本文整理匯總了Python中cv2.goodFeaturesToTrack方法的典型用法代碼示例。如果您正苦於以下問題:Python cv2.goodFeaturesToTrack方法的具體用法?Python cv2.goodFeaturesToTrack怎麽用?Python cv2.goodFeaturesToTrack使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cv2
的用法示例。
在下文中一共展示了cv2.goodFeaturesToTrack方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: center_from_good_features
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
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
}
示例2: extractFeatures
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def extractFeatures(img):
orb = cv2.ORB_create()
# detection
pts = cv2.goodFeaturesToTrack(np.mean(img, axis=2).astype(np.uint8), 3000, qualityLevel=0.01, minDistance=7)
# extraction
kps = [cv2.KeyPoint(x=f[0][0], y=f[0][1], _size=20) for f in pts]
kps, des = orb.compute(img, kps)
# return pts and des
return np.array([(kp.pt[0], kp.pt[1]) for kp in kps]), des
示例3: get_new_tracks
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def get_new_tracks(self, frame, roi):
"""Get new tracks every detect_interval frames."""
# Using mask to determine where to look for feature points.
mask = np.zeros_like(frame)
mask[roi[0]:roi[1], roi[2]:roi[3]] = 255
# Get good feature points.
feature_points = cv2.goodFeaturesToTrack(
frame, mask=mask, **self.feature_params)
if feature_points is not None:
for x, y in np.float32(feature_points).reshape(-1, 2):
self.tracks.append([(x, y)])
示例4: add_tracking_paths
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def add_tracking_paths(frame, tracking_paths):
mask = calculate_region_of_interest(frame, tracking_paths)
# Extract good features to track. You can learn more
# about the parameters here: http://goo.gl/BI2Kml
feature_points = cv2.goodFeaturesToTrack(frame, mask = mask, maxCorners = 500, \
qualityLevel = 0.3, minDistance = 7, blockSize = 7)
if feature_points is not None:
for x, y in np.float32(feature_points).reshape(-1, 2):
tracking_paths.append([(x, y)])
示例5: main
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def main():
image = cv2.imread("images/image_1.jpg")
keypoints = cv2.goodFeaturesToTrack(
cv2.cvtColor(image, cv2.COLOR_RGB2GRAY), maxCorners=100, qualityLevel=0.5, minDistance=5
).squeeze(1)
bboxes = [(kp[0] - 10, kp[1] - 10, kp[0] + 10, kp[1] + 10) for kp in keypoints]
disp_image = visualize(image, keypoints, bboxes)
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(disp_image, cv2.COLOR_RGB2BGR))
plt.tight_layout()
plt.show()
aug = A.Compose(
[A.ShiftScaleRotate(scale_limit=0.1, shift_limit=0.2, rotate_limit=10, always_apply=True)],
bbox_params=A.BboxParams(format="pascal_voc", label_fields=["bbox_labels"]),
keypoint_params=A.KeypointParams(format="xy"),
)
for _i in range(10):
data = aug(image=image, keypoints=keypoints, bboxes=bboxes, bbox_labels=np.ones(len(bboxes)))
aug_image = data["image"]
aug_image = visualize(aug_image, data["keypoints"], data["bboxes"])
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(aug_image, cv2.COLOR_RGB2BGR))
plt.tight_layout()
plt.show()
示例6: detect
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def detect(self, frame, mask=None):
pts = cv2.goodFeaturesToTrack(frame, self.num_features, self.quality_level, self.min_coner_distance, blockSize=self.blockSize, mask=mask)
# convert matrix of pts into list of keypoints
if pts is not None:
kps = [ cv2.KeyPoint(p[0][0], p[0][1], self.blockSize) for p in pts ]
else:
kps = []
#if kVerbose:
# print('detector: Shi-Tomasi, #features: ', len(kps), ', #ref: ', self.num_features, ', frame res: ', frame.shape[0:2])
return kps
示例7: detect
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def detect(self, img):
cnrs = cv2.goodFeaturesToTrack(img, self.maxCorners, self.qualityLevel, self.minDistance,
mask=self.mask, blockSize=self.blockSize,
useHarrisDetector=self.useHarrisDetector, k=self.k)
return corners_to_keypoints(cnrs)
示例8: getCorners
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def getCorners(img_in):
# number of features to track is a distinctive feature
## FeaturesToTrack important -> make accessible
edges = cv2.goodFeaturesToTrack(img_in, 640, 0.0008, 1, mask=None, blockSize=3, useHarrisDetector=1, k=0.06) # k=0.08
corners = np.int0(edges)
return corners
示例9: run
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
self.tracks = new_tracks
cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))
if self.frame_idx % self.detect_interval == 0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1]) for tr in self.tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([(x, y)])
self.frame_idx += 1
self.prev_gray = frame_gray
cv2.imshow('lk_track', vis)
ch = cv2.waitKey(1)
if ch == 27:
break
示例10: run
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if self.p0 is not None:
p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
self.p1 = p2[trace_status].copy()
self.p0 = self.p0[trace_status].copy()
self.gray1 = frame_gray
if len(self.p0) < 4:
self.p0 = None
continue
H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
h, w = frame.shape[:2]
overlay = cv2.warpPerspective(self.frame0, H, (w, h))
vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
if self.use_ransac:
draw_str(vis, (20, 40), 'RANSAC')
else:
p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if p is not None:
for x, y in p[:,0]:
cv2.circle(vis, (x, y), 2, green, -1)
draw_str(vis, (20, 20), 'feature count: %d' % len(p))
cv2.imshow('lk_homography', vis)
ch = cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
self.frame0 = frame.copy()
self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
if ch == ord('r'):
self.use_ransac = not self.use_ransac
