Mensoft

.gitignore

@@ -0,0 +1,5 @@
+checkpoints
+venv
+data
+session_data
+/ByteTrack

.ipynb

@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "178e912b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is CUDA available? True\n",
+      "GPU device count: 1\n",
+      "GPU device name: NVIDIA GeForce RTX 3060\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "print(f\"Is CUDA available? {torch.cuda.is_available()}\")\n",
+    "\n",
+    "# If the above is True, these will give more details:\n",
+    "if torch.cuda.is_available():\n",
+    "  print(f\"GPU device count: {torch.cuda.device_count()}\")\n",
+    "  print(f\"GPU device name: {torch.cuda.get_device_name(0)}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

@@ -1,201 +1,24 @@
-<div align="center">
-<h1>Depth Anything V2</h1>
+🚀 Instrucciones para Ejecutar el Proyecto
+1️⃣ Crear un entorno virtual (recomendado)
+bash
 
-[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://bingykang.github.io/)<sup>2&dagger;</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup>
-<br>
-[**Zhen Zhao**](http://zhaozhen.me/) · [**Xiaogang Xu**](https://xiaogang00.github.io/) · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1*</sup>
+python -m venv .venv
 
-<sup>1</sup>HKU&emsp;&emsp;&emsp;<sup>2</sup>TikTok
-<br>
-&dagger;project lead&emsp;*corresponding author
+Activa el entorno virtual:
 
-<a href="https://arxiv.org/abs/2406.09414"><img src='https://img.shields.io/badge/arXiv-Depth Anything V2-red' alt='Paper PDF'></a>
-<a href='https://depth-anything-v2.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything V2-green' alt='Project Page'></a>
-<a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-V2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
-<a href='https://huggingface.co/datasets/depth-anything/DA-2K'><img src='https://img.shields.io/badge/Benchmark-DA--2K-yellow' alt='Benchmark'></a>
-</div>
+En Windows:
+.venv\Scripts\activate
 
-This work presents Depth Anything V2. It significantly outperforms [V1](https://github.com/LiheYoung/Depth-Anything) in fine-grained details and robustness. Compared with SD-based models, it enjoys faster inference speed, fewer parameters, and higher depth accuracy.
+2️⃣ Instalar dependencias
 
