def generate_hog_negative(folder, winW=400, winH=400):
from src.utils.features import HOG
from src.utils.io import filename2arr
from src.utils.util import sliding_window
X = []
Y = []
for dirName, subdirList, fileList in os.walk(folder):
for filename in fileList:
parts = filename.split('.')
if parts[0] == '':
continue
print "generating file " + dirName + '/' + filename
arr_big = filename2arr(dirName + '/' + filename)
for (arr, m, n) in sliding_window(arr_big, winW=winW, winH=winH):
fd = HOG(arr)
X.append(fd)
Y.append(0)
return X, Y
###################################
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
"""
###################################
extract features and do match
###################################
"""
# t_kp, t_des = sift_descriptor(window,show=False)
# from src.utils.features import matcher_Flann_KNN
# matcher_Flann_KNN(t_kp, t_des, window, m_kp, m_des, model_arr)
