私的AI研究会 > YOLOv7
物体認識のタスク YOLO の新しい版である YOLO v7 を試してみる。
プロジェクト・ホーム（c:/anaconda_win または ~/）
├─Images ← 画像データ
├─model ← 学習済みモデル
├─Videos ← 動画データ
│ :
├─work ← 今回使用するプロジェクト・ディレクトリ
│ ├─openvino
│ └─yolov7
├─workspace_py37
│ └─mylib ← python 共有ライブラリ (パスが通っていること)
└─workspace_py38 ← anaconda 環境下のアプリケーション・プロジェクト
├─ :
└─stable_diffusion| コマンド | 内容 |
| pip list | インストール済みパッケージ名とバージョン一覧 |
| pip freeze | インストール済みパッケージ名とバージョン一覧（パッケージ管理除外） |
| pip install <パッケージ名> | パッケージのインストール |
| pip install <パッケージ名>==<バージョン> | バージョンを指定してインストール （バージョンを省略するとインストール可能なバージョン表示） |
| pip install -r requirements.txt | パッケージリストファイルに従ってインストール |
| pip uninstall <パッケージ名> | インストール済みパッケージのアンインストール |
| pip uninstall <パッケージ名> <パッケージ名> ... | 複数インストール済みパッケージのアンインストール |
| コマンド | 内容 |
| conda update -n base conda | conda のアップデート |
| conda create -n py37 python=3.7 | 仮想環境の作成(python versionを指定) |
| conda env create -f env1.yaml | 仮想環境の作成(yamlファイルの読み込み) |
| conda create -n 新しい環境名 --clone 複製したいベースの環境名 | 仮想環境を複製 |
| conda info -e | 作成した仮想環境一覧の表示 |
| conda activate py37 | 作成した仮想環境に切り替える |
| conda deactivate | 現在の仮想環境から出る |
| conda env remove -n py37 | 仮想環境の削除 |
| conda remove —-all | すべての仮想環境の削除 |
| conda env export > env1.yaml | 現在の仮想環境の設定ファイルを書き出す |
| conda list | 現在の仮想環境のパッケージを確認 |
(py38) PS C:\anaconda_win\work> conda info -e
(py38) PS C:\anaconda_win\workspace_py38> cd ..\work\
(py38) PS C:\anaconda_win\work> ls ← 今回のプロジェクト・フォルダ
ディレクトリ: N:\anaconda_win\work
Mode LastWriteTime Length Name
---- ------------- ------ ----
d----- 2023/04/13 4:39 openvino
d----- 2023/04/13 4:39 yolov7
------ 2023/04/10 13:36 294 requirements.txt
(py38) PS C:\anaconda_win\work> conda info -e
# conda environments:
#
base C:\Users\(xxxxx)\anaconda3
:
py37 C:\Users\(xxxxx)\anaconda3\envs\py37
py38 * C:\Users\(xxxxx)\anaconda3\envs\py38 ← 現在アクティブな仮想環境
(py38) PS C:\anaconda_win\work> conda create -n py38a python=3.8
(py38) PS C:\anaconda_win\work> conda create -n py38a python=3.8
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.13.0
latest version: 23.3.1
Please update conda by running
$ conda update -n base -c defaults conda ← このメッセージが出たら後で「anaconda」をアップデートする
## Package Plan ##
environment location: C:\Users\izuts\anaconda3\envs\py38a
added / updated specs:
- python=3.8
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2023.01.10 | haa95532_0 121 KB
certifi-2022.12.7 | py38haa95532_0 148 KB
libffi-3.4.2 | hd77b12b_6 109 KB
openssl-1.1.1t | h2bbff1b_0 5.5 MB
pip-23.0.1 | py38haa95532_0 2.7 MB
python-3.8.16 | h6244533_3 18.9 MB
setuptools-65.6.3 | py38haa95532_0 1.1 MB
sqlite-3.41.1 | h2bbff1b_0 897 KB
wheel-0.38.4 | py38haa95532_0 83 KB
------------------------------------------------------------
Total: 29.6 MB
The following NEW packages will be INSTALLED:
ca-certificates pkgs/main/win-64::ca-certificates-2023.01.10-haa95532_0
certifi pkgs/main/win-64::certifi-2022.12.7-py38haa95532_0
libffi pkgs/main/win-64::libffi-3.4.2-hd77b12b_6
openssl pkgs/main/win-64::openssl-1.1.1t-h2bbff1b_0
pip pkgs/main/win-64::pip-23.0.1-py38haa95532_0
python pkgs/main/win-64::python-3.8.16-h6244533_3
setuptools pkgs/main/win-64::setuptools-65.6.3-py38haa95532_0
sqlite pkgs/main/win-64::sqlite-3.41.1-h2bbff1b_0
vc pkgs/main/win-64::vc-14.2-h21ff451_1
vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2
wheel pkgs/main/win-64::wheel-0.38.4-py38haa95532_0
wincertstore pkgs/main/win-64::wincertstore-0.2-py38haa95532_2
Proceed ([y]/n)?
Downloading and Extracting Packages
python-3.8.16 | 18.9 MB | #################################### | 100%
wheel-0.38.4 | 83 KB | #################################### | 100%
pip-23.0.1 | 2.7 MB | #################################### | 100%
libffi-3.4.2 | 109 KB | #################################### | 100%
sqlite-3.41.1 | 897 KB | #################################### | 100%
ca-certificates-2023 | 121 KB | #################################### | 100%
setuptools-65.6.3 | 1.1 MB | #################################### | 100%
certifi-2022.12.7 | 148 KB | #################################### | 100%
openssl-1.1.1t | 5.5 MB | #################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate py38a
#
# To deactivate an active environment, use
#
# $ conda deactivate
(py38) PS C:\anaconda_win\work> conda activate py38a
(py38) PS C:\anaconda_win\work> conda activate py38a
(py38a) PS C:\anaconda_win\work> conda list
# packages in environment at C:\Users\izuts\anaconda3\envs\py38a:
#
# Name Version Build Channel
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 py38haa95532_0
libffi 3.4.2 hd77b12b_6
openssl 1.1.1t h2bbff1b_0
pip 23.0.1 py38haa95532_0
python 3.8.16 h6244533_3
setuptools 65.6.3 py38haa95532_0
sqlite 3.41.1 h2bbff1b_0
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
wheel 0.38.4 py38haa95532_0
wincertstore 0.2 py38haa95532_2
(py38a) PS N:\anaconda_win\work> conda info -e
# conda environments:
#
base C:\Users\izuts\anaconda3
:
py37 C:\Users\izuts\anaconda3\envs\py37
py38 C:\Users\izuts\anaconda3\envs\py38
py38a * C:\Users\izuts\anaconda3\envs\py38a ← 現在アクティブな仮想環境
● OpenVINO™ランタイムを使用して「Stable Diffusion」と「YOLOv7」が動作するようにする。
「requirements.txt」
# Stable Diffusion & YOLOv7_OpenVINO openvino==2022.1.0 numpy==1.19.5 opencv-python==4.5.5.64 transformers==4.16.2 diffusers==0.2.4 tqdm==4.64.0 huggingface_hub==0.9.0 scipy==1.9.0 streamlit==1.12.0 watchdog==2.1.9 ftfy==6.1.1 PyMuPDF torchvision matplotlib seaborn onnx googletrans==4.0.0-rc1
(py38a) PS C:\anaconda_win\work> pip install -r requirements.txt
(py38a) PS C:\anaconda_win\work> pip install -r requirements.txt
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Installing collected packages: wcwidth, tokenizers, rfc3986, pytz, mpmath, hyperframe, hpack, h11, chardet, zipp, watchdog, urllib3, tzdata, typing-extensions, tqdm, tornado, toolz, toml, sympy, sniffio, smmap, semver, regex, pyyaml, python-dateutil, pyrsistent, pyparsing, PyMuPDF, pympler, pygments, protobuf, pkgutil-resolve-name, Pillow, packaging, numpy, networkx, mdurl, MarkupSafe, kiwisolver, joblib, idna, hstspreload, h2, ftfy, fonttools, filelock, entrypoints, decorator, cycler, click, charset-normalizer, cachetools, backports.zoneinfo, attrs, validators, scipy, sacremoses, requests, pytz-deprecation-shim, pyarrow, pandas, openvino, opencv-python, onnx, markdown-it-py, jinja2, importlib-resources, importlib-metadata, httpcore, gitdb, contourpy, blinker, tzlocal, torch, rich, pydeck, matplotlib, jsonschema, huggingface_hub, httpx, gitpython, transformers, torchvision, seaborn, googletrans, diffusers, altair, streamlit
Successfully installed MarkupSafe-2.1.2 Pillow-9.5.0 PyMuPDF-1.21.1 altair-4.2.2 attrs-22.2.0 backports.zoneinfo-0.2.1 blinker-1.6.1 cachetools-5.3.0 chardet-3.0.4 charset-normalizer-3.1.0 click-8.1.3 contourpy-1.0.7 cycler-0.11.0 decorator-5.1.1 diffusers-0.2.4 entrypoints-0.4 filelock-3.11.0 fonttools-4.39.3 ftfy-6.1.1 gitdb-4.0.10 gitpython-3.1.31 googletrans-4.0.0rc1 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2023.1.1 httpcore-0.9.1 httpx-0.13.3 huggingface_hub-0.9.0 hyperframe-5.2.0 idna-2.10 importlib-metadata-6.3.0 importlib-resources-5.12.0 jinja2-3.1.2 joblib-1.2.0 jsonschema-4.17.3 kiwisolver-1.4.4 markdown-it-py-2.2.0 matplotlib-3.6.3 mdurl-0.1.2 mpmath-1.3.0 networkx-3.1 numpy-1.19.5 onnx-1.13.1 opencv-python-4.5.5.64 openvino-2022.1.0 packaging-23.1 pandas-1.4.4 pkgutil-resolve-name-1.3.10 protobuf-3.20.3 pyarrow-11.0.0 pydeck-0.8.0 pygments-2.15.0 pympler-1.0.1 pyparsing-3.0.9 pyrsistent-0.19.3 python-dateutil-2.8.2 pytz-2023.3 pytz-deprecation-shim-0.1.0.post0 pyyaml-6.0 regex-2023.3.23 requests-2.28.2 rfc3986-1.5.0 rich-13.3.4 sacremoses-0.0.53 scipy-1.9.0 seaborn-0.12.2 semver-3.0.0 smmap-5.0.0 sniffio-1.3.0 streamlit-1.12.0 sympy-1.11.1 tokenizers-0.13.3 toml-0.10.2 toolz-0.12.0 torch-2.0.0 torchvision-0.15.1 tornado-6.2 tqdm-4.64.0 transformers-4.16.2 typing-extensions-4.5.0 tzdata-2023.3 tzlocal-4.3 urllib3-1.26.15 validators-0.20.0 watchdog-2.1.9 wcwidth-0.2.6 zipp-3.15.0
(py38a) PS C:\anaconda_win\work> pip list
