def execute_GoodFeaturesToTrack(proxy,obj):
'''
https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.html
'''
try: img=obj.sourceObject.Proxy.img.copy()
except: img=cv2.imread(__dir__+'/icons/freek.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(gray,obj.maxCorners,obj.qualityLevel,obj.minDistance)
corners = np.int0(corners)
for i in corners:
x,y = i.ravel()
cv2.circle(img,(x,y),3,255,-1)
obj.Proxy.img = img
python类goodFeaturesToTrack()的实例源码
def center_from_good_features(matrix):
x, y = (0, 0)
weight = 0
corners = cv2.goodFeaturesToTrack(matrix, FEATURE_DETECT_MAX_CORNERS, FEATURE_DETECT_QUALITY_LEVEL,
FEATURE_DETECT_MIN_DISTANCE)
for point in corners:
weight += 1
x += point[0][0]
y += point[0][1]
return {
'x': x / weight,
'y': y / weight,
'count': weight
}
def _find_in_image(self, image, n_max):
"""
Calculate keypoints from a image
Using cv2.goodFeaturesToTrack to find good points
@param: image: np.ndarray
@return: a list
Cowards use this. The brave use '_key_points' which starts from scratch
"""
rows, cols = image.shape
kp = cv2.goodFeaturesToTrack(image, n_max, 0.01, 10, 3)
# assert kp.shape = (<number of keypoints>, 1, 2)
if kp is None:
return []
return [list(i) for i in kp.reshape(-1, 2)[:, ::-1].astype(np.int)]
def get_features_good(self, frame_gray):
"""
Jianbo Shi and Carlo Tomasi wrote a paper in 1994 called
"Good features to track", so now it's called that in OpenCV.
"""
strong_corners = cv2.goodFeaturesToTrack(
frame_gray,
mask=None,
**self.feature_params
)
return strong_corners
def get_features_good(self, frame_gray):
"""
Jianbo Shi and Carlo Tomasi wrote a paper in 1994 called
"Good features to track", so now it's called that in OpenCV.
"""
strong_corners = cv2.goodFeaturesToTrack(
frame_gray,
mask=None,
**self.feature_params
)
return strong_corners
def shi_tomasi(gray):
# image????
# maxCorners???????
# qualityLevel?????????????????????
# minDistance??????????
corners = cv2.goodFeaturesToTrack(gray,25,0.01,10)
cv2.computeCorrespondEpilines()
# ?????? [[ 311., 250.]] ????????
corners = np.int0(corners)
return corners
def detect(self, image):
gray = np.float32(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))
corners = cv2.goodFeaturesToTrack(gray, Configuration.gftt_max_corners, Configuration.gftt_quality_level, Configuration.gftt_min_distance)
return [x[0] for x in corners]
def extract(img):
return np.ravel(cv2.goodFeaturesToTrack(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 8, 0.08, 3))
def detectAllVertices(self, testImg):
# Detecting vertices on the newly constructed board
self.gray = cv2.cvtColor(testImg, cv2.COLOR_BGR2GRAY)
tempVertices = cv2.goodFeaturesToTrack(self.gray, int(self.FINAL_VERTICES_COUNT), 0.01, 10)
tempVertices = np.int0(tempVertices)
newVertices = []
for i in tempVertices:
x, y = i.ravel()
newVertices.append((x, y))
# Matrix to store coordinates of vertices on the board
self.ALL_VERTICES = [[(0, 0) for x in range(self.FACTOR + 2)] for x in range(self.FACTOR + 2)]
# Filling the matrix
self.ALL_VERTICES[0][0] = (self.CORNERS[1])
for i in range(0, self.FACTOR):
for j in range(0, self.FACTOR):
predicted_x = self.ALL_VERTICES[i][j][0] + int(
(self.OUTER_VERTICES[2][self.FACTOR - i][0] - self.OUTER_VERTICES[0][i][0]) / 8)
predicted_y = self.ALL_VERTICES[i][j][1] + int(
(self.OUTER_VERTICES[3][self.FACTOR - i][1] - self.OUTER_VERTICES[1][i][1]) / 8)
minn_dist = self.INT_MAX
for point in newVertices:
this_dist = Geometry.getPointsDistance(point, (predicted_x, self.ALL_VERTICES[i][j][1]))
if this_dist < minn_dist:
self.ALL_VERTICES[i][j + 1] = point
minn_dist = this_dist
minn_dist = self.INT_MAX
for point in newVertices:
this_dist = Geometry.getPointsDistance(point, (self.ALL_VERTICES[i][j][0], predicted_y))
if this_dist < minn_dist:
self.ALL_VERTICES[i + 1][j] = point;
minn_dist = this_dist
self.ALL_VERTICES[self.FACTOR][self.FACTOR] = (self.CORNERS[3])
