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main.py
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main.py
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import os
import pickle
from skimage.io import imread
from skimage.transform import resize
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# prepare data
input_dir = '/home/phillip/Desktop/todays_tutorial/19_parking_car_counter/code/clf-data'
categories = ['empty', 'not_empty']
data = []
labels = []
for category_idx, category in enumerate(categories):
for file in os.listdir(os.path.join(input_dir, category)):
img_path = os.path.join(input_dir, category, file)
img = imread(img_path)
img = resize(img, (15, 15))
data.append(img.flatten())
labels.append(category_idx)
data = np.asarray(data)
labels = np.asarray(labels)
# train / test split
x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)
# train classifier
classifier = SVC()
parameters = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
grid_search = GridSearchCV(classifier, parameters)
grid_search.fit(x_train, y_train)
# test performance
best_estimator = grid_search.best_estimator_
y_prediction = best_estimator.predict(x_test)
score = accuracy_score(y_prediction, y_test)
print('{}% of samples were correctly classified'.format(str(score * 100)))
pickle.dump(best_estimator, open('./model.p', 'wb'))