Github project links: https://github.com/facebookresearch/maskrcnn-benchmark
maskrcnn_benchmark installation steps:
- Installation Anaconda3, creating a virtual environment.
conda activate maskrcnn conda create -n maskrcnn python = 3 conda activate maskrcnn
- Install dependencies in a virtual environment.
conda install ipython pip install ninja yacs cython matplotlib tqdm opencv-python
- Installation PyTorch.
conda install -c pytorch pytorch-nightly torchvision cudatoolkit=9.0
- Select the installation directory.
mkdir maskrcnn export INSTALL_DIR=$PWD cd $INSTALL_DIR
- Uninstall torchvision 0.3.0, the installation torchvision 0.2.2
pip uninstall torchvision pip install torchvision==0.2.2
- Installation pycocotools.
git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python setup.py build_ext install
- Installation apex.
cd $INSTALL_DIR git clone https://github.com/NVIDIA/apex.git cd apex python setup.py install --cuda_ext --cpp_ext
- Installation maskrcnn-benchmark.
cd $INSTALL_DIR git clone https://github.com/facebookresearch/maskrcnn-benchmark.git cd maskrcnn-benchmark python setup.py build develop
unset INSTALL_DIR
maskrcnn-benchmark tests:
- Enter the demo files under maskrcnn-benchmark installation directory folder.
conda activate maskrcnn
cd maskrcnn/maskrcnn-benchmark/demo/
- New demo.py file in the demo directory.
1 from maskrcnn_benchmark.config import cfg 2 from predictor import COCODemo 3 import matplotlib.pylab as pylab 4 import matplotlib.pyplot as plt 5 import cv2 6 7 8 pylab.rcParams['figure.figsize'] = 20, 12 9 10 11 def show_image(image): 12 plt.imshow(image[:, :, [2, 1, 0]]) 13 plt.axis('off') 14 plt.show() 15 16 17 config_file = '../configs/caffe2/e2e_mask_rcnn_X-152-32x8d-FPN-IN5k_1.44x_caffe2.yaml' 18 cfg.merge_from_file(config_file) 19 20 coco_demo = COCODemo( 21 cfg, 22 confidence_threshold=0.7, 23 min_image_size=800 24 ) 25 26 img = cv2.imread('path-to-coco2014/val2014/COCO_val2014_000000000772.jpg') 27 show_image(img) 28 29 predictions = coco_demo.run_on_opencv_image(img) 30 show_image(predictions)
