def lktrack(img1,
img2,
ptsI,
nPtsI,
winsize_ncc=10,
win_size_lk=4,
method=cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Lucas-Kanede Tracker with pyramids
**PARAMETERS**
img1 - Previous image or image containing the known bounding box (Numpy array)
img2 - Current image
ptsI - Points to track from the first image
Format ptsI[0] - x1, ptsI[1] - y1, ptsI[2] - x2, ..
nPtsI - Number of points to track from the first image
winsize_ncc - size of the search window at each pyramid level in LK tracker (in int)
method - Paramete specifying the comparison method for normalized cross correlation
(see http://opencv.itseez.com/modules/imgproc/doc/object_detection.html?highlight=matchtemplate#cv2.matchTemplate)
**RETURNS**
fb - forward-backward confidence value. (corresponds to euclidean distance between).
ncc - normCrossCorrelation values
status - Indicates positive tracks. 1 = PosTrack 0 = NegTrack
ptsJ - Calculated Points of second image
"""
template_pt = []
target_pt = []
fb_pt = []
ptsJ = [-1] * len(ptsI)
for i in range(nPtsI):
template_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
target_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
fb_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
template_pt = np.asarray(template_pt, dtype="float32")
target_pt = np.asarray(target_pt, dtype="float32")
fb_pt = np.asarray(fb_pt, dtype="float32")
target_pt, status, track_error = cv2.calcOpticalFlowPyrLK(
img1,
img2,
template_pt,
target_pt,
winSize=(win_size_lk, win_size_lk),
flags=cv.OPTFLOW_USE_INITIAL_FLOW,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
fb_pt, status_bt, track_error_bt = cv2.calcOpticalFlowPyrLK(
img2,
img1,
target_pt,
fb_pt,
winSize=(win_size_lk, win_size_lk),
flags=cv.OPTFLOW_USE_INITIAL_FLOW,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
status = status & status_bt
ncc = normCrossCorrelation(img1, img2, template_pt, target_pt, status,
winsize_ncc, method)
fb = euclideanDistance(template_pt, target_pt)
newfb = -1 * np.ones(len(fb))
newncc = -1 * np.ones(len(ncc))
for i in np.argwhere(status):
i = i[0]
ptsJ[2 * i] = target_pt[i][0]
ptsJ[2 * i + 1] = target_pt[i][1]
newfb[i] = fb[i]
newncc[i] = ncc[i]
return newfb, newncc, status, ptsJ
def __init__(self, img, new_pts, detector, descriptor, templateImg, skp, sd, tkp, td):
"""
**SUMMARY**
Initializes all the required parameters and attributes of the class.
**PARAMETERS**
* *img* - SimpleCV.Image
* *new_pts* - List of all the tracking points found in the image. - list of cv2.KeyPoint
* *detector* - SURF detector - cv2.FeatureDetector
* *descriptor* - SURF descriptor - cv2.DescriptorExtractor
* *templateImg* - Template Image (First image) - SimpleCV.Image
* *skp* - image keypoints - list of cv2.KeyPoint
* *sd* - image descriptor - numpy.ndarray
* *tkp* - Template Imaeg keypoints - list of cv2.KeyPoint
* *td* - Template image descriptor - numpy.ndarray
**RETURNS**
SimpleCV.Tracking.TrackClass.SURFTrack object
**EXAMPLE**
>>> track = SURFTracker(image, pts, detector, descriptor, temp, skp, sd, tkp, td)
"""
if td is None:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
return
if len(new_pts) < 1:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
self.pts = None
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.detector = detector
self.descriptor = descriptor
return
if sd is None:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
self.pts = None
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.detector = detector
self.descriptor = descriptor
return
np_pts = np.asarray([kp.pt for kp in new_pts])
t, pts, center = cv2.kmeans(np.asarray(np_pts, dtype=np.float32), K=1, bestLabels=None,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1, 10), attempts=1,
flags=cv2.KMEANS_RANDOM_CENTERS)
max_x = int(max(np_pts[:, 0]))
min_x = int(min(np_pts[:, 0]))
max_y = int(max(np_pts[:, 1]))
min_y = int(min(np_pts[:, 1]))
bb = (min_x-5, min_y-5, max_x-min_x+5, max_y-min_y+5)
self = Track.__init__(self, img, bb)
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.pts = np_pts
self.detector = detector
self.descriptor = descriptor
def lktrack(img1, img2, ptsI, nPtsI, winsize_ncc=10, win_size_lk=4, method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Lucas-Kanede Tracker with pyramids
**PARAMETERS**
img1 - Previous image or image containing the known bounding box (Numpy array)
img2 - Current image
ptsI - Points to track from the first image
Format ptsI[0] - x1, ptsI[1] - y1, ptsI[2] - x2, ..
