monodepthで奥行きデータを取得２

はじめに

Open Model Zoo内のDemoに格納されている、monodepth demo(Python版) を使ってみましょう。以下の記事の関連記事となります。

monodepthで奥行きデータを取得

環境

今回はmacOSで実行してみます。

MacBook Pro (13-inch, 2018, Four Thunderbolt 3 Ports)
2.7 GHz クアッドコアIntel Core i7 メモリ16 GB
macOS Big Sur 11.1
Python 3.7.7
openvino 2021.2.185

モデルの確認

models.lstを開いて、使用するモデルを確認します。fcrn-dp-nyu-depth-v2-tf、midasnetの２つが利用出来る事がわかります。モデル未入手の場合は、モデルダウンローダーを使って入手してください。（前回記事を参考）

# This file can be used with the --list option of the model downloader.
fcrn-dp-nyu-depth-v2-tf
midasnet

ヘルプの確認

% python3 monodepth_demo.py -h 
usage: monodepth_demo.py [-h] -m MODEL -i INPUT [-l CPU_EXTENSION] [-d DEVICE]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model
  -i INPUT, --input INPUT
                        Required. Path to a input image file
  -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                        Optional. Required for CPU custom layers. Absolute
                        MKLDNN (CPU)-targeted custom layers. Absolute path to
                        a shared library with the kernels implementations
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU,
                        GPU, FPGA, HDDL or MYRIAD is acceptable. Sample will
                        look for a suitable plugin for device specified.
                        Default value is CPU

実行してみる

入力画像は以下を使いました。Suzuki Bandit 250vです。

モデル：fcrn-dp-nyu-depth-v2-tf で実行

% python3 monodepth_demo.py -m /public/fcrn-dp-nyu-depth-v2-tf/FP16/fcrn-dp-nyu-depth-v2-tf.xml -i IMG_0958.jpg 
[ INFO ] creating inference engine
[ INFO ] Loading network
[ INFO ] preparing input blobs
[ INFO ] Image is resized from (768, 1024) to (228, 304)
[ INFO ] loading model to the plugin
[ INFO ] starting inference
[ INFO ] processing output blob
[ INFO ] Disparity map was saved to disp.pfm
[ INFO ] Color-coded disparity image was saved to disp.png
[ INFO ] This demo is an API example, for any performance measurements please use the dedicated benchmark_app tool from the openVINO toolkit

モデル：midasnet　で実行

% python3 monodepth_demo.py -i IMG_0958.jpg -m /public/midasnet/FP16/midasnet.xml 
[ INFO ] creating inference engine
[ INFO ] Loading network
[ INFO ] preparing input blobs
[ INFO ] Image is resized from (768, 1024) to (384, 384)
[ INFO ] loading model to the plugin
[ INFO ] starting inference
[ INFO ] processing output blob
[ INFO ] Disparity map was saved to disp.pfm
[ INFO ] Color-coded disparity image was saved to disp.png
[ INFO ] This demo is an API example, for any performance measurements please use the dedicated benchmark_app tool from the openVINO toolkit