File size: 3,430 Bytes
37d34c5 |
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 |
# By WASasquatch ( Discord: WAS#0263 | https://civitai.com/user/WAS )
import torch, time, sys, subprocess
import numpy as np
from PIL import Image, ImageFilter
import torchvision.transforms as transforms
MIDAS_INSTALLED = False
class MiDaS_Depth_Approx:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"use_cpu": (["false", "true"],),
"midas_model": (["DPT_Large", "DPT_Hybrid", "DPT_Small"],),
"invert_depth": (["false", "true"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "midas_approx"
CATEGORY = "WAS"
def midas_approx(self, image, use_cpu, midas_model, invert_depth):
global MIDAS_INSTALLED
if not MIDAS_INSTALLED:
self.install_midas()
import cv2 as cv
# Convert the input image tensor to a PIL Image
i = 255. * image.cpu().numpy().squeeze()
img = i
print("Downloading and loading MiDaS Model...")
midas = torch.hub.load("intel-isl/MiDaS", midas_model, trust_repo=True)
device = torch.device("cuda") if torch.cuda.is_available() and use_cpu == 'false' else torch.device("cpu")
print('MiDaS is using device:', device)
midas.to(device).eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
if midas_model == "DPT_Large" or midas_model == "DPT_Hybrid":
transform = midas_transforms.dpt_transform
else:
transform = midas_transforms.small_transform
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
input_batch = transform(img).to(device)
print('Approximating depth from image...')
with torch.no_grad():
prediction = midas(input_batch)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze()
if invert_depth == 'true':
depth = ( 255 - prediction.cpu().numpy().astype(np.uint8) )
depth = depth.astype(np.float32)
else:
depth = prediction.cpu().numpy().astype(np.float32)
depth = depth * 255 / (np.max(depth)) / 255
# Invert depth map
depth = cv.cvtColor(depth, cv.COLOR_GRAY2RGB)
tensor = torch.from_numpy( depth )[None,]
tensors = ( tensor, )
del midas, device, midas_transforms
del transform, img, input_batch, prediction
return tensors
def install_midas(self):
global MIDAS_INSTALLED
if 'timm' not in self.packages():
print("Installing timm...")
subprocess.check_call([sys.executable, '-m', 'pip', '-q', 'install', 'timm'])
if 'opencv-python' not in self.packages():
print("Installing CV2...")
subprocess.check_call([sys.executable, '-m', 'pip', '-q', 'install', 'opencv-python'])
MIDAS_INSTALLED = True
def packages(self):
import sys, subprocess
return [r.decode().split('==')[0] for r in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']).split()]
NODE_CLASS_MAPPINGS = {
"MiDaS Depth Approximation": MiDaS_Depth_Approx
}
|