for svm_c in HYPERPARAMETERS_OPTIONS["svm_c"]:
hyperparameters["svm_c"] = svm_c
print "HYPERPARAMETER: svm_c = " + str(svm_c)
# iterate over different svm_gamma
for svm_gamma in HYPERPARAMETERS_OPTIONS["svm_gamma"]:
hyperparameters["svm_gamma"] = svm_gamma
print "HYPERPARAMETER: svm_gamma = " + str(svm_gamma)
# train svm
print "---------------------"
print "## training svm"
algo.train_svm(
TMP_DIR_TRAINING,
all_labels,
SVM_MODEL_FILE + str(len(performances)),
all_weights=all_weights,
C=hyperparameters["svm_c"],
gamma=hyperparameters["svm_gamma"],
)
performance["hyperparameters"] = hyperparameters
# validate svm
print "---------------------"
print "## validating svm"
TP = 0
FP = 0
TN = 0
FN = 0
R = 0
all_weights[i] = certainty if label == 1 else 1-certainty
# extract features
featureCount = algo.extract_features(all_files, TMP_DIR)
# generate codebook
clusterCount = int(sqrt(featureCount))
algo.gen_codebook(TMP_DIR, clusterCount, SIFT_CODEBOOK,
batch_size = algo.BATCH_SIZE if algo.BATCH_SIZE >= clusterCount else clusterCount)
# generate histograms
algo.compute_histograms(TMP_DIR, SIFT_CODEBOOK, TMP_DIR)
# train svm
algo.train_svm(TMP_DIR, all_labels, SVM_MODEL_FILE, all_weights = all_weights)
print "calculating predictions"
predictions = algo.predict(SVM_MODEL_FILE, SIFT_CODEBOOK, DATASETPATH2, TMP_DIR)
img = Image.open('dop' +f + '/dop-annotated.png').convert('RGBA')
overlay = Image.new('RGBA', img.size, 0)
draw = ImageDraw.Draw(overlay)
print "\n\nPredictions:"
for filepath, is_building in predictions.items():
filename = os.path.basename(filepath)
coverage, x, y = os.path.splitext(filename)[0].split('_')
x = int(x); y = int(y)
