IMG = IMG.astype(int)
hist = color.histogram(IMG, type='global', n_bin=4)
assert np.equal(np.where(hist > 0)[0],
np.array([37, 43, 58,
61])).all(), "global histogram implement failed"
hist = color.histogram(IMG, type='region', n_bin=4, n_slice=2)
assert np.equal(np.where(hist > 0)[0],
np.array([58, 125, 165,
235])).all(), "region histogram implement failed"
# examinate distance
np.random.seed(1)
IMG = sigmoid(np.random.randn(4, 4, 3)) * 255
IMG = IMG.astype(int)
hist = color.histogram(IMG, type='region', n_bin=4, n_slice=2)
IMG2 = sigmoid(np.random.randn(4, 4, 3)) * 255
IMG2 = IMG2.astype(int)
hist2 = color.histogram(IMG2, type='region', n_bin=4, n_slice=2)
assert distance(hist, hist2, d_type='d1') == 2, "d1 implement failed"
assert distance(hist, hist2, d_type='d2-norm') == 2, "d2 implement failed"
# evaluate database
APs = evaluate_class(db, f_class=Color, d_type=d_type, depth=depth)
cls_MAPs = []
for cls, cls_APs in APs.items():
MAP = np.mean(cls_APs)
print("Class {}, MAP {}".format(cls, MAP))
cls_MAPs.append(MAP)
print("MMAP", np.mean(cls_MAPs))
def test_subcost(w1, w2):
result = evaluate.distance(W, vocab, w1, w2)
print(result)
min = -1
name = ""
for key, item in distance_dict.items():
if min == -1:
min = item
name = key
if item < min:
min = item
name = key
return name, min
if __name__ == "__main__":
color = method_select("HOG")
data = {}
for file in os.listdir(".\\database"):
start_time = time.time()
data[file] = color.histogram(".\\database\\" + file, type='global')
tt = time.time() - start_time
print("\t%5s" % (tt), "초")
assert data[file].sum() - 1 < 1e-9, "normalize false"
query = "05_16_오후DJI_0013.tif"
hist2 = color.histogram(query, type='global')
distance_dict = {}
for key, item in data.items():
distance_dict[key] = distance(item, hist2, d_type='d1')
for i in range(0, 5):
name, min = simple_min(distance_dict)
print(name, min)
distance_dict.pop(name)