict(self, image):
_, predicted_depth = self.adabins_kitti_infer_helper.predict_pil(image)
predicted_depth = predicted_depth.squeeze()
predicted_depth = cv2.resize(predicted_depth,
(image.width, image.height))
return predicted_depth
def diverse_depth_predict(self, image):
img_torch = scale_torch(image, 255)
img_torch = img_torch[np.newaxis, :]
predicted_depth, _ = self.diverse_depth_model.module.depth_model(
img_torch)
predicted_depth = predicted_depth.detach().cpu().numpy()
predicted_depth = predicted_depth.squeeze()
return predicted_depth
def midas_predict(self, image_path):
"""Run MonoDepthNN to compute depth maps.
Args:
image_path (str): path to input image
"""
def read_image(path):
"""Read image and output RGB image (0-1).
Args:
path (str): path to file
Returns:
array: RGB image (0-1)
"""
img = cv2.imread(path)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
return img
# get input
img = read_image(image_path)
img_input = self.midas_transform({"image": img})["image"]
# compute
with torch.no_grad():
sample = torch.from_numpy(img_input).to(torch.device(
self.device)).unsqueeze(0)
if self.midas_optimize == True and self.device == "cuda":
sample = sample.to(memory_format=torch.channels_last)
sample = sample.half()
prediction = self.midas_model.forward(sample)
prediction = (torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze().cpu().numpy())
depth_min = prediction.min()
depth_max = prediction.max()
prediction = (prediction - depth_min) / (depth_max - depth_min)
# prediction *= 10
return prediction
class SgDepthInference:
"""Inference without harness or dataloader"""
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model_path = "./pretrained/SGDepth_full.pth"
self.num_classes = 20
self.depth_min = 0.1
self.depth_max = 100
self.all_time = []
self.labels = (
('CLS_ROAD', (128, 64, 128)),
('CLS_SIDEWALK', (244, 35, 232)),
('CLS_BUILDING', (70, 70, 70)),
('CLS_WALL', (102, 102, 156)),
('CLS_FENCE', (190, 153, 153)),
('CLS_POLE', (153, 153, 153)),
('CLS_TRLIGHT', (250, 170, 30)),
('CLS_TRSIGN', (220, 220, 0)),
('CLS_VEGT', (107, 142, 35)),
('CLS_TERR', (152, 251, 152)),
('CLS_SKY', (70, 130, 180)),
('CLS_PERSON', (220, 20, 60)),
('CLS_RIDER', (255, 0, 0)),
('CLS_CAR', (0, 0, 142)),
('CLS_TRUCK', (0, 0, 70)),
('CLS_BUS', (0, 60, 100)),
('CLS_TRAIN', (0, 80, 100)),
('CLS_MCYCLE', (0, 0, 230)),
('CLS_BCYCLE', (119, 11, 32)),
)
self.init_model()
def init_model(self):
sgdepth = SGDepth
with torch.no_grad():
# init 'empty' model
self.model = sgdepth(
1, # opt.model_split_pos
18, # opt.model_num_layers
0.9, # opt.train_depth_grad_scale
0.1, # opt.train_segmentation_grad_scale
'pretrained', # opt.train_weights_init
4, # opt.model_depth_resolutions
18, # opt.model_num_layers_pose
)
# load weights (copied from state manager)
state = self.model.state_dict()
to_load = torch.load(self.model_path)
for (k, v) in to_load.items():
if k not in state:
print(
f" - WARNING: Model file contains unknown key {k} ({list(v.shape)})"
)
for (k, v) in state.items():
if k not in to_load:
print(
f" - WARNING: Model file does not contain key {k} ({list(v.shape)})"
)
else:
state[k] = to_load[k]
self.model.load_state_dict(state)
self.model = self.model.eval()
if self.device == "cuda":
self.model.cuda()
def load_image(self, image):
self.image = image
self.image_o_width, self.image_o_height = self.image.size
resize = transforms.Resize((192, 640))
image = resize(self.image) # resize to argument size
#center_crop = transforms.CenterCrop((opt.inference_crop_height, opt.inference_crop_width))
#image = center_crop(image) # crop to input size
to_tensor = transforms.ToTensor() # transform to tensor
self.input_image = to_tensor(
image
) # save tensor image to self.input_image for saving later
image = self.normalize(self.input_image)
image = image.unsqueeze(0).float()
if self.device == "cuda":
image = image.cuda()
# simulate structure of batch:
image_dict = {('color_aug', 0, 0): image} # dict
image_dict[('color', 0, 0)] = image
image_dict['domain'] = [
'cityscapes_val_seg',
]
image_dict['purposes'] = [[
'segmentation',
], [
'depth',
]]
image_dict['num_classes'] = torch.tensor([self.num_classes])
image_dict['domain_idx'] = torch.tensor(0)
self.batch = (image_dict, ) # batch tuple
