def test_prosac(self):
"""
改进抽样一致
"""
Camera.reset()
self.__last_frame = None
print("PROSAC")
for i in range(1, 30):
start = time.clock()
_, frame = Camera.get_frame()
if self.__last_frame is None:
self.__last_frame = frame
continue
img = cv2.absdiff(frame, self.__last_frame)
self.__last_frame = frame
img = cv2.GaussianBlur(img, (5, 5), 2.5)
img = ImageUtils.morphology(img, cv2.MORPH_DILATE, 16)
_, img = ImageUtils.binary(img, threshold_type=cv2.THRESH_OTSU)
# 计算特征点
key_points, descriptors = ImageUtils.get_key_points(frame, img)
matches = ImageUtils.knn_match(self.__target_descriptors,
descriptors)
if len(matches) > 0:
# 匹配到合适的特征点
points = ImageUtils.get_matches_points(key_points, matches)
src_key_points = ImageUtils.get_matches_points(
self.__target_key_points, matches, 1)
# PROSAC去除错误点
_, mask = cv2.findHomography(src_key_points, points, cv2.RHO)
if mask is not None:
points_after = points[mask.ravel() == 1]
end = time.clock()
print("%d\t%d\t%d\t%f" %
(i, len(points), len(points_after), end - start))
def setUp(self):
target = cv2.imread("result/track_target.jpg")
mask = cv2.imread("result/mask.jpg")
self.__target = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
_, self.__mask = ImageUtils.binary(mask,
threshold_type=cv2.THRESH_OTSU)
self.__target_key_points, self.__target_descriptors = ImageUtils.get_key_points(
self.__target, self.__mask)