(py38a) PS C:\anaconda_win\work> pip list Package Version --------------------- ----------- altair 4.2.2 astroid 2.3.3 attrs 22.2.0 backports.zoneinfo 0.2.1 blinker 1.6.1 cachetools 5.3.0 certifi 2022.12.7 chardet 3.0.4 charset-normalizer 3.1.0 click 8.1.3 colorama 0.4.3 contourpy 1.0.7 cycler 0.11.0 decorator 5.1.1 diffusers 0.2.4 entrypoints 0.4 filelock 3.11.0 fonttools 4.39.3 ftfy 6.1.1 gitdb 4.0.10 GitPython 3.1.31 googletrans 4.0.0rc1 h11 0.9.0 h2 3.2.0 hpack 3.0.0 hstspreload 2023.1.1 httpcore 0.9.1 httpx 0.13.3 huggingface-hub 0.9.0 hyperframe 5.2.0 idna 2.10 importlib-metadata 6.3.0 importlib-resources 5.12.0 isort 4.3.21 Jinja2 3.1.2 joblib 1.2.0 jsonschema 4.17.3 kiwisolver 1.4.4 lazy-object-proxy 1.4.3 markdown-it-py 2.2.0 MarkupSafe 2.1.2 matplotlib 3.6.3 mccabe 0.6.1 mdurl 0.1.2 mpmath 1.3.0 networkx 3.1 numpy 1.19.5 onnx 1.13.1 opencv-python 4.5.5.64 openvino 2022.1.0 packaging 23.1 pandas 1.4.4 Pillow 9.5.0 pip 23.0.1 pkgutil_resolve_name 1.3.10 protobuf 3.20.3 pyarrow 11.0.0 pydeck 0.8.0 Pygments 2.15.0 pylint 2.4.4 Pympler 1.0.1 PyMuPDF 1.21.1 pyparsing 3.0.9 pyrsistent 0.19.3 python-dateutil 2.8.2 pytz 2023.3 pytz-deprecation-shim 0.1.0.post0 PyYAML 6.0 regex 2023.3.23 requests 2.28.2 rfc3986 1.5.0 rich 13.3.4 sacremoses 0.0.53 scipy 1.9.0 seaborn 0.12.2 semver 3.0.0 setuptools 65.6.3 six 1.14.0 smmap 5.0.0 sniffio 1.3.0 streamlit 1.12.0 sympy 1.11.1 tokenizers 0.13.3 toml 0.10.2 toolz 0.12.0 torch 2.0.0 torchvision 0.15.1 tornado 6.2 tqdm 4.64.0 transformers 4.16.2 typing_extensions 4.5.0 tzdata 2023.3 tzlocal 4.3 urllib3 1.26.15 validators 0.20.0 watchdog 2.1.9 wcwidth 0.2.6 wheel 0.38.4 wincertstore 0.2 wrapt 1.11.2 zipp 3.15.0
(py38a) PS C:\anaconda_win\work> cd ../workspace_py38/stable_diffusion (py38a) PS C:\anaconda_win\workspace_py38\stable_diffusion> python demo.py --prompt "Street-art painting of Tom Cruise in style of Gogh, photorealism" 32it [04:03, 7.61s/it]↑ -- 1回あたりの演算速度（秒）
(py38a) PS C:\anaconda_win\workspace_py38\stable_diffusion> python stable_diffusion2.py Starting.. - Program title : Stable Diffusion2 OpenVINO™ Ver 0.02 - OpenVINO engine: 2022.1.0-7019-cdb9bec7210-releases/2022/1 - OpenCV version : 4.5.5 - Log level : 3 - Output dir : result/ - Output header : output_ - model : bes-dev/stable-diffusion-v1-4-openvino - beta_start : 0.00085 - beta_end : 0.012 - beta_schedule : scaled_linear - eta : 0.0 - tokenizer : openai/clip-vit-large-patch14 Prompt: Beautiful green forest where light shines (和訳): 光が輝く美しい緑の森 ** start 0 ** 4029269746 32it [03:26, 6.46s/it] -Output-: result/output_4029269746.png ** end ** 00:03:49 Finished.
/work
├─openvino
├─yolov7
├─YOLOv7_OpenVINO_cpp-python-main
└─yolov7-main
├─ :
└─yolo7.pt(py38a) PS > cd /anaconda_win/work/yolov7-main (py38a) PS > python export.py --weights yolov7.pt
(py38a) PS > cd /anaconda_win/work/yolov7-main (py38a) PS > python export.py --weights yolov7.pt Import onnx_graphsurgeon failure: No module named 'onnx_graphsurgeon' Namespace(batch_size=1, conf_thres=0.25, device='cpu', dynamic=False, dynamic_batch=False, end2end=False, fp16=False, grid=False, img_size=[640, 640], include_nms=False, int8=False, iou_thres=0.45, max_wh=None, simplify=False, topk_all=100, weights='yolov7.pt') YOLOR 2023-4-13 torch 2.0.0+cpu CPU Fusing layers... RepConv.fuse_repvgg_block RepConv.fuse_repvgg_block RepConv.fuse_repvgg_block Model Summary: 306 layers, 36905341 parameters, 36905341 gradients Starting TorchScript export with torch 2.0.0+cpu... TorchScript export success, saved as yolov7.torchscript.pt CoreML export failure: No module named 'coremltools' Starting TorchScript-Lite export with torch 2.0.0+cpu... TorchScript-Lite export success, saved as yolov7.torchscript.ptl Starting ONNX export with onnx 1.13.1... N:\anaconda_win\work\yolov7-main\models\yolo.py:582: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if augment: N:\anaconda_win\work\yolov7-main\models\yolo.py:614: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if profile: N:\anaconda_win\work\yolov7-main\models\yolo.py:629: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if profile: ============== Diagnostic Run torch.onnx.export version 2.0.0+cpu ============== verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ======================== ONNX export success, saved as yolov7.onnx Export complete (27.27s). Visualize with https://github.com/lutzroeder/netron.
(py38a) PS > cd /anaconda_win/work/YOLOv7_OpenVINO_cpp-python-main/ (py38a) PS > python python/image.py -m yolov7.onnx -i data/horses.jpg -p
(py38a) PS C:\anaconda_win\work\YOLOv7_OpenVINO_cpp-python-main> python python/image.py -m yolov7.onnx -i data/horses.jpg -p
Dump preprocessor: Input "images" (color BGR):
User's input tensor: {1,640,640,3}, [N,H,W,C], f32
Model's expected tensor: {1,3,640,640}, [N,C,H,W], f32
Pre-processing steps (2):
convert color (RGB): ({1,640,640,3}, [N,H,W,C], f32, BGR) -> ({1,640,640,3}, [N,H,W,C], f32, RGB)
scale (255,255,255): ({1,640,640,3}, [N,H,W,C], f32, RGB) -> ({1,640,640,3}, [N,H,W,C], f32, RGB)
Implicit pre-processing steps (1):
convert layout [N,C,H,W]: ({1,640,640,3}, [N,H,W,C], f32, RGB) -> ({1,3,640,640}, [N,C,H,W], f32, RGB)
#br
(py38a) PS > cd /anaconda_win/work/YOLOv7_OpenVINO_cpp-python-main/ (py38a) PS > python python/webcam.py -m yolov7.onnx -i 0 -p
(py38a) PS > cd /anaconda_win/work/YOLOv7_OpenVINO_cpp-python-main/
(py38a) PS > python python/webcam.py -m yolov7.onnx -i 0 -p
Dump preprocessor: Input "images" (color BGR):
User's input tensor: {1,640,640,3}, [N,H,W,C], f32
Model's expected tensor: {1,3,640,640}, [N,C,H,W], f32
Pre-processing steps (2):
convert color (RGB): ({1,640,640,3}, [N,H,W,C], f32, BGR) -> ({1,640,640,3}, [N,H,W,C], f32, RGB)
scale (255,255,255): ({1,640,640,3}, [N,H,W,C], f32, RGB) -> ({1,640,640,3}, [N,H,W,C], f32, RGB)
Implicit pre-processing steps (1):
convert layout [N,C,H,W]: ({1,640,640,3}, [N,H,W,C], f32, RGB) -> ({1,3,640,640}, [N,C,H,W], f32, RGB)
Traceback (most recent call last):
File "python/webcam.py", line 24, in <module>
yolov7_detector.infer_cam(args.input)
File "N:\anaconda_win\work\YOLOv7_OpenVINO_cpp-python-main\python\yolov7.py", line 262, in infer_cam
self.infer_queue.start_async({self.input_layer.any_name: img_batch}, (src_img_list, src_size))
File "C:\Users\izuts\anaconda3\envs\py38a\lib\site-packages\openvino\runtime\ie_api.py", line 241, in start_async
inputs, get_input_types(self[self.get_idle_request_id()])
KeyboardInterrupt
[ WARN:0@166.632] global D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_msmf.cpp (539) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async callback
(py38a) PS > cd /anaconda_win/work/YOLOv7_OpenVINO_cpp-python-main/
(py38a) PS> python python/webcam.py -m yolov7.onnx -i 0
Traceback (most recent call last):
File "python/webcam.py", line 24, in <module>
yolov7_detector.infer_cam(args.input)
File "N:\anaconda_win\work\YOLOv7_OpenVINO_cpp-python-main\python\yolov7.py", line 262, in infer_cam
self.infer_queue.start_async({self.input_layer.any_name: img_batch}, (src_img_list, src_size))
File "C:\Users\izuts\anaconda3\envs\py38a\lib\site-packages\openvino\runtime\ie_api.py", line 237, in start_async
super().start_async(
RuntimeError: Can't SetBlob with name: images, because model input (shape={1,3,640,640}) and blob (shape=(1.640.640.3)) are incompatible
[ WARN:1@7.773] global D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_msmf.cpp (539) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB term
(py38a) PS > cd /anaconda_win/work/yolov7 (py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py
| コマンドオプション | デフォールト設定 | 意味 | ※注 |
| -h, --help | - | ヘルプ表示 | |
| -i, --input | ../../Videos/car_m.mp4 | カメラ(cam)または動画・静止画像ファイル | ※1 |
| -m, --model | ../../model/yolov7.onnx | 学習済みモデル(IR/onnx) | |
| -d, --device | CPU | デバイス指定 (CPU/GPU/MYRIAD) | |
| --conf | 0.25 | クラス判定の閾値 (数値が小さい程オブジェクトは増えるが、ノイズも増える | ※2 |
| --iou | 0.45 | Intersection Over Union(検出領域が重なっている割合、数値が大きいほど重なり度合いが高い) | |
| -l, --label | coco.names_jp | ラベル・ファイル | |
| --log | 3 | ログ出力レベル(0/1/2/3/4/5) | |
| -t, --title | y | タイトル表示 (y/n) | |
| -s, --speed | y | スピード計測表示 (y/n) | |
| -o, --out | non | 処理結果を出力する場合のファイル名 | |
| -p, --pre_api | False | Preprocessing api. | |
| -bs, --batchsize | 1 | Batch size. | |
| -n, --nireq | 1 | number of infer request. | |
| -g, --grid | False | With grid in model. |
(py38a) PS N:\anaconda_win\work\yolov7> python object_detect_yolo7.py -h
usage: object_detect_yolo7.py [-h] [-i INPUT_IMAGE] [-m MODEL] [-d DEVICE]
[-lb LABEL_FILE] [--log LOG] [-t TITLE]
[-s SPEED] [-o IMAGE_OUT] [-p] [-bs BATCHSIZE]
[-n NIREQ] [-g]
optional arguments:
-h, --help show this help message and exit
-i INPUT_IMAGE, --input INPUT_IMAGE
Absolute path to image file or cam for camera
stream.Default value is '../../Videos/car_m.mp4'
-m MODEL, --model MODEL
Model Path to an .xml file with a trained
model.Default value is ../../model/yolov7.onnx
-d DEVICE, --device DEVICE
Optional. Specify a target device to infer on. CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
look for a suitable plugin for the device specified.
