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)