nPtsI - Number of points to track from the first image
winsize_ncc - size of the search window at each pyramid level in LK tracker (in int)
method - Paramete specifying the comparison method for normalized cross correlation
(see http://opencv.itseez.com/modules/imgproc/doc/object_detection.html?highlight=matchtemplate#cv2.matchTemplate)
**RETURNS**
fb - forward-backward confidence value. (corresponds to euclidean distance between).
ncc - normCrossCorrelation values
status - Indicates positive tracks. 1 = PosTrack 0 = NegTrack
ptsJ - Calculated Points of second image
"""
template_pt = []
target_pt = []
fb_pt = []
ptsJ = [-1]*len(ptsI)
for i in range(nPtsI):
template_pt.append((ptsI[2*i],ptsI[2*i+1]))
target_pt.append((ptsI[2*i],ptsI[2*i+1]))
fb_pt.append((ptsI[2*i],ptsI[2*i+1]))
template_pt = np.asarray(template_pt,dtype="float32")
target_pt = np.asarray(target_pt,dtype="float32")
fb_pt = np.asarray(fb_pt,dtype="float32")
target_pt, status, track_error = cv2.calcOpticalFlowPyrLK(img1, img2, template_pt, target_pt,
winSize=(win_size_lk, win_size_lk), flags = cv2.OPTFLOW_USE_INITIAL_FLOW,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
fb_pt, status_bt, track_error_bt = cv2.calcOpticalFlowPyrLK(img2,img1, target_pt,fb_pt,
winSize = (win_size_lk,win_size_lk),flags = cv2.OPTFLOW_USE_INITIAL_FLOW,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
status = status & status_bt
ncc = normCrossCorrelation(img1, img2, template_pt, target_pt, status, winsize_ncc, method)
fb = euclideanDistance(template_pt, target_pt)
newfb = -1*np.ones(len(fb))
newncc = -1*np.ones(len(ncc))
for i in np.argwhere(status):
i = i[0]
ptsJ[2 * i] = target_pt[i][0]
ptsJ[2 * i + 1] = target_pt[i][1]
newfb[i] = fb[i]
newncc[i] = ncc[i]
return newfb, newncc, status, ptsJ
def surfTracker(img, bb, ts, **kwargs):
"""
**DESCRIPTION**
(Dev Zone)
Tracking the object surrounded by the bounding box in the given
image using SURF keypoints.
Warning: Use this if you know what you are doing. Better have a
look at Image.track()
**PARAMETERS**
* *img* - Image - Image to be tracked.
* *bb* - tuple - Bounding Box tuple (x, y, w, h)
* *ts* - TrackSet - SimpleCV.Features.TrackSet.
Optional PARAMETERS:
eps_val - eps for DBSCAN
The maximum distance between two samples for them
to be considered as in the same neighborhood.
min_samples - min number of samples in DBSCAN
The number of samples in a neighborhood for a point
to be considered as a core point.
distance - thresholding KNN distance of each feature
if KNN distance > distance, point is discarded.
**RETURNS**
SimpleCV.Features.Tracking.SURFTracker
**HOW TO USE**
>>> cam = Camera()
>>> ts = []
>>> img = cam.getImage()
>>> bb = (100, 100, 300, 300) # get BB from somewhere
>>> ts = surfTracker(img, bb, ts, eps_val=0.7, distance=150)
>>> while (some_condition_here):
... img = cam.getImage()
... bb = ts[-1].bb
... ts = surfTracker(img, bb, ts, eps_val=0.7, distance=150)
... ts[-1].drawBB()
... img.show()
This is too much confusing. Better use
Image.track() method.
READ MORE:
SURF based Tracker:
Matches keypoints from the template image and the current frame.
flann based matcher is used to match the keypoints.
Density based clustering is used classify points as in-region (of bounding box)
and out-region points. Using in-region points, new bounding box is predicted using
k-means.
