import create_segments
import sys
import logging
### Own modules
from general.hog import doHog
from general.pca import runPCA
from general.create_sets import create_sets
from train.svm import runSVM
from train.knn import runKNN
segmentDir = 'tmp/segments/'
featureDir = 'tmp/features/'
trainDir = featureDir + 'train/'
testDir = featureDir + 'test/'
for k in range(19):
k += 1
avgAcc = 0.0
for i in range(10):
print "hog"
doHog(segmentDir, featureDir, "xeryus")
print "create sets"
create_sets(featureDir)
print "knn"
kNN, accuracy = runKNN(trainDir, testDir, k, "hog")
avgAcc += accuracy
print "Average accuracy for k =", k, ":\t", avgAcc/10
avgAcc = 0
createSegments = False
if len(sys.argv) == 2:
createSegments = True
### Directories
segmentDir = 'tmp/segments/'
featureDir = 'tmp/features/'
trainDir = featureDir + 'train/'
### First segment the training images
if createSegments:
logging.info("Creating segments of the images")
create_segments.create_seg()
### Run hog over the segmented images
### Third argument for type of hog ("xeryus" or "other"), default = "xeryus"
logging.info("Doing the HOG")
doHog(segmentDir, featureDir)
### Create random train and test sets from the hog features
# logging.info("Creating train and test set")
create_sets(featureDir)
### Receive trained kNN (it also tests it on characters)
logging.info("Running kNN")
k = 10
kNN = runKNN(trainDir, k)
with open('tmp/knn.pickle', 'w') as f:
pickle.dump(kNN, f)
if __name__ == '__main__':
logging.config.fileConfig('logging.conf')
assert IMG_DIR.exists(), "You must first create segmented images"
block_sizes = ((1, 1), (2, 2), (3, 3), (4, 4))
cell_sizes = ((4, 4), (6, 6), (8, 8), (12, 12), (16, 16))
results = {}
start_time = time.time()
for cell_size in cell_sizes:
for block_size in block_sizes:
svm_time = time.time()
logging.info("Block size: %s Cell size: %s", block_size, cell_size)
logging.debug("Creating HOG features")
do_hog(block_size=block_size, cell_size=cell_size,
char_size=(88, 88), window_size=(96, 96))
logging.debug("Creating test and training sets")
create_sets(FEATURE_DIR)
logging.debug("Training SVM")
SVM, accuracy = runSVM(FEATURE_DIR + 'train/',
FEATURE_DIR + 'test/')
logging.info("SVM training time: %f s", time.time() - svm_time)
logging.info("SVM accuracy: %f", accuracy)
logging.info("Total execution time: %f s", time.time() - start_time)
from train.svm import runSVM
import create_segments
if __name__ == '__main__':
if len(sys.argv) >= 2 and sys.argv[1] != "--create_segments":
print("Usage: python %s [--create_segments]" % sys.argv[0])
need_create_segments = len(sys.argv) == 2
segmentDir = Path('tmp/segments/')
featureDir = Path('tmp/features/')
trainDir = featureDir + 'train/'
testDir = featureDir + 'test/'
if need_create_segments:
logging.info("Create segments of the images")
create_segments.create_seg()
logging.info("Creating HOG features")
doHog(segmentDir, featureDir)
logging.info("Creating train and test set")
create_sets(featureDir, 0.7)
logging.info("Running SVM")
SVM, accuracy = runSVM(trainDir, testDir)
logging.info("SVM accuracy: %f" % accuracy)
with open('tmp/svm.pickle', 'w') as f:
pickle.dump(SVM, f)
logging.info("Done")
