Semi-Autonomous-Robot / Semi Autonomous Robot - ECE 4180

Code for controlling mbed hardware (LED's, motors), as well as code for the Raspberry Pi to run a Support Vector Machine that identifies objects using the Pi camera

Dependencies:   mbed Motordriver mbed-rtos PololuLedStrip

Classifier.py@3:a3ed7ff99772, 2019-12-06 (annotated)

Committer:
arogliero3
Date:
Fri Dec 06 00:58:02 2019 -0500
Revision:
3:a3ed7ff99772
Parent:
0:e0dbd261724a 
update img6

Who changed what in which revision?

UserRevisionLine numberNew contents of line
arogliero3 0:e0dbd261724a 1 #!/usr/bin/env python
arogliero3 0:e0dbd261724a 2
arogliero3 0:e0dbd261724a 3 import numpy as np
arogliero3 0:e0dbd261724a 4 import re
arogliero3 0:e0dbd261724a 5 import scipy.stats
arogliero3 0:e0dbd261724a 6 from sklearn import svm, metrics, model_selection
arogliero3 0:e0dbd261724a 7 from skimage import io, feature, filters, exposure, color
arogliero3 0:e0dbd261724a 8 from joblib import dump, load
arogliero3 0:e0dbd261724a 9
arogliero3 0:e0dbd261724a 10
arogliero3 0:e0dbd261724a 11
arogliero3 0:e0dbd261724a 12 class ImageClassifier:
arogliero3 0:e0dbd261724a 13
arogliero3 0:e0dbd261724a 14 def __init__(self):
arogliero3 0:e0dbd261724a 15 self.classifer = None
arogliero3 0:e0dbd261724a 16
arogliero3 0:e0dbd261724a 17 def imread_convert(self, f):
arogliero3 0:e0dbd261724a 18 return io.imread(f).astype(np.uint8)
arogliero3 0:e0dbd261724a 19
arogliero3 0:e0dbd261724a 20 def load_data_from_folder(self, dir):
arogliero3 0:e0dbd261724a 21 # read all images into an image collection
arogliero3 0:e0dbd261724a 22 ic = io.ImageCollection(dir + "*.bmp", load_func=self.imread_convert)
arogliero3 0:e0dbd261724a 23
arogliero3 0:e0dbd261724a 24 # create one large array of image data
arogliero3 0:e0dbd261724a 25 data = io.concatenate_images(ic)
arogliero3 0:e0dbd261724a 26
arogliero3 0:e0dbd261724a 27 # extract labels from image names
arogliero3 0:e0dbd261724a 28 labels = np.array(ic.files)
arogliero3 0:e0dbd261724a 29 for i, f in enumerate(labels):
arogliero3 0:e0dbd261724a 30 m = re.search("_", f)
arogliero3 0:e0dbd261724a 31 labels[i] = f[len(dir):m.start()]
arogliero3 0:e0dbd261724a 32
arogliero3 0:e0dbd261724a 33 return (data, labels)
arogliero3 0:e0dbd261724a 34
arogliero3 0:e0dbd261724a 35 def extract_image_features(self, data):
arogliero3 0:e0dbd261724a 36 # Please do not modify the header above
arogliero3 0:e0dbd261724a 37
arogliero3 0:e0dbd261724a 38 # extract feature vector from image data
arogliero3 0:e0dbd261724a 39
arogliero3 0:e0dbd261724a 40 ########################
arogliero3 0:e0dbd261724a 41 ######## YOUR CODE HERE
arogliero3 0:e0dbd261724a 42 ########################
arogliero3 0:e0dbd261724a 43
arogliero3 0:e0dbd261724a 44 feature_data = []
arogliero3 0:e0dbd261724a 45
arogliero3 0:e0dbd261724a 46 for image in data:
arogliero3 0:e0dbd261724a 47 feature_data.append(
arogliero3 0:e0dbd261724a 48 feature.hog(image, pixels_per_cell=(25, 25), cells_per_block=(3, 3), feature_vector=True,
arogliero3 0:e0dbd261724a 49 block_norm="L2-Hys"))
arogliero3 0:e0dbd261724a 50
arogliero3 0:e0dbd261724a 51 # Please do not modify the return type below
arogliero3 0:e0dbd261724a 52 return (feature_data)
arogliero3 0:e0dbd261724a 53
arogliero3 0:e0dbd261724a 54 def train_classifier(self, train_data, train_labels):
arogliero3 0:e0dbd261724a 55 # Please do not modify the header above
arogliero3 0:e0dbd261724a 56
arogliero3 0:e0dbd261724a 57 # train model and save the trained model to self.classifier
arogliero3 0:e0dbd261724a 58
arogliero3 0:e0dbd261724a 59 ########################
arogliero3 0:e0dbd261724a 60 ######## YOUR CODE HERE
arogliero3 0:e0dbd261724a 61 ########################
arogliero3 0:e0dbd261724a 62