Default value is CPU
--conf CONF object confidence threshold
--iou IOU IOU threshold for NMS
-lb LABEL_FILE, --labels LABEL_FILE
Absolute path to labels file.Default value is
'coco.names_jp'
--log LOG Log level(-1/0/1/2/3/4/5) Default value is '3'
-t TITLE, --title TITLE
Program title flag.(y/n) Default value is 'y'
-s SPEED, --speed SPEED
Speed display flag.(y/n) Default calue is 'y'
-o IMAGE_OUT, --out IMAGE_OUT
Processed image file path. Default value is 'non'
-p, --pre_api Preprocessing api.
-bs BATCHSIZE, --batchsize BATCHSIZE
Batch size.
-n NIREQ, --nireq NIREQ
number of infer request.
-g, --grid With grid in model.
(py38a) PS C:\anaconda_win\work\YOLOv7_OpenVINO_cpp-python-main> cd ../yolov7 (py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py Starting.. - Program title : Object detection YOLO V7 - OpenCV version : 4.5.5 - OpenVINO engine: 2022.1.0-7019-cdb9bec7210-releases/2022/1 - Input image : ../../Videos/car_m.mp4 - Model : ../../model/yolov7.onnx - Device : CPU - Confidence thr : 0.1 - IOU threshold : 0.6 - Label : coco.names_jp - Log level : 3 - Title flag : y - Speed flag : y - Processed out : non - Preprocessing : False - Batch size : 1 - number of inf : 1 - With grid : False FPS average: 1.80 Finished.
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Videos/car1_m.mp4
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Videos/car2_m.mp4
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Images/desk-image.jpg
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Images/car-person.jpg
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Images/bus.jpg
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i ../../Images/zidane.jpg
(py38a) PS C:\anaconda_win\work\yolov7> python object_detect_yolo7.py -i 0※ 指定できるファイル拡張子 .mp4 .bmp .png .jpg .jpeg .JPG .tif
# -*- coding: utf-8 -*-
##------------------------------------------
## Object detection YOLO V7 Ver 0.03
## with OpenVINO™ runtime
##
## 2023.04.12 Masahiro Izutsu
##------------------------------------------
## GitHub https://github.com/OpenVINO-dev-contest/YOLOv7_OpenVINO_cpp-python
##
## yolo7_ov.py
## ver 0.03 2023.07.18 conf, iou コマンドパラメータによる設定追加
# import処理
import sys
import os
import yolov7s
import argparse
import cv2
import numpy as np
from openvino.inference_engine import get_version
import my_logging
import my_file
import my_color80
import my_process
import my_movedlg
import my_dialog
import my_winstat
from my_print import printG, printR, printY, printC, printB, printN
# 定数定義
from os.path import expanduser
MODEL_DEF = expanduser('../../model/yolov7.onnx')
INPUT_IMAGE_DEF = '../../Videos/car_m.mp4' # 入力イメージ・パラメータ初期値
LABEL_DEF = 'coco.names_jp'
# Parses arguments for the application
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', metavar = 'INPUT_IMAGE', type=str, default = INPUT_IMAGE_DEF,
help = 'Absolute path to image file or cam for camera stream.'
'Default value is \''+INPUT_IMAGE_DEF+'\'')
parser.add_argument('-m', '--model', type=str,
default = MODEL_DEF,
help = 'Model Path to an .xml file with a trained model.'
'Default value is '+MODEL_DEF)
parser.add_argument('-d', '--device', default='CPU', type = str,
help = 'Optional. Specify a target device to infer on. CPU, GPU, FPGA, HDDL or MYRIAD is '
'acceptable. The demo will look for a suitable plugin for the device specified. '
'Default value is CPU')
parser.add_argument('--conf', default = 0.25, type = float, help = 'object confidence threshold')
parser.add_argument('--iou', default = 0.45, type = float, help = 'IOU threshold for NMS')
parser.add_argument('-lb', '--labels', metavar = 'LABEL_FILE', type = str, default = LABEL_DEF,
help = 'Absolute path to labels file.'
'Default value is \''+LABEL_DEF+'\'')
parser.add_argument('--log', metavar = 'LOG', default = '3',
help = 'Log level(-1/0/1/2/3/4/5) Default value is \'3\'')
parser.add_argument('-t', '--title', metavar = 'TITLE',
default = 'y',
help = 'Program title flag.(y/n) Default value is \'y\'')
parser.add_argument('-s', '--speed', metavar = 'SPEED',
default = 'y',
help = 'Speed display flag.(y/n) Default calue is \'y\'')
parser.add_argument('-o', '--out', metavar = 'IMAGE_OUT',
default = 'non',
help = 'Processed image file path. Default value is \'non\'')
parser.add_argument('-p', '--pre_api', required = False, action='store_true',
help = 'Preprocessing api.')
parser.add_argument('-bs', '--batchsize', required = False, default = 1, type=int,
help = 'Batch size.')
parser.add_argument('-n', '--nireq', required = False, default = 1, type=int,
help = 'number of infer request.')
parser.add_argument('-g', '--grid', required = False, action = 'store_true',
help = 'With grid in model.')
return parser
# モデル基本情報の表示
def display_info(title, input_str, model, device, conf, iou, labels, log, titleflg, speedflg, outpath, pre_api, batchsize, nireq, grid):
printG(f' - Program title : {title}')
printG(f' - OpenCV version : {cv2.__version__}')
printG(f' - OpenVINO engine: {get_version()}')
printY(f' - Input image : {input_str}')
printY(f' - Model : {model}')
printY(f' - Device : {device}')
printY(f' - Confidence thr : {conf}')
printY(f' - IOU threshold : {iou}')
printY(f' - Label : {labels}')
printY(f' - Log level : {log}')
printY(f' - Title flag : {titleflg}')
printY(f' - Speed flag : {speedflg}')
printY(f' - Processed out : {outpath}')
printB(f' - Preprocessing : {pre_api}')
printB(f' - Batch size : {batchsize}')
printB(f' - number of inf : {nireq}')
printB(f' - With grid : {grid}')
# ** main関数 **
def main():
# 入力パラメータの処理
args = parse_args().parse_args()
input_str = args.input
log = args.log
logsel = int(log)
labels = args.labels
titleflg = args.title
speedflg = args.speed
model = args.model
device = args.device
outpath = args.out
conf = args.conf
iou = args.iou
pre_api = args.pre_api
batchsize = args.batchsize
nireq = args.nireq
grid = args.grid
# ファイル処理クラスのインスタンス
my_file_treatment = my_file.FileTreatment(logsel)
# 入力ソースの違いによる前処理
if input_str == '0':
input_str = my_dialog.select_image_movie_file(initdir = '../../Videos')
if len(input_str) <= 0:
printR('Program canceled.')
return 0
input_stream = input_str
if input_str.lower() == "cam" or input_str.lower() == "camera":
input_stream = 0
isstream = True
else:
filetype = my_file_treatment.is_pict(input_stream)
isstream = filetype == 'None'
if (filetype == 'NotFound'):
printR(f"'{input_stream}': Input file Not found.")
return 0
if isstream and outpath != 'non' and os.path.splitext(outpath)[1] != '.mp4':
printR(f"'{outpath}': Output file type is '.mp4'")
return 0
# アプリケーション・ログ設定
module = os.path.basename(__file__)
module_name = os.path.splitext(module)[0]
logger = my_logging.get_module_logger_sel(module_name, logsel)
logger.info('Starting..')
