def track(self, frame):
"""
更新目标的新坐标，并返回新旧质心坐标
通过差分获取轮廓，Hu矩匹配
:param numpy.ndArray frame:
:return (Point, Point, list|None): 新旧坐标
"""
if self.__last_frame is None:
self.__last_frame = frame
return self.__position, self.__position, None
else:
img = cv2.absdiff(frame, self.__last_frame)
# 灰度处理后再高斯滤波，降低计算量
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(img, (5, 5), 2.5)
_, img = ImageUtils.binary(img, threshold_type=cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
_, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
min_semblance = -1
min_moments = None
min_index = -1
# 计算特征的相似度
for i, feature in enumerate(self._get_feature(contours)):
moments, hu_moments = feature
semblance = ImageUtils.compare_hu_moments(self.__target_hu_moments, hu_moments)
if semblance is not False and (semblance < min_semblance or min_semblance == -1):
min_semblance = semblance
min_moments = moments
min_index = i
self.__last_frame = frame
if min_moments:
old_position = self.__position
self.__position = ImageUtils.get_centroid(min_moments)
return old_position, self.__position, contours[min_index]
else:
return self.__position, self.__position, None
def __init__(self, target):
"""
构造函数
:param numpy.ndArray target: 要跟踪的目标
"""
self.__last_frame = None
_, self.__target = ImageUtils.binary(target, threshold_type=cv2.THRESH_OTSU)
self.__target_moments = cv2.moments(self.__target, True)
self.__target_hu_moments = cv2.HuMoments(self.__target_moments)
self.__position = ImageUtils.get_centroid(self.__target_moments)