CV / Depth-Anything

Depth-Anything-V2-ViTS

Depth-Anything-V2-Small (ViT-S backbone) outputs a 224x224 relative depth map. The app normalizes it, applies an INFERNO colormap, and alpha-blends onto the original frame. First variant of the depth_anything family.

2 次下载

大小

35 MB

内存

4GB+

精度

Hailo HEF / HailoRT 4.23.x

选择你正在使用的设备，设置指南和文档将相应更新。

平台

推理引擎

快速开始

部署

sudo docker run --rm \
  --name cm5-hailo8-depth-anything-v2-vits \
  --privileged \
  --net=host \
  -e PYTHONUNBUFFERED=1 \
  --device /dev/hailo0:/dev/hailo0 \
  -v /usr/lib/libhailort.so.4.23.0:/usr/lib/libhailort.so.4.23.0:ro \
  -v /usr/lib/libhailort.so:/usr/lib/libhailort.so:ro \
  ghcr.io/seeed-projects/recomputer-hailo8-cv/depth_anything_v2_vits:latest \
  python web_detection.py --model_path model/depth_anything_v2_vits.hef --video_path video/test.mp4

REST API

使用 REST API 进行推理。复制以下命令。

Curl

curl -X POST "http://<Board_IP>:8000/api/models/depth_anything_v2_vits/predict" \
  -F "file=@test.jpg"

Python

import requests
response = requests.post(
    "http://<Board_IP>:8000/api/models/depth_anything_v2_vits/predict",
    files={"file": open("test.jpg", "rb")},
    timeout=30,
)
print(response.json())

模型详情

Depth-Anything-V2-ViTS on reComputer R Series (CM5 + Hailo-8)

Depth-Anything-V2-Small (ViT-S backbone) performs zero-shot monocular depth estimation on Hailo-8. The HEF outputs a 224×224×1 relative depth map; the app normalizes it, applies an INFERNO colormap, and alpha-blends onto the original frame.

Model Info

Property	Value
Architecture	ViT-S (Depth-Anything-V2-Small-hf)
Task	Zero-shot depth estimation
Input	224x224x3 RGB
Output	Depth map 224x224x1 (relative)
Parameters	24.2M
Operations	16.7G
AbsRel	0.147
HEF	Hailo Model Zoo v2.19.0, Hailo-8

Run With Demo Video

bash

sudo docker run --rm \
  --name cm5-hailo8-depth-anything-v2-vits \
  --privileged \
  --net=host \
  -e PYTHONUNBUFFERED=1 \
  --device /dev/hailo0:/dev/hailo0 \
  -v /usr/lib/libhailort.so.4.23.0:/usr/lib/libhailort.so.4.23.0:ro \
  -v /usr/lib/libhailort.so:/usr/lib/libhailort.so:ro \
  ghcr.io/seeed-projects/recomputer-hailo8-cv/depth_anything_v2_vits:latest \
  python web_detection.py --model_path model/depth_anything_v2_vits.hef --video_path video/test.mp4

Open http://<Board_IP>:8000 to view the depth overlay.

REST API

bash

curl -X POST "http://<Board_IP>:8000/api/models/depth_anything_v2_vits/predict" \
  -F "file=@test.jpg"

Returns depth_stats (min, max, mean, std) for the predicted depth map.

Endpoint	Method	Purpose
`/api/models/depth_anything_v2_vits/predict`	POST	Depth statistics (JSON)
`/api/video_feed`	GET	MJPEG with INFERNO depth overlay

Implementation Notes

Relative depth: output is per-frame min-max normalized (not metric). Higher = farther. Dark (INFERNO) = close, bright = far.
Slider controls alpha blend between original frame and depth colormap.
normalize_in_net ImageNet RGB; no input_conversion → feed raw uint8 RGB.
No on-chip post-processing — raw FLOAT32 depth map output.

Development Notes

Source: src/rpi5_hailo8_depth_anything_v2_vits/
Container: ghcr.io/seeed-projects/recomputer-hailo8-cv/depth_anything_v2_vits:latest
Family: depth_anything (variants v2_vits / vits)

输入与输出

Input: image, video, or USB camera frame. Output: relative depth map with INFERNO colormap overlay on MJPEG preview, plus depth statistics (min/max/mean/std) via API.