# YOLO7 オブジェクト
yolov7_detector=yolov7s.YOLOV7_OPENVINO(model, device, conf, iou, pre_api, batchsize, nireq, grid)
title = yolov7_detector.title
# 分類ラベルの取得
with open(labels, encoding='utf-8') as labels_file:
label_list = labels_file.read().splitlines()
yolov7_detector.set_classes(label_list)
# 情報表示
display_info(title, input_str, model, device, conf, iou, labels, log, titleflg, speedflg, outpath, pre_api, batchsize, nireq, grid)
logger.debug(f'Object list\n {label_list}')
# 関数内パラメータ
window_name = yolov7_detector.title + " (hit 'q' or 'esc' key to exit)"
# 入力準備
if (isstream):
# カメラ
cap = cv2.VideoCapture(input_stream)
success, img_frame = cap.read()
if success == False:
logger.error('camera read error !!')
loopflg = cap.isOpened()
img_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
img_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else:
# 画像ファイル読み込み
img_frame = cv2.imread(input_stream)
if img_frame is None:
logger.error('unable to read the input.')
return 0
# アスペクト比を固定してリサイズ（スクリーンに収まるサイズ）
h, w = my_movedlg.get_display_size()
mx = h - 10 if h > w else w - 10
img_frame = my_process.frame_resize(img_frame, mx)
loopflg = True # 1回ループ
img_width = img_frame.shape[1]
img_height = img_frame.shape[0]
# ウインドウ初期設定
cv2.namedWindow(window_name, flags=cv2.WINDOW_AUTOSIZE | cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow(window_name, 20, 0)
# 処理結果の記録 step1
if (outpath != 'non'):
if (isstream):
fps = int(cap.get(cv2.CAP_PROP_FPS))
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
outvideo = cv2.VideoWriter(outpath, fourcc, fps, (img_width, img_height))
# YOLO7 Set callback function for postprocess
yolov7_detector.infer_queue.set_callback(yolov7_detector.postprocess)
# fps計測開始
yolov7_detector.fpsWithTick.get()
# メインループ
while (loopflg):
if img_frame is None:
logger.error('unable to read the input.')
return 0
src_img_list = []
src_img_list.append(img_frame)
img = yolov7_detector.letterbox(img_frame, yolov7_detector.img_size)
src_size = img_frame.shape[:2]
img = img.astype(dtype=np.float32)
if (yolov7_detector.pre_api == False):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # BGR to RGB
img /= 255.0
img = img.transpose(2, 0, 1) # NHWC to NCHW
input_image = np.expand_dims(img, 0)
# Do inference
yolov7_detector.infer_queue.start_async({yolov7_detector.input_layer.any_name: input_image}, (src_img_list, src_size))
# ウインドウの更新/画像表示
yolov7_detector.infer_queue.wait_all()
cv2.imshow(window_name, img_frame)
# 処理結果の記録 step2
if (outpath != 'non'):
if (isstream):
outvideo.write(img_frame)
else:
cv2.imwrite(outpath, img_frame)
# 何らかのキーが押されたら終了
breakflg = False
while(True):
key = cv2.waitKey(1) # (カメラ・アクセス速度調整)
if key == 27 or key == 113: # 'esc' or 'q'
breakflg = True
break
if not my_winstat._is_visible(window_name): # 'Close' button
breakflg = True
break
if (isstream):
break
if ((breakflg == False) and isstream):
# 次のフレームを読み出す
success, img_frame = cap.read()
if success == False: # 動画終了
break
loopflg = cap.isOpened()
else:
loopflg = False
# 終了処理
if (isstream):
cap.release()
# 処理結果の記録 step3
if (outpath != 'non'):
if (isstream):
outvideo.release()
cv2.destroyAllWindows()
printC('FPS average: {:>10.2f}'.format(yolov7_detector.fpsWithTick.get_average()))
logger.info('Finished.\n')
if __name__ == "__main__":
sys.exit(main() or 0)
# -*- coding: utf-8 -*-
##------------------------------------------
## YOLOV7_OPENVINO Ver 0.03
##
## 2023.04.12 Masahiro Izutsu
##------------------------------------------
## GitHub https://github.com/OpenVINO-dev-contest/YOLOv7_OpenVINO_cpp-python
##
## yolo7s.py
## ver 0.02 2023.05.19 ラベルファイル数を取得するように変更
## ver 0.03 2023.07.18 conf, iou コマンドパラメータによる設定追加
from openvino.runtime import Core
import cv2
import numpy as np
import random
import time
from openvino.preprocess import PrePostProcessor, ColorFormat
from openvino.runtime import Layout, AsyncInferQueue, PartialShape
import my_fps
import my_puttext
import my_color80
TEXT_COLOR = my_color80.CR_white
class YOLOV7_OPENVINO(object):
def __init__(self, model_path, device, conf, iou, pre_api, batchsize, nireq, grid):
self.title = 'Object detection YOLO V7'
# 日本語フォント指定
self.fontFace = my_puttext.get_font()
# 計測値
self.fpsWithTick = my_fps.fpsWithTick()
self.speedflg = 'y'
# set the hyperparameters
self.classes = [
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
]
self.batchsize = batchsize
self.grid = grid
self.img_size = (640, 640)
self.conf_thres = conf
self.iou_thres = iou
self.class_num = len(self.classes)
self.colors = [[random.randint(0, 255) for _ in range(3)] for _ in self.classes]
self.stride = [8, 16, 32]
self.anchor_list = [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]]
self.anchor = np.array(self.anchor_list).astype(float).reshape(3, -1, 2)
area = self.img_size[0] * self.img_size[1]
self.size = [int(area / self.stride[0] ** 2), int(area / self.stride[1] ** 2), int(area / self.stride[2] ** 2)]
self.feature = [[int(j / self.stride[i]) for j in self.img_size] for i in range(3)]
ie = Core()
self.model = ie.read_model(model_path)
self.input_layer = self.model.input(0)
new_shape = PartialShape([self.batchsize, 3, self.img_size[0], self.img_size[1]])
self.model.reshape({self.input_layer.any_name: new_shape})
self.pre_api = pre_api
if (self.pre_api == True):
# Preprocessing API
ppp = PrePostProcessor(self.model)
# Declare section of desired application's input format
ppp.input().tensor() \
.set_layout(Layout("NHWC")) \
.set_color_format(ColorFormat.BGR)
# Here, it is assumed that the model has "NCHW" layout for input.
ppp.input().model().set_layout(Layout("NCHW"))
# Convert current color format (BGR) to RGB
ppp.input().preprocess() \
.convert_color(ColorFormat.RGB) \
.scale([255.0, 255.0, 255.0])
self.model = ppp.build()
print(f'Dump preprocessor: {ppp}')
self.compiled_model = ie.compile_model(model=self.model, device_name=device)
self.infer_queue = AsyncInferQueue(self.compiled_model, nireq)
def set_classes(self, classes):
self.classes = classes
self.class_num = len(self.classes)
def letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114)):
# Resize and pad image while meeting stride-multiple constraints
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
new_unpad[1] # wh padding
# divide padding into 2 sides
dw /= 2
dh /= 2
# resize
if shape[::-1] != new_unpad:
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
# add border
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
return img
def xywh2xyxy(self, x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def nms(self, prediction, conf_thres, iou_thres):
predictions = np.squeeze(prediction[0])
# Filter out object confidence scores below threshold
obj_conf = predictions[:, 4]
predictions = predictions[obj_conf > conf_thres]
obj_conf = obj_conf[obj_conf > conf_thres]
# Multiply class confidence with bounding box confidence
predictions[:, 5:] *= obj_conf[:, np.newaxis]
# Get the scores
scores = np.max(predictions[:, 5:], axis=1)
# Filter out the objects with a low score
valid_scores = scores > conf_thres
predictions = predictions[valid_scores]
scores = scores[valid_scores]
# Get the class with the highest confidence
class_ids = np.argmax(predictions[:, 5:], axis=1)
# Get bounding boxes for each object
boxes = self.xywh2xyxy(predictions[:, :4])
# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
# indices = nms(boxes, scores, self.iou_threshold)
indices = cv2.dnn.NMSBoxes(boxes.tolist(), scores.tolist(), conf_thres, iou_thres)
return boxes[indices], scores[indices], class_ids[indices]
def clip_coords(self, boxes, img_shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, 0].clip(0, img_shape[1]) # x1
boxes[:, 1].clip(0, img_shape[0]) # y1
boxes[:, 2].clip(0, img_shape[1]) # x2
boxes[:, 3].clip(0, img_shape[0]) # y2
def scale_coords(self, img1_shape, img0_shape, coords, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
# gain = old / new
if ratio_pad is None:
gain = min(img1_shape[0] / img0_shape[0],
img1_shape[1] / img0_shape[1])
padding = (img1_shape[1] - img0_shape[1] * gain) / \
2, (img1_shape[0] - img0_shape[0] * gain) / 2
else:
gain = ratio_pad[0][0]
padding = ratio_pad[1]
coords[:, [0, 2]] -= padding[0] # x padding
coords[:, [1, 3]] -= padding[1] # y padding
coords[:, :4] /= gain
self.clip_coords(coords, img0_shape)
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def draw(self, img, boxinfo):
# タイトル描画
if len(self.title) != 0:
cv2.putText(img, self.title, (10 + 2, 30 + 2), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(0, 0, 0), lineType=cv2.LINE_AA)
cv2.putText(img, self.title, (10, 30), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(200, 200, 0), lineType=cv2.LINE_AA)
# FPSを表示
fps = self.fpsWithTick.get()
st_fps = 'fps: {:>6.2f}'.format(fps)
if (self.speedflg == 'y'):
cv2.rectangle(img, (10, 38), (95, 55), (90, 90, 90), -1)
cv2.putText(img, st_fps, (15, 50), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.4, color=(255, 255, 255), lineType=cv2.LINE_AA)
for xyxy, conf, cls in boxinfo:
xmin = int(xyxy[0])
ymin = int(xyxy[1])
xmax = int(xyxy[2])
ymax = int(xyxy[3])
label = self.classes[int(cls)] + ' ' + str(round(conf * 100, 1)) + '%'
# Plots one bounding box on image img
color_boder = my_color80.get_boder_bgr80(int(cls))
color_back = my_color80.get_back_bgr80(int(cls))
x1, y1, x2, y2 = my_puttext.cv2_putText(img, label, (xmin + 4, ymin - 7), self.fontFace, 14, TEXT_COLOR, 0, True) # 描画範囲の取得
cv2.rectangle(img, (xmin, ymin -28), (max(xmax, x2), ymin), color_back, -1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color_boder, 2)
my_puttext.cv2_putText(img, label, (xmin + 4, ymin - 7), self.fontFace, 14, TEXT_COLOR, 0)
def postprocess(self, infer_request, info):
src_img_list, src_size = info
for batch_id in range(self.batchsize):
if self.grid:
results = np.expand_dims(infer_request.get_output_tensor(0).data[batch_id], axis=0)
else:
output = []
# Get the each feature map's output data
output.append(self.sigmoid(infer_request.get_output_tensor(0).data[batch_id].reshape(-1, self.size[0]*3, 5+self.class_num)))
output.append(self.sigmoid(infer_request.get_output_tensor(1).data[batch_id].reshape(-1, self.size[1]*3, 5+self.class_num)))
output.append(self.sigmoid(infer_request.get_output_tensor(2).data[batch_id].reshape(-1, self.size[2]*3, 5+self.class_num)))
# Postprocessing
grid = []
for _, f in enumerate(self.feature):
grid.append([[i, j] for j in range(f[0]) for i in range(f[1])])
result = []
for i in range(3):
src = output[i]
xy = src[..., 0:2] * 2. - 0.5
wh = (src[..., 2:4] * 2) ** 2
dst_xy = []
dst_wh = []
for j in range(3):
dst_xy.append((xy[:, j * self.size[i]:(j + 1) * self.size[i], :] + grid[i]) * self.stride[i])
dst_wh.append(wh[:, j * self.size[i]:(j + 1) *self.size[i], :] * self.anchor[i][j])
src[..., 0:2] = np.concatenate((dst_xy[0], dst_xy[1], dst_xy[2]), axis=1)
src[..., 2:4] = np.concatenate((dst_wh[0], dst_wh[1], dst_wh[2]), axis=1)
result.append(src)
results = np.concatenate(result, 1)
boxes, scores, class_ids = self.nms(results, self.conf_thres, self.iou_thres)
img_shape = self.img_size
self.scale_coords(img_shape, src_size, boxes)
# Draw the results
self.draw(src_img_list[batch_id], zip(boxes, scores, class_ids))
※「py38a」仮想環境で実行可能な修正を加えて本プロジェクト内に再配置した
(py38a) PS C:\anaconda_win\work\YOLOv7_OpenVINO_cpp-python-main> cd ../openvino (py38a) PS C:\anaconda_win\work\openvino> python object_detect_yolo5.py Starting.. - Program title : Object detection YOLO V5 - OpenCV version : 4.5.5 - OpenVINO engine: 2022.1.0-7019-cdb9bec7210-releases/2022/1 - Input image : ../../Videos/car_m.mp4 - Model : ../../model/yolov5s.xml - Device : CPU - Label : coco.names_jp - Threshold : 0.5 - Threshold (IOU): 0.4 - Log level : 3 - Program Title : y - Speed flag : y - Processed out : non Loading model to the plugin Starting inference... FPS average: 2.70 Finished.
