controlnet_aux/midas/__init__.py

import os

import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image

from ..util import HWC3, resize_image
from .api import MiDaSInference


class MidasDetector:
 def __init__(self, model):
 self.model = model
 
 @classmethod
 def from_pretrained(cls, pretrained_model_or_path, model_type="dpt_hybrid", filename=None, cache_dir=None, local_files_only=False):
 if pretrained_model_or_path == "lllyasviel/ControlNet":
 filename = filename or "annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
 else:
 filename = filename or "dpt_hybrid-midas-501f0c75.pt"

 if os.path.isdir(pretrained_model_or_path):
 model_path = os.path.join(pretrained_model_or_path, filename)
 else:
 model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)

 model = MiDaSInference(model_type=model_type, model_path=model_path)

 return cls(model)
 

 def to(self, device):
 self.model.to(device)
 return self
 
 def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, image_resolution=512, output_type=None):
 device = next(iter(self.model.parameters())).device
 if not isinstance(input_image, np.ndarray):
 input_image = np.array(input_image, dtype=np.uint8)
 output_type = output_type or "pil"
 else:
 output_type = output_type or "np"
 
 input_image = HWC3(input_image)
 input_image = resize_image(input_image, detect_resolution)

 assert input_image.ndim == 3
 image_depth = input_image
 with torch.no_grad():
 image_depth = torch.from_numpy(image_depth).float()
 image_depth = image_depth.to(device)
 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)

 if depth_and_normal:
 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)[:, :, ::-1]
 
 depth_image = HWC3(depth_image)
 if depth_and_normal:
 normal_image = HWC3(normal_image)

 img = resize_image(input_image, image_resolution)
 H, W, C = img.shape

 depth_image = cv2.resize(depth_image, (W, H), interpolation=cv2.INTER_LINEAR)
 if depth_and_normal:
 normal_image = cv2.resize(normal_image, (W, H), interpolation=cv2.INTER_LINEAR)
 
 if output_type == "pil":
 depth_image = Image.fromarray(depth_image)
 if depth_and_normal:
 normal_image = Image.fromarray(normal_image)
 
 if depth_and_normal:
 return depth_image, normal_image
 else:
 return depth_image