WIP

.pre-commit-config.yaml

@@ -0,0 +1,82 @@
+# can be used to exclude certain regex patterns or paths
+exclude: '^$'
+# if set to true, fails on first failure
+fail_fast: false
+repos:
+  # The following pre-commit hooks could be very useful.
+  # They are not part of CI/CD, so use or remove them as you please
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+    - id: trailing-whitespace
+    - id: end-of-file-fixer
+    - id: debug-statements
+    - id: name-tests-test
+      args: [
+        "--pytest-test-first" # test_.*\.py
+        #"--pytest" # .*_test\.py
+        #"--unittest" # test.*\.py
+      ]
+    - id: check-added-large-files
+    - id: check-docstring-first
+    - id: check-executables-have-shebangs
+    - id: check-shebang-scripts-are-executable
+    - id: check-merge-conflict
+    - id: check-yaml
+      args: ['--unsafe']
+    - id: check-toml
+    - id: check-xml
+    - id: detect-private-key
+
+  - repo: https://gitlab.com/bmares/check-json5
+    rev: v1.0.0
+    hooks:
+    - id: check-json5
+
+  - repo: https://github.com/DavidAnson/markdownlint-cli2
+    rev: v0.13.0
+    hooks:
+      - id: markdownlint-cli2
+        args: [ "--fix", "--config", ".markdownlint.yaml" ]
+
+  # part of CI/CD
+  - repo: local
+    hooks:
+      - id: poetry-check
+        name: poetry-check --lock
+        description: run poetry check to validate config
+        entry: poetry check --lock
+        language: python
+        pass_filenames: false
+        files: ^(.*/)?pyproject\.toml$
+#      - id: poetry-export
+#        name: poetry-export
+#        description: run poetry export to sync lock file with requirements.txt
+#        entry: poetry export
+#        language: python
+#        pass_filenames: false
+#        files: ^(.*/)?poetry\.lock$
+#        args: ["-f", "requirements.txt", "-o", "requirements.txt"]
+#      - id: poetry-export-dev
+#        name: poetry-export --with-dev
+#        description: run poetry export to sync lock file with requirements.txt
+#        entry: poetry export
+#        language: python
+#        pass_filenames: false
+#        files: ^(.*/)?poetry\.lock$
+#        args: ["--with", "dev", "-f", "requirements.txt", "-o", "requirements-dev.txt"]
+
+      - id: ruff-format
+        name: ruff-format
+        description: "Run 'ruff format' for extremely fast Python formatting"
+        entry: ruff format --force-exclude
+        language: python
+        types_or: [python, pyi]
+        args: []
+      - id: ruff
+        name: ruff
+        description: Run 'ruff' for extremely fast Python linting
+        entry: ruff check --force-exclude
+        language: python
+        types_or: [python, pyi]
+        args: [--fix]

README.md

@@ -38,10 +38,10 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
     ```bash
     # With pip:
     pip install facenet-pytorch
-    
+
     # or clone this repo, removing the '-' to allow python imports:
     git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
-    
+
     # or use a docker container (see https://github.com/timesler/docker-jupyter-dl-gpu):
     docker run -it --rm timesler/jupyter-dl-gpu pip install facenet-pytorch && ipython
     ```
@@ -50,10 +50,10 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
 
     ```py
     from facenet_pytorch import MTCNN, InceptionResnetV1
-    
+
     # If required, create a face detection pipeline using MTCNN:
     mtcnn = MTCNN(image_size=<image_size>, margin=<margin>)
-    
+
     # Create an inception resnet (in eval mode):
     resnet = InceptionResnetV1(pretrained='vggface2').eval()
     ```
@@ -62,7 +62,7 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
 
     ```py
     from PIL import Image
-    
+
     img = Image.open(<image path>)
 
     # Get cropped and prewhitened image tensor
@@ -176,7 +176,7 @@ The package and any of the example notebooks can be run with docker (or nvidia-d
 docker run --rm -p 8888:8888
     -v ./facenet-pytorch:/home/jovyan timesler/jupyter-dl-gpu \
     -v <path to data>:/home/jovyan/data
-    pip install facenet-pytorch && jupyter lab 
+    pip install facenet-pytorch && jupyter lab
 ```
 
 Navigate to the examples/ directory and run any of the ipython notebooks.

README_cn.md

@@ -72,7 +72,7 @@ Pytorch 模型权重使用从 David Sandberg 的 [tensorflow Facenet repo](https
 ## 快速启动
 
 1. 安装：
-   
+
 ````bash
 # 使用pip安装：
 pip install facenet-pytorch
@@ -85,7 +85,7 @@ docker run -it --rm timesler/jupyter-dl-gpu pip install facenet-pytorch && ipyth
 ````
 
 2. 在python中，导入 facenet-pytorch 并实例化模型：
-   
+
 ````python
 from facenet_pytorch import MTCNN, InceptionResnetV1
 
@@ -97,7 +97,7 @@ resnet = InceptionResnetV1(pretrained='vggface2').eval()
 ````
 
 3. 处理图像：
-   
+
 ````python
 from PIL import Image
 