(py38a) PS C:\anaconda_win\work\openvino> python object_detect_yolo3.py
Starting..
- Program title : Object detection TinyYOLO V3
- OpenCV version : 4.5.5
- OpenVINO engine: 2022.1.0-7019-cdb9bec7210-releases/2022/1
- Input image : ../../Videos/car_m.mp4
- Model : ../../model/public/FP32/yolo-v3-tiny-tf.xml
- Device : CPU
- Labels File : coco.names_jp
- Threshold : 0.6
- Intersection Over Union: 0.25
- Log level : 3
- Program Title : y
- Speed flag : y
- Processed out : non
- Input Shape : [1, 3, 416, 416]
- Output Shapes :
- output #0 name : conv2d_12/Conv2D/YoloRegion
- output shape: [1, 255, 26, 26]
- output #1 name : conv2d_9/Conv2D/YoloRegion
- output shape: [1, 255, 13, 13]
FPS average: 17.30
Finished.# -*- coding: utf-8 -*-
##------------------------------------------
## Object detection YOLO V5 Ver 0.05
## with OpenVINO™ toolkit
##
## 2022.11.01 Masahiro Izutsu
##------------------------------------------
## ver 0.00 2021.10.02 yolov5_demo_OV.py (original: yolov5_demo_OV2021.3.py)
## YOLO V5 OpenVINO demoprogram
## for https://github.com/ultralytics/yolov5/tree/v5.0
## GitHub https://github.com/violet17/yolov5_demo
##
## object_detect_yolo5.py (yolov5_demo_OV.py 2021.10.02)
## ver 0.05 2023.04.12 クラス描画範囲の出力対応
# 定数定義
title = 'Object detection YOLO V5'
from os.path import expanduser
MODEL_DEF = expanduser('../../model/yolov5s.xml')
INPUT_IMAGE_DEF = '../../Videos/car_m.mp4' # 入力イメージ・パラメータ初期値
LABEL_DEF = 'coco.names_jp'
# モジュール読み込み
from openvino.inference_engine import IECore
from openvino.inference_engine import get_version
# import処理
import sys
import cv2
import numpy as np
import os
import argparse
import my_logging
import my_puttext
import my_file
import my_movedlg
import my_dialog
import my_process
import my_winstat
import my_fps
import my_color80
from my_print import printG, printR, printY, printC, printB, printN
from math import exp as exp
# Adjust these thresholds
PROB_THRESHOLD = 0.5
IOU_THRESHOLD = 0.4
# Used for display
TEXT_COLOR = my_color80.CR_white
# Parses arguments for the application
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', metavar = 'INPUT_IMAGE', type=str, default = INPUT_IMAGE_DEF,
help = 'Absolute path to image file or cam for camera stream.'
'Default value is \''+INPUT_IMAGE_DEF+'\'')
parser.add_argument('-m', '--model', type=str,
default = MODEL_DEF,
help = 'Model Path to an .xml file with a trained model.'
'Default value is '+MODEL_DEF)
parser.add_argument('-d', '--device', default='CPU', type = str,
help = 'Optional. Specify a target device to infer on. CPU, GPU, FPGA, HDDL or MYRIAD is '
'acceptable. The demo will look for a suitable plugin for the device specified. '
'Default value is CPU')
parser.add_argument('-lb', '--labels', metavar = 'LABEL_FILE', type = str, default = LABEL_DEF,
help = 'Absolute path to labels file.'
'Default value is \''+LABEL_DEF+'\'')
parser.add_argument('--threshold', metavar = 'FLOAT', type = float, default = PROB_THRESHOLD,
help = 'Optional. Probability threshold for detections filtering'
'Default value is \''+ str(PROB_THRESHOLD) +'\'')
parser.add_argument('--iou', metavar = 'FLOAT', type = float, default = IOU_THRESHOLD,
help = 'Optional. Intersection over union threshold for overlapping detections filtering'
'Default value is \''+ str(IOU_THRESHOLD) +'\'')
parser.add_argument('--log', metavar = 'LOG', default = '3',
help = 'Log level(-1/0/1/2/3/4/5) Default value is \'3\'')
parser.add_argument('-t', '--title', metavar = 'TITLE',
default = 'y',
help = 'Program title flag.(y/n) Default value is \'y\'')
parser.add_argument('-s', '--speed', metavar = 'SPEED',
default = 'y',
help = 'Speed display flag.(y/n) Default calue is \'y\'')
parser.add_argument('-o', '--out', metavar = 'IMAGE_OUT',
default = 'non',
help = 'Processed image file path. Default value is \'non\'')
return parser
# モデル基本情報の表示
def display_info(input_str, model, device, labels, threshold, iou_threshold, log, titleflg, speedflg, outpath):
printG(f' - Program title : {title}')
printG(f' - OpenCV version : {cv2.__version__}')
printG(f' - OpenVINO engine: {get_version()}')
printY(f' - Input image : {input_str}')
printY(f' - Model : {model}')
printY(f' - Device : {device}')
printY(f' - Label : {labels}')
printY(f' - Threshold : {threshold}')
printY(f' - Threshold (IOU): {iou_threshold}')
printY(f' - Log level : {log}')
printY(f' - Program Title : {titleflg}')
printY(f' - Speed flag : {speedflg}')
printY(f' - Processed out : {outpath}')
class YoloParams:
# ------------------------------------------- Extracting layer parameters ------------------------------------------
# Magic numbers are copied from yolo samples
def __init__(self, side):
self.num = 3 #if 'num' not in param else int(param['num'])
self.coords = 4 #if 'coords' not in param else int(param['coords'])
self.classes = 80 #if 'classes' not in param else int(param['classes'])
self.side = side
self.anchors = [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0,
198.0,
373.0, 326.0] #if 'anchors' not in param else [float(a) for a in param['anchors'].split(',')]
#self.isYoloV3 = False
#if param.get('mask'):
