def trainSignDetector(train_list, label_encoder):
"""
train_list : list of users to use for training
eg ["user_1", "user_2", "user_3"]
"""
imageset, _ = helpers.getHandSet(train_list, 'Dataset/')
# Load data for the multiclass classification task
X_mul, Y_mul = get_data(train_list, imageset, 'Dataset/')
print("Multiclass data loaded")
Y_mul = label_encoder.fit_transform(Y_mul)
#Train Multiclass classifier using Sci-kit learn classifiers
"""TODO: Experiment with different models and hyperparameters to see what gives you the best results
#For comparison of some options, see http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html """
model = SVC(kernel='linear', C=0.9, probability=True)
signDetector = model.fit(X_mul, Y_mul)
print("Sign Detector Trained")
return signDetector
def get_accuracy(test_list, label_encoder, signDetector):
#initialize correct counter to 0
correct = 0
imageset, _ = helpers.getHandSet(test_list, 'Dataset/')
#get test data
X, Y = get_data(test_list, imageset, 'Dataset/')
Y = label_encoder.transform(Y)
#total is the number of examples in test data
total = len(X)
#for each example in test data
for i in range(len(X)):
#get the model's prediction for that example
outp = label_encoder.transform(
[helpers.predict(label_encoder, signDetector, X[i])])[0]
#if the prediction matched the correct label, increment the correct counter
if Y[i] == outp:
correct += 1
return correct / total
#label_encoder = LabelEncoder().fit(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N',
# 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y'])
#
#user_list =['user_3', 'user_4','user_5','user_6','user_7','user_9','user_10']
#
#trainlistsize = len(user_list)//2
#
#train_list = user_list[:trainlistsize]
#test_list = user_list[trainlistsize:]
#
#signDetector = trainSignDetector(train_list, label_encoder)
#matrix = get_confusion_matrix(test_list, label_encoder, signDetector)
#accuracy = get_accuracy(test_list, label_encoder, signDetector)
def get_accuracy(test_list, signDetector, handDetector, label_encoder):
#get dictionary of form {filename: imagearray}
imageset, _ = helpers.getHandSet(test_list, 'Dataset/')
#total number of examples in the test set
total = len(imageset)
#number of examples correctly classified
correct = 0
#incrementor to keep track of progress
inc = 0
#for each example in the test set
for key in imageset:
inc += 1
print(inc)
#get the predicted label
outp = label_encoder.transform([
recognize_gesture(imageset[key], handDetector, signDetector,
label_encoder)[2]
])
#get the actual label
label = label_encoder.transform([key.split('/')[2][0]])
#if predicted and actual labels match, increment number correct
if outp == label:
correct += 1
#accuracy is number correct / total number
return correct / total
def load_binary_data(user_list, data_directory):
data1, df = helpers.getHandSet(
user_list,
data_directory) # data 1 - actual images , df is actual bounding box
# third return, i.e., z is a list of hog vecs, labels
z = buildhandnothand_lis(df, data1)
return data1, df, z[0], z[1]
def get_confusion_matrix(test_list, label_encoder, signDetector):
#initialize all matrix values to 0
matrix = np.zeros((24, 24))
imageset, _ = helpers.getHandSet(test_list, 'Dataset/')
#get test data
X, Y = get_data(test_list, imageset, 'Dataset/')
Y = label_encoder.transform(Y)
#for each example in test data
for i in range(len(X)):
#get the model's prediction for that example
outp = label_encoder.transform(
[helpers.predict(label_encoder, signDetector, X[i])])[0]
#increment appropriate cell in matrix
matrix[int(Y[i])][int(outp)] += 1
return matrix
def get_accuracy(test_list, label_encoder, signDetector):
#initialize correct counter to 0
correct = 0
imageset, _ = helpers.getHandSet(test_list, 'Dataset/')
#get test data
X, Y = get_data(test_list, imageset, 'Dataset/')
Y = label_encoder.transform(Y)
#total is the number of examples in test data
total = len(X)
#for each example in test data
for i in range(len(X)):
#get the model's prediction for that example
outp = label_encoder.transform(
[helpers.predict(label_encoder, signDetector, X[i])])[0]
#if the prediction matched the correct label, increment the correct counter
if Y[i] == outp:
correct += 1
return correct / total
def get_confusion_matrix(test_list, signDetector, handDetector, label_encoder):
#initialize values to 0
matrix = np.zeros((24, 24))
#get dictionary of form {filename: imagearray}
imageset, _ = helpers.getHandSet(test_list, 'Dataset/')
#incrementor to keep track of progress
inc = 0
#for each example in the test set
for key in imageset:
inc += 1
print(inc)
#get the predicted label
outp = label_encoder.transform([
recognize_gesture(imageset[key], handDetector, signDetector,
label_encoder)[2]
])[0]
#get the actual label
label = label_encoder.transform([key.split('/')[2][0]])[0]
#increment the appropriate cell in the confusion matrix
matrix[label][outp] += 1
return matrix