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# Midas Depth Estimation
# From https://github.com/isl-org/MiDaS
# MIT LICENSE
import cv2
import numpy as np
import torch
import os
import sys
current_directory = os.getcwd()
sys.path.append(current_directory)
from einops import rearrange
# from .api import MiDaSInference
from condition.utils import annotator_ckpts_path
from condition.midas.midas.dpt_depth import DPTDepthModel
from condition.midas.midas.midas_net import MidasNet
from condition.midas.midas.midas_net_custom import MidasNet_small
from condition.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
import os
import torch.nn as nn
from torchvision.transforms import Compose
ISL_PATHS = {
"dpt_large": os.path.join(annotator_ckpts_path, "dpt_large-midas-2f21e586.pt"),
"dpt_hybrid": os.path.join(annotator_ckpts_path, "dpt_hybrid-midas-501f0c75.pt"),
"midas_v21": "",
"midas_v21_small": "",
}
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def load_midas_transform(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load transform only
if model_type == "dpt_large": # DPT-Large
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
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":
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":
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:
assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
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(),
]
)
return transform
def load_model(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load network
model_path = ISL_PATHS[model_type]
model_path = 'checkpoints/dpt_hybrid-midas-501f0c75.pt'
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
if not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
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(),
]
)
return model.eval(), transform
class MiDaSInference(nn.Module):
MODEL_TYPES_TORCH_HUB = [
"DPT_Large",
"DPT_Hybrid",
"MiDaS_small"
]
MODEL_TYPES_ISL = [
"dpt_large",
"dpt_hybrid",
"midas_v21",
"midas_v21_small",
]
def __init__(self, model_type):
super().__init__()
assert (model_type in self.MODEL_TYPES_ISL)
model, _ = load_model(model_type)
self.model = model
self.model.train = disabled_train
def forward(self, x):
with torch.no_grad():
prediction = self.model(x)
return prediction
class MidasDetector:
def __init__(self,device=torch.device('cuda:0'), model_type="dpt_hybrid"):
self.device = device
self.model = MiDaSInference(model_type=model_type).to(device)
def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1):
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
image_depth = image_depth
image_depth = image_depth / 127.5 - 1.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = self.model(image_depth)[0]
depth_pt = depth.clone()
depth_pt -= torch.min(depth_pt)
depth_pt /= torch.max(depth_pt)
depth_pt = depth_pt.cpu().numpy()
depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
depth_np = depth.cpu().numpy()
x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
z = np.ones_like(x) * a
x[depth_pt < bg_th] = 0
y[depth_pt < bg_th] = 0
# normal = np.stack([x, y, z], axis=2)
# normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
# normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
return depth_image#, normal_image
if __name__ == '__main__':
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
import torchvision.transforms.functional as F
apply_depth = MidasDetector(device=torch.device('cuda:0'))
img = cv2.imread('/data/vjuicefs_sz_cv_v2/11171709/ControlAR_github/condition/example/t2i/multi_resolution/car_1_448_768.jpg')
img = cv2.resize(img,(768,448))
detected_map = apply_depth(torch.from_numpy(img).cuda().float())
print(img.shape, img.max(),img.min(),detected_map.shape, detected_map.max(),detected_map.min())
plt.imshow(detected_map, cmap='gray')
plt.show()
cv2.imwrite('condition/example_depth.jpg', detected_map)
# cv2.imwrite('condition/example_normal.jpg', normal_map)
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