-![teaser](assets/teaser.png)
-
-
-## News
-- **2025-01-22:** [Video Depth Anything](https://videodepthanything.github.io) has been released. It generates consistent depth maps for super-long videos (e.g., over 5 minutes).
-- **2024-12-22:** [Prompt Depth Anything](https://promptda.github.io/) has been released. It supports 4K resolution metric depth estimation when low-res LiDAR is used to prompt the DA models.
-- **2024-07-06:** Depth Anything V2 is supported in [Transformers](https://github.com/huggingface/transformers/). See the [instructions](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2) for convenient usage.
-- **2024-06-25:** Depth Anything is integrated into [Apple Core ML Models](https://developer.apple.com/machine-learning/models/). See the instructions ([V1](https://huggingface.co/apple/coreml-depth-anything-small), [V2](https://huggingface.co/apple/coreml-depth-anything-v2-small)) for usage.
-- **2024-06-22:** We release [smaller metric depth models](https://github.com/DepthAnything/Depth-Anything-V2/tree/main/metric_depth#pre-trained-models) based on Depth-Anything-V2-Small and Base.
-- **2024-06-20:** Our repository and project page are flagged by GitHub and removed from the public for 6 days. Sorry for the inconvenience.
-- **2024-06-14:** Paper, project page, code, models, demo, and benchmark are all released.
-
-
-## Pre-trained Models
-
-We provide **four models** of varying scales for robust relative depth estimation:
-
-| Model | Params | Checkpoint |
-|:-|-:|:-:|
-| Depth-Anything-V2-Small | 24.8M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Small/resolve/main/depth_anything_v2_vits.pth?download=true) |
-| Depth-Anything-V2-Base | 97.5M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth?download=true) |
-| Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) |
-| Depth-Anything-V2-Giant | 1.3B | Coming soon |
-
-
-## Usage
-
-### Prepraration
-
-```bash
-git clone https://github.com/DepthAnything/Depth-Anything-V2
-cd Depth-Anything-V2
 pip install -r requirements.txt
-```
-
-Download the checkpoints listed [here](#pre-trained-models) and put them under the `checkpoints` directory.
-
-### Use our models
-```python
-import cv2
-import torch
-
-from depth_anything_v2.dpt import DepthAnythingV2
-
-DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
-
-model_configs = {
-    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
-    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
-    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
-    'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
-}
-
-encoder = 'vitl' # or 'vits', 'vitb', 'vitg'
-
-model = DepthAnythingV2(**model_configs[encoder])
-model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location='cpu'))
-model = model.to(DEVICE).eval()
-
-raw_img = cv2.imread('your/image/path')
-depth = model.infer_image(raw_img) # HxW raw depth map in numpy
-```
-
-If you do not want to clone this repository, you can also load our models through [Transformers](https://github.com/huggingface/transformers/). Below is a simple code snippet. Please refer to the [official page](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2) for more details.
-
-- Note 1: Make sure you can connect to Hugging Face and have installed the latest Transformers.
-- Note 2: Due to the [upsampling difference](https://github.com/huggingface/transformers/pull/31522#issuecomment-2184123463) between OpenCV (we used) and Pillow (HF used), predictions may differ slightly. So you are more recommended to use our models through the way introduced above.
-```python
-from transformers import pipeline
-from PIL import Image
-
-pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf")
-image = Image.open('your/image/path')
-depth = pipe(image)["depth"]
-```
-
-### Running script on *images*
-
-```bash
-python run.py \
-  --encoder <vits | vitb | vitl | vitg> \
-  --img-path <path> --outdir <outdir> \
-  [--input-size <size>] [--pred-only] [--grayscale]
-```
-Options:
-- `--img-path`: You can either 1) point it to an image directory storing all interested images, 2) point it to a single image, or 3) point it to a text file storing all image paths.
-- `--input-size` (optional): By default, we use input size `518` for model inference. ***You can increase the size for even more fine-grained results.***
-- `--pred-only` (optional): Only save the predicted depth map, without raw image.
-- `--grayscale` (optional): Save the grayscale depth map, without applying color palette.
-
-For example:
-```bash
-python run.py --encoder vitl --img-path assets/examples --outdir depth_vis
-```
-
-### Running script on *videos*
-
-```bash
-python run_video.py \
-  --encoder <vits | vitb | vitl | vitg> \
-  --video-path assets/examples_video --outdir video_depth_vis \
-  [--input-size <size>] [--pred-only] [--grayscale]
-```
-
-***Our larger model has better temporal consistency on videos.***
-
-### Gradio demo
-
-To use our gradio demo locally:
-
-```bash
-python app.py
-```
-
-You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/Depth-Anything-V2).
-
-***Note: Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)).*** In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use [intermediate features](https://github.com/DepthAnything/Depth-Anything-V2/blob/2cbc36a8ce2cec41d38ee51153f112e87c8e42d8/depth_anything_v2/dpt.py#L164-L169) instead. Although this modification did not improve details or accuracy, we decided to follow this common practice.
-
-
-## Fine-tuned to Metric Depth Estimation
-
-Please refer to [metric depth estimation](./metric_depth).
-
-
-## DA-2K Evaluation Benchmark
-
-Please refer to [DA-2K benchmark](./DA-2K.md).
-
-
-## Community Support
-
-**We sincerely appreciate all the community support for our Depth Anything series. Thank you a lot!**
-
-- Apple Core ML:
-    - https://developer.apple.com/machine-learning/models
-    - https://huggingface.co/apple/coreml-depth-anything-v2-small
-    - https://huggingface.co/apple/coreml-depth-anything-small
-- Transformers:
-    - https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2
-    - https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything
-- TensorRT:
-    - https://github.com/spacewalk01/depth-anything-tensorrt
-    - https://github.com/zhujiajian98/Depth-Anythingv2-TensorRT-python
-- ONNX: https://github.com/fabio-sim/Depth-Anything-ONNX
-- ComfyUI: https://github.com/kijai/ComfyUI-DepthAnythingV2
-- Transformers.js (real-time depth in web): https://huggingface.co/spaces/Xenova/webgpu-realtime-depth-estimation
-- Android:
-  - https://github.com/shubham0204/Depth-Anything-Android
-  - https://github.com/FeiGeChuanShu/ncnn-android-depth_anything
-
-
-## Acknowledgement
-
-We are sincerely grateful to the awesome Hugging Face team ([@Pedro Cuenca](https://huggingface.co/pcuenq), [@Niels Rogge](https://huggingface.co/nielsr), [@Merve Noyan](https://huggingface.co/merve), [@Amy Roberts](https://huggingface.co/amyeroberts), et al.) for their huge efforts in supporting our models in Transformers and Apple Core ML.
-
-We also thank the [DINOv2](https://github.com/facebookresearch/dinov2) team for contributing such impressive models to our community.
-
-
-## LICENSE
-
-Depth-Anything-V2-Small model is under the Apache-2.0 license. Depth-Anything-V2-Base/Large/Giant models are under the CC-BY-NC-4.0 license.
 
+3️⃣ Levantar el servidor backend (API) con Uvicorn
 
-## Citation
+uvicorn api:app --host 0.0.0.0 --port 8000 --reload
 
-If you find this project useful, please consider citing:
+🔹 Cambia api:app por el módulo y objeto correctos según tu estructura.
 
-```bibtex
-@article{depth_anything_v2,
-  title={Depth Anything V2},
-  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-  journal={arXiv:2406.09414},
-  year={2024}
-}
+4️⃣ Iniciar la aplicación Streamlit
 
-@inproceedings{depth_anything_v1,
-  title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
-  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-  booktitle={CVPR},
-  year={2024}
-}
-```
+streamlit run dashboard.py --server.port 8501

backend/check_gpu.py

@@ -0,0 +1,9 @@
+# check_gpu.py
+import torch
+print("--- Running GPU Check ---")
+print(f"Is CUDA available? {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    print(f"Device name: {torch.cuda.get_device_name(0)}")
+else:
+    print("CUDA not found by this Python interpreter.")
+print("-------------------------")