from src.utils.preprocessing import preprocessing target = preprocessing(target_arr) model = preprocessing(model_arr) """ ################################### constructing pyramids and use sliding windows to match features. ################################### """ from src.utils.features import sift_descriptor m_kp, m_des = sift_descriptor(model, show=False) from skimage.transform import pyramid_gaussian from src.utils.util import sliding_window winH, winW = 400, 400 # for (i, resized) in enumerate(pyramid_gaussian(target, downscale=2)): for resized in [target]: if resized.shape[0] < winH or resized.shape[1] < winW: break for (x, y, window) in sliding_window(resized): if window.shape[0] != winH or window.shape[1] != winW or window.max() == 0: continue lighter = float(window[np.where(window > (window.max() - 100))].size) if lighter / window.size < 0.1: continue """
import os import sys import inspect from src.utils.io import filename2arr """ ############################### set env path ############################### """ tests_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # script directory src/ src_dir = os.path.dirname(tests_dir) xray_dir = os.path.dirname(src_dir) #xray directory os.chdir(xray_dir) sys.path.append(src_dir) filenum = '1' targetfn = xray_dir+ '/data/LL/' + filenum + '.jpg' target_arr = filename2arr(targetfn) from src.utils.preprocessing import preprocessing target = preprocessing(target_arr) from src.utils.features import sift_descriptor t_kp, t_des = sift_descriptor(target,show=True)