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 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 test_close(self):
"""
闭运算
"""
ret = ImageUtils.morphology(self.__image, cv2.MORPH_CLOSE, 8)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/close.jpg", ret)
def test_dilate(self):
"""
膨胀
"""
ret = ImageUtils.morphology(self.__image, cv2.MORPH_DILATE, 2)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/dilate.jpg", ret)
def test_erode(self):
"""
腐蚀
"""
ret = ImageUtils.morphology(self.__image, cv2.MORPH_ERODE, 2)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/erode.jpg", ret)
def setUp(self):
self.__image = cv2.imread("outputs/1_0.jpg")
if self.__image is None:
self.fail("读取样本失败")
_, img = ImageUtils.binary(self.__image,
threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/origin.jpg", img)
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)
def test_mean_blur(self):
"""
均值滤波
"""
ret = cv2.pyrMeanShiftFiltering(self.__image, 3, 3)
cv2.imwrite("result/mean_0.jpg", ret)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/mean_1.jpg", ret)
def test_gaussian_blur(self):
"""
高斯滤波
"""
ret = cv2.GaussianBlur(self.__image, (3, 3), 0.8)
cv2.imwrite("result/gaussian_0.jpg", ret)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/gaussian_1.jpg", ret)
def test_median_blur(self):
"""
中值滤波
"""
ret = cv2.medianBlur(self.__image, 1)
cv2.imwrite("result/median_0.jpg", ret)
_, ret = ImageUtils.binary(ret, threshold_type=cv2.THRESH_OTSU)
cv2.imwrite("result/median_1.jpg", ret)
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)
def _get_difference_frame(pre_frame, next_frame):
"""
帧进行差分并二值化，二值化使用OTSU算法
:param numpy.ndarray pre_frame: 前一帧
:param numpy.ndarray next_frame: 后一帧
:rtype: numpy.ndarray
:returns: 差分帧
"""
img = cv2.absdiff(pre_frame, next_frame)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, img = ImageUtils.binary(img,
threshold_type=cv2.THRESH_BINARY_INV
| cv2.THRESH_OTSU)
return img