示例11: _sparse_sd
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def _sparse_sd(data_instance,
of_params={'st_pars': dict(maxCorners = 200,
qualityLevel = 0.2,
minDistance = 7,
blockSize = 21),
'lk_pars': dict(winSize = (20, 20),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0))},
lead_steps=12):
# define penult and last frames
penult_frame = data_instance[-2]
last_frame = data_instance[-1]
# find features to track
old_corners = cv2.goodFeaturesToTrack(data_instance[0], mask=None,
**of_params['st_pars'])
# track corners by optical flow algorithm
new_corners, st, err = cv2.calcOpticalFlowPyrLK(prevImg=penult_frame,
nextImg=last_frame,
prevPts=old_corners,
nextPts=None,
**of_params['lk_pars'])
# select only good attempts for corner tracking
success = st.ravel() == 1
new_corners = new_corners[success].copy()
old_corners = old_corners[success].copy()
# calculate Simple Delta
delta = new_corners.reshape(-1, 2) - old_corners.reshape(-1, 2)
# simplificate furher transformations
pts_source = new_corners.reshape(-1, 2)
# propagate our corners through time
pts_target_container = []
for lead_step in range(lead_steps):
pts_target_container.append(pts_source + delta * (lead_step + 1))
return pts_source, pts_target_container
示例12: getMatches_goodtemplmatch
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def getMatches_goodtemplmatch(img1, img2, templ_shape, max):
"""Return pairs of corresponding points
using combining Shi-Tomasi corner detector and template matching."""
if not np.array_equal(img1.shape, img2.shape):
print "error: inconsistent array dimention", img1.shape, img2.shape
sys.exit()
if not (np.all(templ_shape <= img1.shape[:2]) and
np.all(templ_shape <= img2.shape[:2])):
print "error: template shape shall fit img1 and img2"
sys.exit()
feature_params = dict(maxCorners=max, qualityLevel=0.01,
minDistance=5, blockSize=5)
kps1 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params)
kps2 = cv2.goodFeaturesToTrack(img2, mask=None, **feature_params)
Hs, Ws = img1.shape[:2]
Ht, Wt = templ_shape
matches = []
for [[xt, yt]] in kps1:
if int(yt) + Ht > Hs or int(xt) + Wt > Ws:
continue
result = cv2.matchTemplate(
img2, img1[int(yt):int(yt) + Ht, int(xt):int(xt) + Wt],
cv2.TM_CCOEFF_NORMED)
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result)
if maxVal > 0.85:
matches.append((maxVal, (int(xt), int(yt)), maxLoc))
for [[xt, yt]] in kps2:
if int(yt) + Ht > Hs or int(xt) + Wt > Ws:
continue
result = cv2.matchTemplate(
img1, img2[int(yt):int(yt) + Ht, int(xt):int(xt) + Wt],
cv2.TM_CCOEFF_NORMED)
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result)
if maxVal > 0.85:
matches.append((maxVal, maxLoc, (int(xt), int(yt))))
matches.sort(key=lambda e: e[0], reverse=True)
if len(matches) >= max:
return np.int32([matches[i][1:] for i in range(max)])
else:
return np.int32([c[1:] for c in matches])
示例13: sparseOpticalFlow
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def sparseOpticalFlow():
# use 0 for webcam capturing
# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture('test/Pedestrian overpass.mp4')
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize = (15,15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
while(1):
ret,frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
# Select good points
good_new = p1[st==1]
good_old = p0[st==1]
# draw the tracks
for i,(new,old) in enumerate(zip(good_new,good_old)):
a,b = new.ravel()
c,d = old.ravel()
mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
img = cv2.add(frame,mask)
cv2.imshow('frame',img)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
# Now update the previous frame and previous points
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1,1,2)
cv2.destroyAllWindows()
cap.release()
# DENSE OPTICAL FLOW
示例14: updateError
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def updateError(self, frame):
self.frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, self.frame_gray
p0 = np.float32([tr[-1][:2] for tr in self.tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **self.lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **self.lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
self.xerror = 0.0
self.yerror = 0.0
self.n = 0.0
current_time = time.time()
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y, current_time))
if len(tr) > 500:
del tr[0]
new_tracks.append(tr)
if(len(tr)>=2):
t = np.float32([v[2] for v in tr])
x = np.float32([v[0] for v in tr])
y = np.float32([v[1] for v in tr])
self.xerror = self.xerror + (x[-1] - x[0])
self.yerror = self.yerror + (y[-1] - y[0])
self.n = self.n + 1.0
if self.n>0:
self.xerror = self.xerror / float(self.n)
self.yerror = self.yerror / float(self.n)
self.tracks = new_tracks
if self.xerror==0 and self.yerror==0:
current_time = time.time()
mask = np.zeros_like(self.frame_gray)
mask[:] = 255
p = cv2.goodFeaturesToTrack(self.frame_gray, mask = mask, **self.feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([(x, y, current_time)])
self.prev_gray = self.frame_gray
示例15: run
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import goodFeaturesToTrack [as 別名]
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
self.tracks = new_tracks
cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))
if self.frame_idx % self.detect_interval == 0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1]) for tr in self.tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([(x, y)])
self.frame_idx += 1
self.prev_gray = frame_gray
cv2.imshow('lk_track', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break