A multimedia query processing engine
pip install -r requirements.txt
For the full basic demo of the current functionality run:
python run.py --is_debug=TrueThis is a depth prediction from a single RGB image.
We used the state-of-the-art image depth prediction described in the paper Deeper Depth Prediction with Fully Convolutional Residual Networks. The code for depth prediction is based on their provided implementation.
The provided model was used to obtain the results reported in the paper on the benchmark datasets NYU Depth v2 and Make3D for indoor and outdoor scenes respectively.
Such a trained model can be used for inference on arbitrary images.
# go to depth_prediction folder:
cd depth_prediction
# get depth map for a single image
PYTHONPATH=../ python predictor.py --image_path=../resources/demo/image.jpg
# get depth maps for all images in the given directory
PYTHONPATH=../ python predictor.py --image_path=../resources/demo/Example of a resized input image and the output depth map (the input image and the depth map were scaled equally for better comparison):
Download the pre-trained model to the following folder:
deeplens/object_detection/darknet/cfg/
for YOLO v3 from:
wget https://pjreddie.com/media/files/yolov3.weights
Download the pre-trained model to the following folder:
deeplens/object_detection/pytorch_ssd/models
for pytorch_ssd from:
wget https://storage.googleapis.com/models-hao/vgg16-ssd-mp-0_7726.pth