def normalize(self, tensor):
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
normalize = transforms.Normalize(mean, std)
tensor = normalize(tensor)
return tensor
def get_depth_meters(self, image):
# load image and transform it in necessary batch format
self.load_image(image)
start = time.time()
with torch.no_grad():
output = self.model(self.batch) # forward pictures
self.all_time.append(time.time() - start)
start = 0
disps_pred = output[0]["disp", 0] # depth results
segs_pred = output[0]['segmentation_logits', 0] # seg results
segs_pred = segs_pred.exp().cpu()
segs_pred = segs_pred.numpy() # transform preds to np array
segs_pred = segs_pred.argmax(
1) # get the highest score for classes per pixel
depth_pred = disps_pred
depth_pred = np.array(
depth_pred[0][0].cpu()) # depth predictions to numpy and CPU
def scale_depth(disp):
min_disp = 1 / self.depth_max
max_disp = 1 / self.depth_min
return min_disp + (max_disp - min_disp) * disp
depth_pred = scale_depth(depth_pred) # Depthmap in meters
return depth_pred
def sgdepth_predict(self, image):
return self.sgdepth_model.get_depth_meters(image)
def monodepth2_predict(self, input_image):
original_width, original_height = input_image.size
input_image = input_image.resize((self.feed_width, self.feed_height),
Image.LANCZOS)
input_image = transforms.ToTensor()(input_image).unsqueeze(0)
device = torch.device(self.device)
input_image = input_image.to(device)
features = self.monodepth2_encoder(input_image)
outputs = self.monodepth2_depth_decoder(features)
disp = outputs[("disp", 0)]
disp_resized = torch.nn.functional.interpolate(
disp, (original_height, original_width),
mode="bilinear",
align_corners=False)
_, predicted_depth = disp_to_depth(disp, 0.1, 10)
predicted_depth = predicted_depth.detach().cpu().numpy()
predicted_depth = predicted_depth.squeeze()
predicted_depth = cv2.resize(predicted_depth,
(original_width, original_height))
return predicted_depth
def predict_depth(self, path):
image = Image.open(path)
original = cv2.imread(path)
# Predict with AdaBins NYU pre-trained model
adabins_nyu_prediction = self.adabins_nyu_predict(image)
adabins_nyu_max = np.max(adabins_nyu_prediction)
result_shape = adabins_nyu_prediction.shape
# Predict with AdaBins KITTI pre-trained model
adabins_kitti_prediction = self.adabins_kitti_predict(image)
adabins_kitti_max = np.max(adabins_kitti_prediction)
# Predict with DiverseDepth model
diverse_depth_prediction = self.diverse_depth_predict(image)
# Predict with MiDaS model
midas_depth_prediction = self.midas_predict(path)
midas_depth_prediction = (midas_depth_prediction -
np.max(midas_depth_prediction)) * -1
# Predict with SGDepth
sgdepth_depth_prediction = self.sgdepth_predict(image)
sgdepth_depth_prediction = 1 / sgdepth_depth_prediction
sgdepth_depth_prediction = cv2.resize(
sgdepth_depth_prediction, (result_shape[1], result_shape[0]))
# Predict with monodepth2
monodepth2_depth_prediction = self.monodepth2_predict(image)
def print_min_max(label, d):
print(label, "[" + str(np.min(d)) + ", " + str(np.max(d)) + "]")
if adabins_nyu_max <= 6: # ~19ft
# Maybe indoor scene
diverse_depth_prediction *= (adabins_nyu_max /
np.max(diverse_depth_prediction))
midas_depth_prediction *= (adabins_nyu_max /
np.max(midas_depth_prediction))
average_depth = (
adabins_nyu_prediction + diverse_depth_prediction +
midas_depth_prediction * 5 + sgdepth_depth_prediction +
monodepth2_depth_prediction) / 9
else:
# Maybe outdoor scene
diverse_depth_prediction *= (adabins_kitti_max /
np.max(diverse_depth_prediction))
midas_depth_prediction *= (adabins_nyu_max /
np.max(midas_depth_prediction))
average_depth = (
diverse_depth_prediction + midas_depth_prediction * 5 +
sgdepth_depth_prediction + monodepth2_depth_prediction) / 8
# print_min_max("Adabins NYU", adabins_nyu_prediction)
# print_min_max("Adabins KITTI", adabins_kitti_prediction)
# print_min_max("DiverseDepth", diverse_depth_prediction)
# print_min_max("MiDaS", midas_depth_prediction)
# print_min_max("SGDepth", sgdepth_depth_prediction)
# print_min_max("Monodepth2", monodepth2_depth_prediction)
# print_min_max("Average", average_depth)
# print("---------------------------------------")
return original, average_depth, colorize_depth(average_depth, 0, 20)