# mask = [int(idx) for idx in param['mask'].split(',')]
# self.num = len(mask)
# maskedAnchors = []
# for idx in mask:
# maskedAnchors += [self.anchors[idx * 2], self.anchors[idx * 2 + 1]]
# self.anchors = maskedAnchors
# self.isYoloV3 = True # Weak way to determine but the only one.
def log_params(self, logger):
params_to_print = {'classes': self.classes, 'num': self.num, 'coords': self.coords, 'anchors': self.anchors}
[logger.debug(" {:8}: {}".format(param_name, param)) for param_name, param in params_to_print.items()]
def letterbox(img, size=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
w, h = size
# Scale ratio (new / old)
r = min(h / shape[0], w / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = w - new_unpad[0], h - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (w, h)
ratio = w / shape[1], h / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
top2, bottom2, left2, right2 = 0, 0, 0, 0
if img.shape[0] != h:
top2 = (h - img.shape[0])//2
bottom2 = top2
img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color) # add border
elif img.shape[1] != w:
left2 = (w - img.shape[1])//2
right2 = left2
img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color) # add border
return img
def scale_bbox(x, y, height, width, class_id, confidence, im_h, im_w, resized_im_h=640, resized_im_w=640):
gain = min(resized_im_w / im_w, resized_im_h / im_h) # gain = old / new
pad = (resized_im_w - im_w * gain) / 2, (resized_im_h - im_h * gain) / 2 # wh padding
x = int((x - pad[0])/gain)
y = int((y - pad[1])/gain)
w = int(width/gain)
h = int(height/gain)
xmin = max(0, int(x - w / 2))
ymin = max(0, int(y - h / 2))
xmax = min(im_w, int(xmin + w))
ymax = min(im_h, int(ymin + h))
# Method item() used here to convert NumPy types to native types for compatibility with functions, which don't
# support Numpy types (e.g., cv2.rectangle doesn't support int64 in color parameter)
return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id.item(), confidence=confidence.item())
def entry_index(side, coord, classes, location, entry):
side_power_2 = side ** 2
n = location // side_power_2
loc = location % side_power_2
return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)
def parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold):
# ------------------------------------------ Validating output parameters ------------------------------------------
out_blob_n, out_blob_c, out_blob_h, out_blob_w = blob.shape
predictions = 1.0/(1.0+np.exp(-blob))
assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
"be equal to width. Current height = {}, current width = {}" \
"".format(out_blob_h, out_blob_w)
# ------------------------------------------ Extracting layer parameters -------------------------------------------
orig_im_h, orig_im_w = original_im_shape
resized_image_h, resized_image_w = resized_image_shape
objects = list()
side_square = params.side * params.side
# ------------------------------------------- Parsing YOLO Region output -------------------------------------------
bbox_size = int(out_blob_c/params.num) #4+1+num_classes
for row, col, n in np.ndindex(params.side, params.side, params.num):
bbox = predictions[0, n*bbox_size:(n+1)*bbox_size, row, col]
x, y, width, height, object_probability = bbox[:5]
class_probabilities = bbox[5:]
if object_probability < threshold:
continue
x = (2*x - 0.5 + col)*(resized_image_w/out_blob_w)
y = (2*y - 0.5 + row)*(resized_image_h/out_blob_h)
if int(resized_image_w/out_blob_w) == 8 & int(resized_image_h/out_blob_h) == 8: #80x80,
idx = 0
elif int(resized_image_w/out_blob_w) == 16 & int(resized_image_h/out_blob_h) == 16: #40x40
idx = 1
elif int(resized_image_w/out_blob_w) == 32 & int(resized_image_h/out_blob_h) == 32: # 20x20
idx = 2
width = (2*width)**2* params.anchors[idx * 6 + 2 * n]
height = (2*height)**2 * params.anchors[idx * 6 + 2 * n + 1]
class_id = np.argmax(class_probabilities)
confidence = object_probability
objects.append(scale_bbox(x=x, y=y, height=height, width=width, class_id=class_id, confidence=confidence,
im_h=orig_im_h, im_w=orig_im_w, resized_im_h=resized_image_h, resized_im_w=resized_image_w))
return objects
def intersection_over_union(box_1, box_2):
width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
if width_of_overlap_area < 0 or height_of_overlap_area < 0:
area_of_overlap = 0
else:
area_of_overlap = width_of_overlap_area * height_of_overlap_area
box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
area_of_union = box_1_area + box_2_area - area_of_overlap
if area_of_union == 0:
return 0
return area_of_overlap / area_of_union
# ** main関数 **
def main():
# 日本語フォント指定
fontFace = my_puttext.get_font()
# 入力パラメータの処理
ARGS = parse_args().parse_args()
input_str = ARGS.input
log = ARGS.log
logsel = int(log)
labels = ARGS.labels
titleflg = ARGS.title
speedflg = ARGS.speed
model = ARGS.model
threshold = ARGS.threshold
iou_threshold = ARGS.iou
device = ARGS.device
outpath = ARGS.out
# ファイル処理クラスのインスタンス
my_file_treatment = my_file.FileTreatment(logsel)
# 入力ソースの違いによる前処理
if input_str == '0':
input_str = my_dialog.select_image_movie_file(initdir = '../../Videos')
if len(input_str) <= 0:
printR('Program canceled.')
return 0
input_stream = input_str
if input_str.lower() == "cam" or input_str.lower() == "camera":
input_stream = 0
isstream = True
else:
filetype = my_file_treatment.is_pict(input_stream)
isstream = filetype == 'None'
if (filetype == 'NotFound'):
printR(f"'{input_stream}': Input file Not found.")
return 0
if isstream and outpath != 'non' and os.path.splitext(outpath)[1] != '.mp4':
printR(f"'{outpath}': Output file type is '.mp4'")
return 0
# アプリケーション・ログ設定
module = os.path.basename(__file__)
module_name = os.path.splitext(module)[0]
logger = my_logging.get_module_logger_sel(module_name, logsel)
logger.info('Starting..')
# 分類ラベルの取得
with open(labels, encoding='utf-8') as labels_file:
label_list = labels_file.read().splitlines()
# 情報表示
display_info(input_str, model, device, labels, threshold, iou_threshold, log, titleflg, speedflg, outpath)
logger.debug(f'Object list\n {label_list}')
## 1. Plugin initialization for specified device and load extensions library if specified
logger.debug("Creating Inference Engine...")
ie = IECore()
## 2. Reading the IR generated by the Model Optimizer (.xml and .bin files)
logger.debug(f"Loading network:\n\t{model}")
net = ie.read_network(model=model)
## 3. Load CPU extension for support specific layer
## 4. Preparing inputs
logger.debug("Preparing inputs")
input_blob = next(iter(net.input_info))
# Defaulf batch_size is 1
net.batch_size = 1
# Read and pre-process input images
in_n, in_c, in_h, in_w = net.input_info[input_blob].input_data.shape
# 関数内パラメータ
window_name = title + " (hit 'q' or 'esc' key to exit)"
# 入力準備
if (isstream):
# カメラ
cap = cv2.VideoCapture(input_stream)
success, img_frame = cap.read()
if success == False:
logger.error('camera read error !!')
loopflg = cap.isOpened()
img_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
img_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else:
# 画像ファイル読み込み
img_frame = cv2.imread(input_stream)
if img_frame is None:
logger.error('unable to read the input.')
return 0
# アスペクト比を固定してリサイズ（スクリーンに収まるサイズ）
h, w = my_movedlg.get_display_size()
mx = h - 10 if h > w else w - 10
img_frame = my_process.frame_resize(img_frame, mx)
loopflg = True # 1回ループ
img_width = img_frame.shape[1]
img_height = img_frame.shape[0]
# ウインドウ初期設定
cv2.namedWindow(window_name, flags=cv2.WINDOW_AUTOSIZE | cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow(window_name, 20, 0)
# 処理結果の記録 step1
if (outpath != 'non'):
if (isstream):
fps = int(cap.get(cv2.CAP_PROP_FPS))
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
outvideo = cv2.VideoWriter(outpath, fourcc, fps, (img_width, img_height))
## 5. Loading model to the plugin
logger.info("Loading model to the plugin")
exec_net = ie.load_network(network=net, num_requests=2, device_name=device)
cur_request_id = 0
render_time = 0
parsing_time = 0
## 6. Doing inference
logger.info("Starting inference...")
# 計測値初期化
fpsWithTick = my_fps.fpsWithTick()
frame_count = 0
fps_total = 0
fpsWithTick.get() # fps計測開始
# メインループ
while (loopflg):
if img_frame is None:
logger.error('unable to read the input.')
return 0
frame = img_frame
in_frame = letterbox(frame, (in_w, in_h))
in_frame0 = in_frame
# resize input_frame to network size
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((in_n, in_c, in_h, in_w))
# Start inference
req_handle = exec_net.start_async(request_id=0, inputs={input_blob: in_frame})
status = req_handle.wait()
# Collecting object detection results
objects = list()
if exec_net.requests[cur_request_id].wait(-1) == 0:
output = exec_net.requests[cur_request_id].output_blobs
for layer_name, out_blob in output.items():
layer_params = YoloParams(side=out_blob.buffer.shape[2])
logger.debug("Layer {} parameters: ".format(layer_name)) # ログを表示
layer_params.log_params(logger)
objects += parse_yolo_region(out_blob.buffer, in_frame.shape[2:], frame.shape[:-1], layer_params, threshold)
# Filtering overlapping boxes with respect to the --iou_threshold CLI parameter
objects = sorted(objects, key=lambda obj : obj['confidence'], reverse=True)
for i in range(len(objects)):
if objects[i]['confidence'] == 0:
continue
for j in range(i + 1, len(objects)):
if intersection_over_union(objects[i], objects[j]) > iou_threshold:
objects[j]['confidence'] = 0
# Drawing objects with respect to the --prob_threshold CLI parameter
objects = [obj for obj in objects if obj['confidence'] >= threshold]
if (titleflg == 'y'):
