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import os
import cv2
import numpy as np
import torch
from einops import rearrange
from PIL import Image
from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME
from .api import MiDaSInference
class MidasDetector:
def __init__(self, model):
self.model = model
self.device = "cpu"
@classmethod
def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, model_type="dpt_hybrid", filename="dpt_hybrid-midas-501f0c75.pt"):
subfolder = "annotator/ckpts" if pretrained_model_or_path == "lllyasviel/ControlNet" else ''
model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder)
model = MiDaSInference(model_type=model_type, model_path=model_path)
return cls(model)
def to(self, device):
self.model.to(device)
self.device = device
return self
def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs):
input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
image_depth = detected_map
with torch.no_grad():
image_depth = torch.from_numpy(image_depth).float()
image_depth = image_depth.to(self.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)
depth_image = remove_pad(depth_image)
if depth_and_normal:
normal_image = remove_pad(normal_image)
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
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