Merge pull request #12 from CornellDataScience/pre-commit

Replace clang-format scripts with pre-commit hooks

.github/workflows/pre-commit.yml

@@ -0,0 +1,14 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v3
+    - uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -0,0 +1,19 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+    -   id: check-xml
+-   repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: 'v17.0.6'  # Use the sha / tag you want to point at
+    hooks:
+    -   id: clang-format
+-   repo: https://github.com/psf/black
+    rev: 22.10.0
+    hooks:
+    -   id: black

README.md

@@ -2,14 +2,17 @@
 
 [![cpp-linter](https://github.com/cpp-linter/cpp-linter-action/actions/workflows/cpp-linter.yml/badge.svg)](https://github.com/cpp-linter/cpp-linter-action/actions/workflows/cpp-linter.yml)
 
-# To Lint CPP
-Make sure git-clang-format is installed with `npm install -g clang-format`
-1. Call `clang-format.sh` under ci/
-2. `git add` and `git commit`!
+## Installing pre-commit hooks
 
-## To run 
+Install pre-commmit following the directions from [here](https://pre-commit.com/#install). If you're on a Mac with `homebrew`, the easiest way is `brew install pre-commit`.
 
-`cd build` 
+Then, run `pre-commit install`. You only need to do this once.
+
+Now, pre-commit will run automatically when you make a commit, and will fix files for you. Make sure to add the changed files after pre-commit runs!
+
+## To run
+
+`cd build`
 
 `cmake ..`
 
@@ -21,7 +24,7 @@ Make sure git-clang-format is installed with `npm install -g clang-format`
 
 - `cd` to `src/message/`
 
-- Make a `.env` file with `TWILIO_ACCOUNT_SID` and `TWILIO_AUTH_TOKEN` set to your account SID and Auth Token 
+- Make a `.env` file with `TWILIO_ACCOUNT_SID` and `TWILIO_AUTH_TOKEN` set to your account SID and Auth Token
 
 - macOS: `brew tap twilio/brew && brew install twilio`
 

src/haarcascade_frontalface_default.xml

@@ -33311,4 +33311,4 @@
           12 1 3 11 2.</_>
         <_>
           9 12 3 11 2.</_></rects></_></features></cascade>
-</opencv_storage>
+</opencv_storage>

src/model/EdgeTraining.py

@@ -11,9 +11,7 @@
 
 data_path = "realtime"
 
-train_path = os.path.join(
-        data_path, "train.pickle"
-    )  # A path for the train file
+train_path = os.path.join(data_path, "train.pickle")  # A path for the train file
 
 siamese = SiameseNetwork(
     seed=seed,
@@ -27,21 +25,21 @@
 )
 
 loader = DataLoader(
-            width=WIDTH,
-            height=HEIGHT,
-            cells=CEELS,
-            data_path=data_path,
-            output_path=train_path,
-        )
+    width=WIDTH,
+    height=HEIGHT,
+    cells=CEELS,
+    data_path=data_path,
+    output_path=train_path,
+)
 loader.load(set_name="train")
 
 siamese.fit(
-        weights_file="weights/weights.h5",
-        train_path=train_path,
-        validation_size=0.2,
-        batch_size=32,
-        epochs=2,
-        early_stopping=True,
-        patience=5,
-        min_delta=0.1,
-    )
+    weights_file="weights/weights.h5",
+    train_path=train_path,
+    validation_size=0.2,
+    batch_size=32,
+    epochs=2,
+    early_stopping=True,
+    patience=5,
+    min_delta=0.1,
+)

src/model/Siamese_Predictor.py

@@ -41,6 +41,7 @@ def preprocess_image(filename):
 
     return img
 
+
 # load the model
 siamese = SiameseNetwork(
     seed=seed,

src/model/message/sms_message.py

@@ -1,20 +1,17 @@
-import os 
-from twilio.rest import Client 
+import os
+from twilio.rest import Client
 from dotenv import load_dotenv
 
 load_dotenv()
 
-account_sid = os.getenv('TWILIO_ACCOUNT_SID')
-auth_token = os.getenv('TWILIO_AUTH_TOKEN')
+account_sid = os.getenv("TWILIO_ACCOUNT_SID")
+auth_token = os.getenv("TWILIO_AUTH_TOKEN")
+
+from_number = "+18886815709"
+to_number = "+19788814542"
 
