Diff: Classifier.py

Revision:

0:e0dbd261724a

diff -r 000000000000 -r e0dbd261724a Classifier.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Classifier.py	Thu Dec 05 20:34:10 2019 -0500
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+
+import numpy as np
+import re
+import scipy.stats
+from sklearn import svm, metrics, model_selection
+from skimage import io, feature, filters, exposure, color
+from joblib import dump, load
+
+
+
+class ImageClassifier:
+
+    def __init__(self):
+        self.classifer = None
+
+    def imread_convert(self, f):
+        return io.imread(f).astype(np.uint8)
+
+    def load_data_from_folder(self, dir):
+        # read all images into an image collection
+        ic = io.ImageCollection(dir + "*.bmp", load_func=self.imread_convert)
+
+        # create one large array of image data
+        data = io.concatenate_images(ic)
+
+        # extract labels from image names
+        labels = np.array(ic.files)
+        for i, f in enumerate(labels):
+            m = re.search("_", f)
+            labels[i] = f[len(dir):m.start()]
+
+        return (data, labels)
+
+    def extract_image_features(self, data):
+        # Please do not modify the header above
+
+        # extract feature vector from image data
+
+        ########################
+        ######## YOUR CODE HERE
+        ########################
+
+        feature_data = []
+
+        for image in data:
+            feature_data.append(
+                feature.hog(image, pixels_per_cell=(25, 25), cells_per_block=(3, 3), feature_vector=True,
+                            block_norm="L2-Hys"))
+
+        # Please do not modify the return type below
+        return (feature_data)
+
+    def train_classifier(self, train_data, train_labels):
+        # Please do not modify the header above
+
+        # train model and save the trained model to self.classifier
+
+        ########################
+        ######## YOUR CODE HERE
+        ########################
+
+        # this param distribution dictionary was obtained from the sklearn documentation page
+        param_distribution = {'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
+                              'kernel': ['rbf']}
+
+        model = model_selection.RandomizedSearchCV(svm.SVC(), param_distribution, cv=3, n_iter=6)
+
+        self.classifer = model.fit(train_data, train_labels)
+
+    def predict_labels(self, data):
+        # Please do not modify the header
+
+        # predict labels of test data using trained model in self.classifier
+        # the code below expects output to be stored in predicted_labels
+
+        ########################
+        ######## YOUR CODE HERE
+        ########################
+
+        predicted_labels = self.classifer.predict(data)
+
+        # Please do not modify the return type below
+        return predicted_labels
+
+
+def main():
+    img_clf = ImageClassifier()
+
+    # load images
+    print("Loading Images")
+    (train_raw, train_labels) = img_clf.load_data_from_folder('./train/')
+    (test_raw, test_labels) = img_clf.load_data_from_folder('./test/')
+
+    # convert images into features
+    print("Extracting features from images")
+    train_data = img_clf.extract_image_features(train_raw)
+    test_data = img_clf.extract_image_features(test_raw)
+
+    # train model and test on training data
+    print("Training Model")
+    img_clf.train_classifier(train_data, train_labels)
+    predicted_labels = img_clf.predict_labels(train_data)
+    print("\nTraining results")
+    print("=============================")
+    print("Confusion Matrix:\n", metrics.confusion_matrix(train_labels, predicted_labels))
+    print("Accuracy: ", metrics.accuracy_score(train_labels, predicted_labels))
+    print("F1 score: ", metrics.f1_score(train_labels, predicted_labels, average='micro'))
+
+    # test model
+    predicted_labels = img_clf.predict_labels(test_data)
+    print("\nTest results")
+    print("=============================")
+    print("Confusion Matrix:\n", metrics.confusion_matrix(test_labels, predicted_labels))
+    print("Accuracy: ", metrics.accuracy_score(test_labels, predicted_labels))
+    print("F1 score: ", metrics.f1_score(test_labels, predicted_labels, average='micro'))
+
+    dump(img_clf, "model.joblib")
+
+
+if __name__ == "__main__":
+    main()