https://github.com/defi0x1/build-classifier-logisticregression

Classification countryside and metropilitian
https://github.com/defi0x1/build-classifier-logisticregression

binary-classification classification histogram-of-oriented-gradients hog-features kmeans-clustering logisticsregression opencv

Last synced: about 2 months ago
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Classification countryside and metropilitian

• Host: GitHub
• URL: https://github.com/defi0x1/build-classifier-logisticregression
• Owner: defi0x1
• Created: 2019-05-28T04:44:55.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-06-01T15:55:28.000Z (about 7 years ago)
• Last Synced: 2025-06-26T06:46:00.322Z (12 months ago)
• Topics: binary-classification, classification, histogram-of-oriented-gradients, hog-features, kmeans-clustering, logisticsregression, opencv
• Language: Python
• Homepage:
• Size: 9.89 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Classification-With-HOG-K-mean-Logistic

## Intuition

  - Build a Classifier to classify metropolitian & countryside

  - Using:

      - K-Means clustering, histogram (HOG) for prerocess data and extract features

      - LogisticsRegression for classify metropolitian and countryside

  - Data: [metropolitian](https://drive.google.com/open?id=1xjVkboqPbmeEnXPyN7D2tpNRxAOhdhEa) & [countryside](https://drive.google.com/open?id=1kJjjszN0nv5y2xfn3TWqVMJSu6aAa5K5)

## Architecture

  - Preprocess Images : Using opencv for imread data with function [load_image](https://github.com/minhhaui/Classification-With-HOG-K-mean-Logistic/blob/master/preprocess_image.py#L8) and resize output image (350,350)

  

  - K-Means Clustering : We will extract dominant colors with k = 5 centroid with function [setUPKmean](https://github.com/minhhaui/Classification-With-HOG-K-mean-Logistic/blob/master/preprocess_image.py#L35)

  

  - HOG (Histogram Oriented Gradients) : Construct the features vector for each image using 5 clustered RGB colors, sorted by color frequency.

  

  - LogisticRegression : We use [Logistic Regression](https://github.com/minhhaui/Classification-With-HOG-K-mean-Logistic/blob/master/setup_model.py#L6) model to classify the feature vectors built from the HOG.

    Implement the Gradient Descent method to optimize the model.

       ```

        batch_size = 32

        epochs = 10

        threshold = 0.5

        learning_rate = 0.001

        

       ````

  - Accuracy: 73.8 % with 1206 samples ( validation set = 20% , training set = 80%)

  - Output K-means Clustering with centroid = 5:

  

     

   - Vector Histogram:

   

     

   - Ouput HOG:  

   

     

     

  

# Tree Project

```

  Classification/

                model/

                      model.sav # model results after training

                resources_data/

                        countryside         # contains data of countryside ( have some image for intuition)

                        metropolitian       # contains data of metropilitian ( have some image for intuition)

                        image_predict       # contains image for test predict

                        output_image_test   # some results of k-mean and hog

                predict.py                  # predict some image from resources_data/image_predict with args pathto model

                preprocess_image.py         # contains imread data, fit k-mean and hog

                setup_model.py              # Build LogisicRegression from scatch

                train.py                    # start training with args path data train

                      

```

# USAGE:

## Requirements

   ```

      skimage

      opencv-python

      numpy

      matplotlib

      sklearn

   ```

## Training

  `python train.py --inputcountry path_to_countrydata/ --inputmetro path_to_metropolitiandata/`

## prediction

  `python predict.py --model path_to_model/ --image path_to_image/`