-from_number =   '+18886815709'
-to_number =     '+19788814542'
 
 def send_message(intruder_detected):
     msg = "Intruder Alert!" if intruder_detected else "Welcome!"
     client = Client(account_sid, auth_token)
-    message = client.messages.create(
-                        body=msg,
-                        from_=from_number,
-                        to=to_number)
-
+    message = client.messages.create(body=msg, from_=from_number, to=to_number)

src/model/realtime/splits/train.txt

@@ -2,4 +2,4 @@ David_Han     1 5
 David_Han     2 6
 David_Han     3 7
 David_Han     4 8
-David_Han     5 10
+David_Han     5 10

src/model/utils.py

@@ -2,6 +2,7 @@
 
 target_shape = (200, 200)
 
+
 def preprocess_image(filename):
     """
     Load the specified file as a JPEG image, preprocess it and
@@ -25,4 +26,4 @@ def preprocess_triplets(anchor, positive, negative):
         preprocess_image(anchor),
         preprocess_image(positive),
         preprocess_image(negative),
-    )
+    )

utils/haarcascade_frontalface_default.xml

@@ -33311,4 +33311,4 @@
           12 1 3 11 2.</_>
         <_>
           9 12 3 11 2.</_></rects></_></features></cascade>
-</opencv_storage>
+</opencv_storage>

utils/motion_detection.cpp

@@ -1,45 +1,42 @@
-#include <opencv2/opencv.hpp>
 #include <iostream>
+#include <opencv2/opencv.hpp>
 
 const double MIN_CONTOUR_SIZE = 3000; // Adjust this threshold as needed
 
-bool detect_motion(std::string im_path1, std::string im_path2)
-{
-
-    // load images
-    cv::Mat image1 = cv::imread(im_path1);
-    cv::Mat image2 = cv::imread(im_path2);
-
-    // preprocess
-    cv::Mat gray1, gray2;
-
-    cv::cvtColor(image1, gray1, cv::COLOR_BGR2GRAY);
-    cv::cvtColor(image2, gray2, cv::COLOR_BGR2GRAY);
-    cv::GaussianBlur(gray1, gray1, cv::Size(21, 21), 0);
-    cv::GaussianBlur(gray2, gray2, cv::Size(21, 21), 0);
-
-    cv::Mat diff;
-    cv::absdiff(gray1, gray2, diff);
-
-    cv::Mat thresh;
-    cv::threshold(diff, thresh, 25, 255, cv::THRESH_BINARY);
-
-    std::vector<std::vector<cv::Point>> contours;
-    cv::findContours(thresh, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
-
-    for (int i = 0; i < contours.size(); i++)
-    {
-        cv::drawContours(image2, contours, i, cv::Scalar(0, 255, 0), 2);
-    }
+bool detect_motion(std::string im_path1, std::string im_path2) {
+
+  // load images
+  cv::Mat image1 = cv::imread(im_path1);
+  cv::Mat image2 = cv::imread(im_path2);
+
+  // preprocess
+  cv::Mat gray1, gray2;
+
+  cv::cvtColor(image1, gray1, cv::COLOR_BGR2GRAY);
+  cv::cvtColor(image2, gray2, cv::COLOR_BGR2GRAY);
+  cv::GaussianBlur(gray1, gray1, cv::Size(21, 21), 0);
+  cv::GaussianBlur(gray2, gray2, cv::Size(21, 21), 0);
+
+  cv::Mat diff;
+  cv::absdiff(gray1, gray2, diff);
+
+  cv::Mat thresh;
+  cv::threshold(diff, thresh, 25, 255, cv::THRESH_BINARY);
+
+  std::vector<std::vector<cv::Point>> contours;
+  cv::findContours(thresh, contours, cv::RETR_EXTERNAL,
+                   cv::CHAIN_APPROX_SIMPLE);
+
+  for (int i = 0; i < contours.size(); i++) {
+    cv::drawContours(image2, contours, i, cv::Scalar(0, 255, 0), 2);
+  }
 
-    bool motionDetected = false;
-    for (int i = 0; i < contours.size(); i++)
-    {
-        if (cv::contourArea(contours[i]) > MIN_CONTOUR_SIZE)
-        {
-            motionDetected = true;
-            break;
-        }
+  bool motionDetected = false;
+  for (int i = 0; i < contours.size(); i++) {
+    if (cv::contourArea(contours[i]) > MIN_CONTOUR_SIZE) {
+      motionDetected = true;
+      break;
     }
-    return motionDetected;
+  }
+  return motionDetected;
 }