logger.debug("\nDetected boxes for batch {}:".format(1))
logger.debug(" Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR ")
origin_im_size = frame.shape[:-1]
for obj in objects:
# Validation bbox of detected object
if obj['xmax'] > origin_im_size[1] or obj['ymax'] > origin_im_size[0] or obj['xmin'] < 0 or obj['ymin'] < 0:
continue
# オブジェクト別の色指定
BOX_COLOR = my_color80.get_boder_bgr80(obj['class_id'])
LABEL_BG_COLOR = my_color80.get_back_bgr80(obj['class_id'])
det_label = label_list[obj['class_id']] if label_list and len(label_list) >= obj['class_id'] else \
str(obj['class_id'])
if (titleflg == 'y'):
logger.debug(
"{:^9} | {:10f} | {:4} | {:4} | {:4} | {:4} | {} ".format(det_label, obj['confidence'], obj['xmin'],
obj['ymin'], obj['xmax'], obj['ymax'],
BOX_COLOR))
lablstr = det_label + ' ' + str(round(obj['confidence'] * 100, 1)) + ' %'
x1, y1, x2, y2 = my_puttext.cv2_putText(frame, lablstr, (obj['xmin'] + 4, obj['ymin'] - 7), fontFace, 14, TEXT_COLOR, 0, True) # 描画範囲の取得
cv2.rectangle(frame, (obj['xmin'], obj['ymin']-26), (max(obj['xmax'], x2), obj['ymin']), LABEL_BG_COLOR, -1)
cv2.rectangle(frame, (obj['xmin'], obj['ymin']), (obj['xmax'], obj['ymax']), BOX_COLOR, 2)
my_puttext.cv2_putText(frame, lablstr, (obj['xmin'] + 4, obj['ymin'] - 7), fontFace, 14, TEXT_COLOR, 0)
# FPSを計算する
fps = fpsWithTick.get()
st_fps = 'fps: {:>6.2f}'.format(fps)
if (speedflg == 'y'):
cv2.rectangle(frame, (10, 38), (95, 55), (90, 90, 90), -1)
cv2.putText(frame, st_fps, (15, 50), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.4, color=(255, 255, 255), lineType=cv2.LINE_AA)
# タイトル描画
if (titleflg == 'y'):
cv2.putText(frame, title, (10 + 2, 30 + 2), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(0, 0, 0), lineType=cv2.LINE_AA)
cv2.putText(frame, title, (10, 30), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(200, 200, 0), lineType=cv2.LINE_AA)
# ウインドウの更新/画像表示
cv2.imshow(window_name, frame)
# 処理結果の記録 step2
if (outpath != 'non'):
if (isstream):
outvideo.write(frame)
else:
cv2.imwrite(outpath, frame)
# 何らかのキーが押されたら終了
breakflg = False
while(True):
key = cv2.waitKey(1) # (カメラ・アクセス速度調整)
if key == 27 or key == 113: # 'esc' or 'q'
breakflg = True
break
if not my_winstat._is_visible(window_name): # 'Close' button
breakflg = True
break
if (isstream):
break
if ((breakflg == False) and isstream):
# 次のフレームを読み出す
success, img_frame = cap.read()
if success == False: # 動画終了
break
loopflg = cap.isOpened()
else:
loopflg = False
# 終了処理
if (isstream):
cap.release()
# 処理結果の記録 step3
if (outpath != 'non'):
if (isstream):
outvideo.release()
cv2.destroyAllWindows()
del net
del exec_net
printC('FPS average: {:>10.2f}'.format(fpsWithTick.get_average()))
logger.info('Finished.\n')
if __name__ == '__main__':
sys.exit(main() or 0)
# -*- coding: utf-8 -*-
##------------------------------------------
## Object detection Ver 0.06
## with OpenVINO™ toolkit
##
## model: yolo-v3-tiny-tf
##
## 2022.10.30 Masahiro Izutsu
##------------------------------------------
## ver 0.00 2021.02.24
## ver 0.01 2021.03.25 device parameter
## ver 0.02 2021.06.23 fps display
## ver 0.03 2022.01.04 linux/windows
## object_detect_yolo3_3.py (object_detect_yolo3_2.py ver 0.03 2022.01.04)
## ver 0.05 2022.11.26 object has no attribute 'outputs'
## object_detect_yolo3.py (object_detect_yolo3_3.py ver 0.05 2022.11.26)
## ver 0.06 2023.04.12 クラス描画範囲の出力対応
# 定数定義
title = 'Object detection TinyYOLO V3'
from os.path import expanduser
MODEL_DEF_DETECT = expanduser('../../model/public/FP32/yolo-v3-tiny-tf.xml')
INPUT_IMAGE_DEF = '../../Videos/car_m.mp4' # 入力イメージ・パラメータ初期値
LABEL_DEF = 'coco.names_jp'
# モジュール読み込み
from openvino.inference_engine import IECore
from openvino.inference_engine import get_version
# import処理
import sys
import cv2
import numpy as np
import os
import argparse
import my_logging
import my_puttext
import my_file
import my_movedlg
import my_dialog
import my_process
import my_winstat
import my_fps
import my_color80
from my_print import printG, printR, printY, printC, printB, printN
# Adjust these thresholds
DETECTION_THRESHOLD = 0.60
IOU_THRESHOLD = 0.25
# Tiny yolo anchor box values
anchors = [10,14, 23,27, 37,58, 81,82, 135,169, 344,319]
# Used for display
TEXT_COLOR = my_color80.CR_white # white text
# Parses arguments for the application
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', metavar = 'INPUT_IMAGE', type=str, default = INPUT_IMAGE_DEF,
help = 'Absolute path to image file or cam for camera stream.'
'Default value is \''+INPUT_IMAGE_DEF+'\'')
parser.add_argument('--ir', metavar = 'IR_File', type=str,
default = MODEL_DEF_DETECT,
help = 'Absolute path to the neural network IR xml file.'
'Default value is '+MODEL_DEF_DETECT)
parser.add_argument('-d', '--device', default='CPU', type = str,
help = 'Optional. Specify a target device to infer on. CPU, GPU, FPGA, HDDL or MYRIAD is '
'acceptable. The demo will look for a suitable plugin for the device specified. '
'Default value is CPU')
parser.add_argument('-lb', '--labels', metavar = 'LABEL_FILE', type = str, default = LABEL_DEF,
help = 'Absolute path to labels file.'
'Default value is \''+LABEL_DEF+'\'')
parser.add_argument('--threshold', metavar = 'FLOAT', type = float, default = DETECTION_THRESHOLD,
help = 'Threshold for detection.'
'Default value is \''+ str(DETECTION_THRESHOLD) +'\'')
parser.add_argument('--iou', metavar = 'FLOAT', type = float, default = IOU_THRESHOLD,
help = 'Intersection Over Union.'
'Default value is \''+ str(IOU_THRESHOLD) +'\'')
parser.add_argument('--log', metavar = 'LOG', default = '3',
help = 'Log level(-1/0/1/2/3/4/5) Default value is \'3\'')
parser.add_argument('-t', '--title', metavar = 'TITLE',
default = 'y',
help = 'Program title flag.(y/n) Default value is \'y\'')
parser.add_argument('-s', '--speed', metavar = 'SPEED',
default = 'y',
help = 'Speed display flag.(y/n) Default calue is \'y\'')
parser.add_argument('-o', '--out', metavar = 'IMAGE_OUT',
default = 'non',
help = 'Processed image file path. Default value is \'non\'')
return parser
# creates a mask to remove duplicate objects (boxes) and their related probabilities and classifications
# that should be considered the same object. This is determined by how similar the boxes are
# based on the intersection-over-union metric.
# box_list is as list of boxes (4 floats for centerX, centerY and Length and Width)
def get_duplicate_box_mask(box_list, iou_threshold):
# The intersection-over-union threshold to use when determining duplicates.
# objects/boxes found that are over this threshold will be
# considered the same object
max_iou = iou_threshold
box_mask = np.ones(len(box_list))
for i in range(len(box_list)):
if box_mask[i] == 0: continue
for j in range(i + 1, len(box_list)):
if get_intersection_over_union(box_list[i], box_list[j]) >= max_iou:
if box_list[i][4] < box_list[j][4]:
box_list[i], box_list[j] = box_list[j], box_list[i]
box_mask[j] = 0.0
filter_iou_mask = np.array(box_mask > 0.0, dtype='bool')
return filter_iou_mask
# Evaluate the intersection-over-union for two boxes
# The intersection-over-union metric determines how close
# two boxes are to being the same box. The closer the boxes
# are to being the same, the closer the metric will be to 1.0
# box_1 and box_2 are arrays of 4 numbers which are the (x, y)
# points that define the center of the box and the length and width of
# the box.
# Returns the intersection-over-union (between 0.0 and 1.0)
# for the two boxes specified.
def get_intersection_over_union(box_1, box_2):
# one diminsion of the intersecting box
intersection_dim_1 = min(box_1[0]+0.5*box_1[2],box_2[0]+0.5*box_2[2])-\
max(box_1[0]-0.5*box_1[2],box_2[0]-0.5*box_2[2])
# the other dimension of the intersecting box
intersection_dim_2 = min(box_1[1]+0.5*box_1[3],box_2[1]+0.5*box_2[3])-\
max(box_1[1]-0.5*box_1[3],box_2[1]-0.5*box_2[3])
if intersection_dim_1 < 0 or intersection_dim_2 < 0 :
# no intersection area
intersection_area = 0
else :
# intersection area is product of intersection dimensions
intersection_area = intersection_dim_1*intersection_dim_2
# calculate the union area which is the area of each box added
# and then we need to subtract out the intersection area since
# it is counted twice (by definition it is in each box)
union_area = box_1[2]*box_1[3] + box_2[2]*box_2[3] - intersection_area;
# now we can return the intersection over union
iou = intersection_area / union_area
return iou
# モデル基本情報の表示
def display_info(input_shape, net_outputs, input_str, ir, labels, threshold, iou_threshold, device, log, titleflg, speedflg, outpath):
output_nodes = []
output_iter = iter(net_outputs)
for i in range(len(net_outputs)):
output_nodes.append(next(output_iter))
printG(f' - Program title : {title}')
printG(f' - OpenCV version : {cv2.__version__}')
printG(f' - OpenVINO engine: {get_version()}')
printY(f' - Input image : {input_str}')
printY(f' - Model : {ir}')
printY(f' - Device : {device}')
printY(f' - Labels File : {labels}')
printY(f' - Threshold : {threshold}')
printY(f' - Intersection Over Union: {iou_threshold}')
printY(f' - Log level : {log}')
printY(f' - Program Title : {titleflg}')
printY(f' - Speed flag : {speedflg}')
printY(f' - Processed out : {outpath}')
printN(f' - Input Shape : {input_shape}')
printN(' - Output Shapes :')
for j in range(len(output_nodes)):
printN(f' - output #{j} name : {output_nodes[j]}')
printN(f' - output shape: {net_outputs[output_nodes[j]].shape}')
# This function parses the output results from tiny yolo v3.
# The results are transposed so the output shape is (1, 13, 13, 255) or (1, 26, 26, 255). Original will be (1, 255, w, h).