@@ -214,7 +214,7 @@ MTCNN 可用于构建人脸跟踪系统（使用 `MTCNN.detect()` 方法）。
 docker run --rm -p 8888:8888
     -v ./facenet-pytorch:/home/jovyan timesler/jupyter-dl-gpu \
     -v <path to data>:/home/jovyan/data
-    pip install facenet-pytorch && jupyter lab 
+    pip install facenet-pytorch && jupyter lab
 ````
 
 导航到 example/ 目录并运行任何 ipython 笔记本。

codecov.yml

@@ -2,5 +2,5 @@ coverage:
   status:
     project: off
     patch: off
-codecov: 
+codecov:
   token: 1e4f3aaa-9c74-4888-9408-71cc7fcfb64c

examples/face_tracking_cn.ipynb

@@ -22,8 +22,8 @@
    "source": [
     "from facenet_pytorch import MTCNN\n",
     "import torch\n",
-    "import numpy as np\n",
     "import mmcv, cv2\n",
+    "import numpy as np\n",
     "from PIL import Image, ImageDraw\n",
     "from IPython import display"
    ]
@@ -158,16 +158,16 @@
     "frames_tracked = []\n",
     "for i, frame in enumerate(frames):\n",
     "    print('\\r当前帧: {}'.format(i + 1), end='')\n",
-    "    \n",
+    "\n",
     "    # 检测人脸\n",
     "    boxes, _ = mtcnn.detect(frame)\n",
-    "    \n",
+    "\n",
     "    # 绘制人脸框\n",
     "    frame_draw = frame.copy()\n",
     "    draw = ImageDraw.Draw(frame_draw)\n",
-    "    for box in boxes:\n",
+    "    for box in boxes[0]\n",
     "        draw.rectangle(box.tolist(), outline=(255, 0, 0), width=6)\n",
-    "    \n",
+    "\n",
     "    # 添加到图像列表\n",
     "    frames_tracked.append(frame_draw.resize((640, 360), Image.BILINEAR))\n",
     "print('\\n结束')"
@@ -232,10 +232,10 @@
    "outputs": [],
    "source": [
     "dim = frames_tracked[0].size\n",
-    "fourcc = cv2.VideoWriter_fourcc(*'FMP4')    \n",
+    "fourcc = cv2.VideoWriter_fourcc(*'FMP4')\n",
     "video_tracked = cv2.VideoWriter('video_tracked.mp4', fourcc, 25.0, dim)\n",
     "for frame in frames_tracked:\n",
-    "    video_tracked.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\n",
+    "    video_tracked.write(np.asarray(frame)[:, :, ::-1])\n",
     "video_tracked.release()"
    ]
   }

examples/infer.ipynb

@@ -20,14 +20,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "import os\n",
-    "\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
+    "import os\n",
     "\n",
     "workers = 0 if os.name == 'nt' else 4"
    ]
@@ -119,10 +118,7 @@
     "def collate_fn(x):\n",
     "    return x[0]\n",
     "\n",
-    "root_dir = Path(__file__).parent.parent\n",
-    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
-    "\n",
-    "dataset = datasets.ImageFolder(data_dir)\n",
+    "dataset = datasets.ImageFolder('../data/test_images')\n",
     "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
     "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
    ]

examples/infer_cn.ipynb

@@ -26,14 +26,13 @@
    },
    "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "import os\n",
-    "\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
+    "import os\n",
     "\n",
     "workers = 0 if os.name == 'nt' else 4"
    ]
@@ -145,10 +144,7 @@
     "def collate_fn(x):\n",
     "    return x[0]\n",
     "\n",
-    "root_dir = Path(__file__).parent.parent\n",
-    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
-    "\n",
-    "dataset = datasets.ImageFolder(data_dir)\n",
+    "dataset = datasets.ImageFolder('../data/test_images')\n",
     "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
     "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
    ]

examples/lfw_evaluate.ipynb

@@ -15,12 +15,13 @@
    "execution_count": 1,
    "outputs": [],
    "source": [
-    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training, extract_face\n",
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
     "import torch\n",
-    "from torch.utils.data import DataLoader, SubsetRandomSampler, SequentialSampler\n",
+    "from torch.utils.data import DataLoader, SequentialSampler\n",
     "from torchvision import datasets, transforms\n",
     "import numpy as np\n",
-    "import os"
+    "import os\n",
+    "import math"
    ],
    "metadata": {
     "collapsed": false,
@@ -487,8 +488,7 @@
    ],
    "source": [
     "print(accuracy)\n",
-    "np.mean(accuracy)\n",
-    "\n"
+    "np.mean(accuracy)"
    ],
    "metadata": {
     "collapsed": false,

examples/lfw_evaluate_cn.ipynb

@@ -23,12 +23,13 @@
    },
    "outputs": [],
    "source": [
-    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training, extract_face\n",
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
     "import torch\n",
-    "from torch.utils.data import DataLoader, SubsetRandomSampler, SequentialSampler\n",
+    "from torch.utils.data import DataLoader, SequentialSampler\n",
     "from torchvision import datasets, transforms\n",
     "import numpy as np\n",
-    "import os"
+    "import os\n",
+    "import math"
    ]
   },
   {

facenet_pytorch/__init__.py

@@ -5,10 +5,18 @@
 from facenet_pytorch.models.utils import training
 from facenet_pytorch.models.utils.detect_face import extract_face
 
-__all__ = ["InceptionResnetV1", "MTCNN", "ONet", "PNet", "RNet", "fixed_image_standardization", "prewhiten", "training", "extract_face"]
+__all__ = [
+    "InceptionResnetV1",
+    "MTCNN",
+    "ONet",
+    "PNet",
+    "RNet",
+    "fixed_image_standardization",
+    "prewhiten",
+    "training",
+    "extract_face",
+]
 
 warnings.filterwarnings(
-    action="ignore",
-    message="This overload of nonzero is deprecated:\n\tnonzero()",
-    category=UserWarning,
+    action="ignore", message="This overload of nonzero is deprecated:\n\tnonzero()", category=UserWarning
 )