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# Midas Depth Estimation
# From https://github.com/isl-org/MiDaS
# MIT LICENSE
import cv2
import numpy as np
import torch
from einops import rearrange
from .api import MiDaSInference
class MidasDetector:
def __init__(self):
# self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
self.model = MiDaSInference(model_type="dpt_hybrid").cpu()
def __call__(self, input_image):
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
# image_depth = torch.from_numpy(image_depth).float().cuda()
image_depth = torch.from_numpy(image_depth).float().cpu()
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 -= torch.min(depth)
depth /= torch.max(depth)
depth = depth.cpu().numpy()
depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
return depth_image
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