# Tiny yolo does detection on two different scales using 13x13 grid and 26x26 grid.
# This is how the output is parsed:
# Imagine the image being split up into 13x13 or 26x26 grid. Each grid cell contains 3 anchor boxes.
# For each of those 3 anchor boxes, there are 85 values.
# 80 class probabilities + 4 coordinate values + 1 box confidence score = 85 values
# So that results in each grid cell having 255 values (85 values x 3 anchor boxes = 255 values)
def parseTinyYoloV3Output(output_node_results, filtered_objects, source_image_width, source_image_height, scaled_w, scaled_h, detection_threshold, num_labels):
# transpose the output node results
output_node_results = output_node_results.transpose(0,2,3,1)
output_h = output_node_results.shape[1]
output_w = output_node_results.shape[2]
# 80 class scores + 4 coordinate values + 1 objectness score = 85 values
# 85 values * 3 prior box scores per grid cell= 255 values
# 255 values * either 26 or 13 grid cells
num_of_classes = num_labels
num_anchor_boxes_per_cell = 3
# Set the anchor offset depending on the output result shape
anchor_offset = 0
if output_w == 13:
anchor_offset = 2 * 3
elif output_w == 26:
anchor_offset = 2 * 0
# used to calculate approximate coordinates of bounding box
x_ratio = float(source_image_width) / scaled_w
y_ratio = float(source_image_height) / scaled_h
# Filter out low scoring results
output_size = output_w * output_h
for result_counter in range(output_size):
row = int(result_counter / output_w)
col = int(result_counter % output_h)
for anchor_boxes in range(num_anchor_boxes_per_cell):
# check the box confidence score of the anchor box. This is how likely the box contains an object
box_confidence_score = output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 4]
if box_confidence_score < detection_threshold:
continue
# Calculate the x, y, width, and height of the box
x_center = (col + output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 0]) / output_w * scaled_w
y_center = (row + output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 1]) / output_h * scaled_h
width = np.exp(output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 2]) * anchors[anchor_offset + 2 * anchor_boxes]
height = np.exp(output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 3]) * anchors[anchor_offset + 2 * anchor_boxes + 1]
# Now we check for anchor box for the highest class probabilities.
# If the probability exceeds the threshold, we save the box coordinates, class score and class id
for class_id in range(num_of_classes):
class_probability = output_node_results[0][row][col][anchor_boxes * num_of_classes + 5 + 5 + class_id]
# Calculate the class's confidence score by multiplying the box_confidence score by the class probabiity
class_confidence_score = class_probability * box_confidence_score
if (class_confidence_score) < detection_threshold:
continue
# Calculate the bounding box top left and bottom right vertexes
xmin = max(int((x_center - width / 2) * x_ratio), 0)
ymin = max(int((y_center - height / 2) * y_ratio), 0)
xmax = min(int(xmin + width * x_ratio), source_image_width-1)
ymax = min(int(ymin + height * y_ratio), source_image_height-1)
filtered_objects.append((xmin, ymin, xmax, ymax, class_confidence_score, class_id))
# ** main関数 **
def main():
# 日本語フォント指定
fontFace = my_puttext.get_font()
# 入力パラメータの処理
ARGS = parse_args().parse_args()
input_str = ARGS.input
log = ARGS.log
logsel = int(log)
labels = ARGS.labels
titleflg = ARGS.title
speedflg = ARGS.speed
ir = ARGS.ir
detection_threshold = ARGS.threshold
iou_threshold = ARGS.iou
device = ARGS.device
outpath = ARGS.out
# ファイル処理クラスのインスタンス
my_file_treatment = my_file.FileTreatment(logsel)
# 入力ソースの違いによる前処理
if input_str == '0':
input_str = my_dialog.select_image_movie_file(initdir = '../../Videos')
if len(input_str) <= 0:
printR('Program canceled.')
return 0
input_stream = input_str
if input_str.lower() == "cam" or input_str.lower() == "camera":
input_stream = 0
isstream = True
else:
filetype = my_file_treatment.is_pict(input_stream)
isstream = filetype == 'None'
if (filetype == 'NotFound'):
printR(f"'{input_stream}': Input file Not found.")
return 0
if isstream and outpath != 'non' and os.path.splitext(outpath)[1] != '.mp4':
printR(f"'{outpath}': Output file type is '.mp4'")
return 0
# アプリケーション・ログ設定
module = os.path.basename(__file__)
module_name = os.path.splitext(module)[0]
logger = my_logging.get_module_logger_sel(module_name, logsel)
logger.info('Starting..')
# 分類ラベルの取得
with open(labels, encoding='utf-8') as labels_file:
label_list = labels_file.read().splitlines()
## 1. Create ie core and network ##
# Select the myriad plugin and IRs to be used
ie = IECore()
net = ie.read_network(model = ir, weights = ir[:-3] + 'bin')
# Set up the input blobs
input_key = list(net.input_info.keys())[0] # 入力データ・キー名
input_blob_name = net.input_info[input_key].name
input_blob = net.input_info[input_blob_name].name
input_shape = net.input_info[input_blob].input_data.shape
# 情報表示
display_info(input_shape, net.outputs, input_str, ir, labels, detection_threshold, iou_threshold, device, log, titleflg, speedflg, outpath)
# Load the network and get the network input shape information
exec_net = ie.load_network(network = net, device_name = device)
n, c, network_input_h, network_input_w = input_shape
# 関数内パラメータ
window_name = title + " (hit 'q' or 'esc' key to exit)"
# 入力準備
if (isstream):
# カメラ
cap = cv2.VideoCapture(input_stream)
success, img_frame = cap.read()
if success == False:
logger.error('camera read error !!')
loopflg = cap.isOpened()
img_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
img_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else:
# 画像ファイル読み込み
img_frame = cv2.imread(input_stream)
if img_frame is None:
logger.error('unable to read the input.')
return 0
# アスペクト比を固定してリサイズ（スクリーンに収まるサイズ）
h, w = my_movedlg.get_display_size()
mx = h - 10 if h > w else w - 10
img_frame = my_process.frame_resize(img_frame, mx)
loopflg = True # 1回ループ
img_width = img_frame.shape[1]
img_height = img_frame.shape[0]
# ウインドウ初期設定
cv2.namedWindow(window_name, flags=cv2.WINDOW_AUTOSIZE | cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow(window_name, 20, 0)
# 処理結果の記録 step1
if (outpath != 'non'):
if (isstream):
fps = int(cap.get(cv2.CAP_PROP_FPS))
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
outvideo = cv2.VideoWriter(outpath, fourcc, fps, (img_width, img_height))
# Width and height calculations. These will be used to scale the bounding boxes
source_image_width = img_frame.shape[1]
source_image_height = img_frame.shape[0]
scaled_w = int(source_image_width * min(network_input_w/source_image_width, network_input_w/source_image_height))
scaled_h = int(source_image_height * min(network_input_h/source_image_width, network_input_h/source_image_height))
# 計測値初期化
fpsWithTick = my_fps.fpsWithTick()
frame_count = 0
fps_total = 0
fpsWithTick.get() # fps計測開始
# メインループ
while (loopflg):
if img_frame is None:
logger.error('unable to read the input.')
return 0
frame = img_frame
## 2. Preprocessing ##
# Image preprocessing (resize, transpose, reshape)
input_image = cv2.resize(frame, (network_input_w, network_input_h), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = np.transpose(input_image, (2,0,1))
reshaped_image = input_image.reshape((n, c, network_input_h, network_input_w))
## 3. Perform Inference ##
## 4. Get results ##
all_output_results = exec_net.infer(inputs={input_blob: reshaped_image})
## 5. Post processing for results ##
# Post-processing for tiny yolo v3
# The post process consists of the following steps:
# 1. Parse the output and filter out low scores
# 2. Filter out duplicates using intersection over union
# 3. Draw boxes and text
### 1. Tiny yolo v3 has two outputs and we check/parse both outputs
filtered_objects = []
for output_node_results in all_output_results.values():
parseTinyYoloV3Output(output_node_results, filtered_objects, source_image_width, source_image_height, scaled_w, scaled_h, detection_threshold, len(label_list))
### 2. Filter out duplicate objects from all detected objects
filtered_mask = get_duplicate_box_mask(filtered_objects, iou_threshold)
### 3. Draw rectangles and set up display texts
for object_index in range(len(filtered_objects)):
if filtered_mask[object_index] == True:
# get all values from the filtered object list
xmin = filtered_objects[object_index][0]
ymin = filtered_objects[object_index][1]
xmax = filtered_objects[object_index][2]
ymax = filtered_objects[object_index][3]
confidence = filtered_objects[object_index][4]
class_id = filtered_objects[object_index][5]
# オブジェクト別の色指定
BOX_COLOR = my_color80.get_boder_bgr80(class_id)
LABEL_BG_COLOR = my_color80.get_back_bgr80(class_id)
# Set up the text for display
lablstr = label_list[class_id] + str(round(confidence * 100, 1)) + ' %'
x1, y1, x2, y2 = my_puttext.cv2_putText(frame, lablstr, (xmin+5, ymin+18), fontFace, 14, TEXT_COLOR, 0, True) # 描画範囲の取得
cv2.rectangle(frame,(xmin, ymin), (max(xmax, x2), ymin+20), LABEL_BG_COLOR, -1)
my_puttext.cv2_putText(frame, lablstr, (xmin+5, ymin+18), fontFace, 14, TEXT_COLOR, 0)
# Set up the bounding box
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), BOX_COLOR, 1)
# FPSを計算する
fps = fpsWithTick.get()
st_fps = 'fps: {:>6.2f}'.format(fps)
if (speedflg == 'y'):
cv2.rectangle(frame, (10, 38), (95, 55), (90, 90, 90), -1)
cv2.putText(frame, st_fps, (15, 50), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.4, color=(255, 255, 255), lineType=cv2.LINE_AA)
# タイトル描画
if (titleflg == 'y'):
cv2.putText(frame, title, (10 + 2, 30 + 2), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(0, 0, 0), lineType=cv2.LINE_AA)
cv2.putText(frame, title, (10, 30), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(200, 200, 0), lineType=cv2.LINE_AA)
# ウインドウの更新/画像表示
cv2.imshow(window_name, frame)
# 処理結果の記録 step2
if (outpath != 'non'):
if (isstream):
outvideo.write(frame)
else:
cv2.imwrite(outpath, frame)
# 何らかのキーが押されたら終了
breakflg = False
while(True):
key = cv2.waitKey(1) # (カメラ・アクセス速度調整)
if key == 27 or key == 113: # 'esc' or 'q'
breakflg = True
break
if not my_winstat._is_visible(window_name): # 'Close' button
breakflg = True
break
if (isstream):
break
if ((breakflg == False) and isstream):
# 次のフレームを読み出す
success, img_frame = cap.read()
if success == False: # 動画終了
break
loopflg = cap.isOpened()
else:
loopflg = False
# 終了処理
if (isstream):
cap.release()
# 処理結果の記録 step3
if (outpath != 'non'):
if (isstream):
outvideo.release()
cv2.destroyAllWindows()
del net
del exec_net
printC('FPS average: {:>10.2f}'.format(fpsWithTick.get_average()))
logger.info('Finished.\n')
# main関数エントリーポイント(実行開始)
if __name__ == "__main__":
sys.exit(main())
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