https://github.com/cedrickchee/realtime-detectron
Real-time Detectron using webcam.
https://github.com/cedrickchee/realtime-detectron
deeplearning demo detectron mask-rcnn object-detection object-segmentation real-time webcam
Last synced: about 2 months ago
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Real-time Detectron using webcam.
- Host: GitHub
- URL: https://github.com/cedrickchee/realtime-detectron
- Owner: cedrickchee
- Created: 2018-02-08T03:55:38.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-06T07:52:21.000Z (about 7 years ago)
- Last Synced: 2025-05-07T20:38:43.408Z (5 months ago)
- Topics: deeplearning, demo, detectron, mask-rcnn, object-detection, object-segmentation, real-time, webcam
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 40
- Watchers: 2
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Real-time Detectron
This is a demo project of a real-time Mask R-CNN using [Detectron](https://github.com/facebookresearch/Detectron). We will be using consumer grade webcam for capturing the video stream.
Here's an [example of demo](https://www.reddit.com/r/MachineLearning/comments/7vuqvc/p_realtime_mask_rcnn_using_facebook_detectron/) created by reddit's user `_sshin_`.
**Project Status:** Early release. Still in heavy development. What this means is, things might be moved around quickly and things will break.
## Introduction
Detectron is Facebook AI Research (FAIR)'s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
In this project, we have implemented a simple solution to run Detectron Mask R-CNN algorithm with webcam. We are using Mask R-CNN for object detection and instance segmentation.
## Installation
Runtime requirements:
* Python 2.7
* Note: [Python 3 is not supported by Detectron yet](https://github.com/facebookresearch/Detectron/issues/85)
* Ubuntu Linux
* Tested in Ubuntu 16.04 LTS.
* CUDA 8 and above
* Tested with CUDA 8 and CUDA 9.
* cuDNN 6 and above
* OpenCV 3.4
* Python dependencies
* Detectron
* Caffe2 (Detectron is powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework)
### Installing Detectron
How to install Detectron and its dependencies (including Caffe2). Please refer to this [guide](https://github.com/facebookresearch/Detectron/blob/master/INSTALL.md).
## Quick Start: Using Detectron
After installation, please see the following for brief instructions covering inference with Detectron.
## Inference with Pretrained Models
#### Directory of Image Files
To run inference on a directory of image files (`demo/*.jpg` in this example), you can use the `inference.py` tool. In this example, we're using an end-to-end trained Mask R-CNN model with a ResNet-101-FPN backbone from the model zoo:
First, place `inference.py` file in Detectron `tools` directory and `visualize.py` file in Detectron `lib/utils` directory. Then, run the following command:
```
python2 tools/infererence.py \
--cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml \
--output-dir /tmp/detectron-visualizations \
--image-ext jpg \
--wts https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl \
demo
```
Detectron should automatically download the model from the URL specified by the `--wts` argument. This tool will output visualizations of the detections in the directory specified by `--output-dir`.
**Notes:**
The code used for this demo in `inference.py` is the same as the one mentioned in [Detectron documentation](https://github.com/facebookresearch/Detectron/blob/master/GETTING_STARTED.md#1-directory-of-image-files) except with a few modifications to support webcam as input.
## Credits
Referenced these implementations:
1. [Demo with Mask R-CNN in Google Colab with GPU acceleration](https://github.com/OSSDC/OSSDC-VisionBasedACC/blob/master/image-segmentation/ossdc_matterport_Mask_RCNN_colaboratory.ipynb)