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)
logging.info("Block size: %s Cell size: %s", block_size, cell_size)
logging.debug("Creating HOG features")
w = (cell_size[0] - (target_width % cell_size[0])) + target_width
h = (cell_size[1] - (target_height % cell_size[1])) + target_height
if cell_size[0] * block_size[0] > w or \
cell_size[1] * block_size[1] > h:
logging.info("Skipping parameters")
continue
for i in range(10):
svm_time = time.time()
do_hog(block_size=block_size,
cell_size=cell_size,
char_size=(w, h),
window_size=(w + cell_size[0], h + cell_size[1]))
logging.debug("Training SVM")
SVM, accuracy = runSVM(HOG_DIR + 'train/', HOG_DIR + 'test/')
logging.info("SVM training time: %f s", time.time() - svm_time)
logging.info("SVM accuracy: %f", accuracy)
with open(RESULTS_FILE, 'a') as f:
f.write('{},{},{},{},{},{},{},{}\n'.format(
num, cell_size[0], cell_size[1], block_size[0],
block_size[1], w, h, accuracy))
num += 1
logging.info("Total running time: %f s", time.time() - start_time)
h = (cell_size[1] - (target_height % cell_size[1])) + target_height
if cell_size[0] * block_size[0] > w or cell_size[1] * block_size[1] > h:
logging.info("Skipping parameters")
continue
for i in range(10):
svm_time = time.time()
do_hog(
block_size=block_size,
cell_size=cell_size,
char_size=(w, h),
window_size=(w + cell_size[0], h + cell_size[1]),
)
logging.debug("Training SVM")
SVM, accuracy = runSVM(HOG_DIR + "train/", HOG_DIR + "test/")
logging.info("SVM training time: %f s", time.time() - svm_time)
logging.info("SVM accuracy: %f", accuracy)
with open(RESULTS_FILE, "a") as f:
f.write(
"{},{},{},{},{},{},{},{}\n".format(
num, cell_size[0], cell_size[1], block_size[0], block_size[1], w, h, accuracy
)
)
num += 1
logging.info("Total running 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")