File size: 2,870 Bytes
78360e7 |
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 |
# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
import torch
from einops import rearrange
from PIL import Image
import cv2
def convert_to_numpy(image):
if isinstance(image, Image.Image):
image = np.array(image)
elif isinstance(image, torch.Tensor):
image = image.detach().cpu().numpy()
elif isinstance(image, np.ndarray):
image = image.copy()
else:
raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
return image
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(
input_image, (W, H),
interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img, k
def resize_image_ori(h, w, image, k):
img = cv2.resize(
image, (w, h),
interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class DepthAnnotator:
def __init__(self, cfg, device=None):
from .api import MiDaSInference
pretrained_model = cfg['PRETRAINED_MODEL']
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
self.model = MiDaSInference(model_type='dpt_hybrid', model_path=pretrained_model).to(self.device)
self.a = cfg.get('A', np.pi * 2.0)
self.bg_th = cfg.get('BG_TH', 0.1)
@torch.no_grad()
@torch.inference_mode()
@torch.autocast('cuda', enabled=False)
def forward(self, image):
image = convert_to_numpy(image)
image_depth = image
h, w, c = image.shape
image_depth, k = resize_image(image_depth,
1024 if min(h, w) > 1024 else min(h, w))
image_depth = torch.from_numpy(image_depth).float().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)
depth_image = depth_image[..., None].repeat(3, 2)
depth_image = resize_image_ori(h, w, depth_image, k)
return depth_image
class DepthVideoAnnotator(DepthAnnotator):
def forward(self, frames):
ret_frames = []
for frame in frames:
anno_frame = super().forward(np.array(frame))
ret_frames.append(anno_frame)
return ret_frames |