arogliero3 0:e0dbd261724a 63 # this param distribution dictionary was obtained from the sklearn documentation page
arogliero3 0:e0dbd261724a 64 param_distribution = {'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
arogliero3 0:e0dbd261724a 65 'kernel': ['rbf']}
arogliero3 0:e0dbd261724a 66
arogliero3 0:e0dbd261724a 67 model = model_selection.RandomizedSearchCV(svm.SVC(), param_distribution, cv=3, n_iter=6)
arogliero3 0:e0dbd261724a 68
arogliero3 0:e0dbd261724a 69 self.classifer = model.fit(train_data, train_labels)
arogliero3 0:e0dbd261724a 70
arogliero3 0:e0dbd261724a 71 def predict_labels(self, data):
arogliero3 0:e0dbd261724a 72 # Please do not modify the header
arogliero3 0:e0dbd261724a 73
arogliero3 0:e0dbd261724a 74 # predict labels of test data using trained model in self.classifier
arogliero3 0:e0dbd261724a 75 # the code below expects output to be stored in predicted_labels
arogliero3 0:e0dbd261724a 76
arogliero3 0:e0dbd261724a 77 ########################
arogliero3 0:e0dbd261724a 78 ######## YOUR CODE HERE
arogliero3 0:e0dbd261724a 79 ########################
arogliero3 0:e0dbd261724a 80
arogliero3 0:e0dbd261724a 81 predicted_labels = self.classifer.predict(data)
arogliero3 0:e0dbd261724a 82
arogliero3 0:e0dbd261724a 83 # Please do not modify the return type below
arogliero3 0:e0dbd261724a 84 return predicted_labels
arogliero3 0:e0dbd261724a 85
arogliero3 0:e0dbd261724a 86
arogliero3 0:e0dbd261724a 87 def main():
arogliero3 0:e0dbd261724a 88 img_clf = ImageClassifier()
arogliero3 0:e0dbd261724a 89
arogliero3 0:e0dbd261724a 90 # load images
arogliero3 0:e0dbd261724a 91 print("Loading Images")
arogliero3 0:e0dbd261724a 92 (train_raw, train_labels) = img_clf.load_data_from_folder('./train/')
arogliero3 0:e0dbd261724a 93 (test_raw, test_labels) = img_clf.load_data_from_folder('./test/')
arogliero3 0:e0dbd261724a 94
arogliero3 0:e0dbd261724a 95 # convert images into features
arogliero3 0:e0dbd261724a 96 print("Extracting features from images")
arogliero3 0:e0dbd261724a 97 train_data = img_clf.extract_image_features(train_raw)
arogliero3 0:e0dbd261724a 98 test_data = img_clf.extract_image_features(test_raw)
arogliero3 0:e0dbd261724a 99
arogliero3 0:e0dbd261724a 100 # train model and test on training data
arogliero3 0:e0dbd261724a 101 print("Training Model")
arogliero3 0:e0dbd261724a 102 img_clf.train_classifier(train_data, train_labels)
arogliero3 0:e0dbd261724a 103 predicted_labels = img_clf.predict_labels(train_data)
arogliero3 0:e0dbd261724a 104 print("\nTraining results")
arogliero3 0:e0dbd261724a 105 print("=============================")
arogliero3 0:e0dbd261724a 106 print("Confusion Matrix:\n", metrics.confusion_matrix(train_labels, predicted_labels))
arogliero3 0:e0dbd261724a 107 print("Accuracy: ", metrics.accuracy_score(train_labels, predicted_labels))
arogliero3 0:e0dbd261724a 108 print("F1 score: ", metrics.f1_score(train_labels, predicted_labels, average='micro'))
arogliero3 0:e0dbd261724a 109
arogliero3 0:e0dbd261724a 110 # test model
arogliero3 0:e0dbd261724a 111 predicted_labels = img_clf.predict_labels(test_data)
arogliero3 0:e0dbd261724a 112 print("\nTest results")
arogliero3 0:e0dbd261724a 113 print("=============================")
arogliero3 0:e0dbd261724a 114 print("Confusion Matrix:\n", metrics.confusion_matrix(test_labels, predicted_labels))
arogliero3 0:e0dbd261724a 115 print("Accuracy: ", metrics.accuracy_score(test_labels, predicted_labels))
arogliero3 0:e0dbd261724a 116 print("F1 score: ", metrics.f1_score(test_labels, predicted_labels, average='micro'))
arogliero3 0:e0dbd261724a 117
arogliero3 0:e0dbd261724a 118 dump(img_clf, "model.joblib")
arogliero3 0:e0dbd261724a 119
arogliero3 0:e0dbd261724a 120
arogliero3 0:e0dbd261724a 121 if __name__ == "__main__":
arogliero3 0:e0dbd261724a 122 main()