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 )