def processDirectory(classifier, inputFolder, outputFolder=None):
logger = logging.getLogger("TestClassifier")
acceptableExtensions = ('jpg', 'jpeg', 'png')
try:
shutil.rmtree(os.path.join(outputFolder, 'positive'))
shutil.rmtree(os.path.join(outputFolder, 'negative'))
os.makedirs(os.path.join(outputFolder, 'positive'))
os.makedirs(os.path.join(outputFolder, 'negative'))
except OSError:
pass
for filename in os.listdir(inputFolder):
if filename.endswith(acceptableExtensions):
logger.debug('Processing %s' % (filename,))
image = Image(os.path.join(inputFolder, filename), windowSize=(30, 30), shiftSize=(3, 3))
# _, windows = image.process()
image.prepare()
print filename
s = ((np.array(image.image.shape) - np.array(image.windowSize)) // np.array(image.shiftSize)) + 1
print s
image.windowsAmountInfo = s
windows = sliding_window(image = image.image, windowSize=(30, 30),shiftSize=(3, 3), flatten=True)
l = windows.shape
windows = windows.reshape(l[0],900)
windows = np.insert(windows, 0, 0, axis=1 )
result = classifier.predict(windows)
print np.sum(result)
def slice_images(self, dir_in, dir_out, h_size, v_size):
"""
1. Read images from directory and slice they into small images
"""
# test
# A = np.arange(120).reshape((10, 12))
# print A
# slice_image_to_small_images(A, out_dir+'image_%04d.jpg')
image_files = FileHelper.read_images_in_dir(dir_in)
i = 0
for filename in image_files:
print filename.split(".")[0] + "_%04d.jpg"
# im = imread(os.path.join(dir_in, filename))
# im = rgb2gray(im)
im = Image(os.path.join(dir_in, filename))
im.prepare()
self.__slice_image_to_small_images(
im.image, os.path.join(dir_out, filename.split(".")[0] + "_%04d.jpg"), h_size, v_size
)