"""
eps_val = 0.69
min_samples = 5
distance = 100
for key in kwargs:
if key == 'eps_val':
eps_val = kwargs[key]
elif key == 'min_samples':
min_samples = kwargs[key]
elif key == 'dist':
distance = kwargs[key]
from scipy.spatial import distance as Dis
from sklearn.cluster import DBSCAN
if len(ts) == 0:
# Get template keypoints
bb = (int(bb[0]), int(bb[1]), int(bb[2]), int(bb[3]))
templateImg = img
detector = cv2.FeatureDetector_create("SURF")
descriptor = cv2.DescriptorExtractor_create("SURF")
templateImg_cv2 = templateImg.getNumpyCv2()[bb[1]:bb[1] + bb[3],
bb[0]:bb[0] + bb[2]]
tkp = detector.detect(templateImg_cv2)
tkp, td = descriptor.compute(templateImg_cv2, tkp)
else:
templateImg = ts[-1].templateImg
tkp = ts[-1].tkp
td = ts[-1].td
detector = ts[-1].detector
descriptor = ts[-1].descriptor
newimg = img.getNumpyCv2()
# Get image keypoints
skp = detector.detect(newimg)
skp, sd = descriptor.compute(newimg, skp)
if td is None:
print("Descriptors are Empty")
return None
if sd is None:
track = SURFTracker(img, skp, detector, descriptor, templateImg, skp,
sd, tkp, td)
return track
# flann based matcher
flann_params = dict(algorithm=1, trees=4)
flann = cv2.flann_Index(sd, flann_params)
idx, dist = flann.knnSearch(td, 1, params={})
del flann
# filter points using distnace criteria
dist = (dist[:, 0] / 2500.0).reshape(-1, ).tolist()
idx = idx.reshape(-1).tolist()
indices = sorted(list(range(len(dist))), key=lambda i: dist[i])
dist = [dist[i] for i in indices]
idx = [idx[i] for i in indices]
skp_final = []
skp_final_labelled = []
data_cluster = []
for i, dis in zip(idx, dist):
if dis < distance:
skp_final.append(skp[i])
data_cluster.append((skp[i].pt[0], skp[i].pt[1]))
#Use Denstiy based clustering to further fitler out keypoints
n_data = np.asarray(data_cluster)
D = Dis.squareform(Dis.pdist(n_data))
S = 1 - (D / np.max(D))
db = DBSCAN(eps=eps_val, min_samples=min_samples).fit(S)
core_samples = db.core_sample_indices_
labels = db.labels_
for label, i in zip(labels, list(range(len(labels)))):
if label == 0:
skp_final_labelled.append(skp_final[i])
track = SURFTrack(img, skp_final_labelled, detector, descriptor,
templateImg, skp, sd, tkp, td)
return track
def surfTracker(img, bb, ts, **kwargs):
"""
**DESCRIPTION**
(Dev Zone)
Tracking the object surrounded by the bounding box in the given
image using SURF keypoints.
Warning: Use this if you know what you are doing. Better have a
look at Image.track()
**PARAMETERS**
* *img* - Image - Image to be tracked.
* *bb* - tuple - Bounding Box tuple (x, y, w, h)
* *ts* - TrackSet - SimpleCV.Features.TrackSet.
Optional PARAMETERS:
eps_val - eps for DBSCAN
The maximum distance between two samples for them
to be considered as in the same neighborhood.
min_samples - min number of samples in DBSCAN
The number of samples in a neighborhood for a point
to be considered as a core point.
distance - thresholding KNN distance of each feature
if KNN distance > distance, point is discarded.
**RETURNS**
SimpleCV.Features.Tracking.SURFTracker
**HOW TO USE**
>>> cam = Camera()
>>> ts = []
>>> img = cam.getImage()
>>> bb = (100, 100, 300, 300) # get BB from somewhere
>>> ts = surfTracker(img, bb, ts, eps_val=0.7, distance=150)
>>> while (some_condition_here):
... img = cam.getImage()
... bb = ts[-1].bb
... ts = surfTracker(img, bb, ts, eps_val=0.7, distance=150)
... ts[-1].drawBB()
... img.show()
This is too much confusing. Better use
Image.track() method.
READ MORE:
SURF based Tracker:
Matches keypoints from the template image and the current frame.
flann based matcher is used to match the keypoints.
Density based clustering is used classify points as in-region (of bounding box)
and out-region points. Using in-region points, new bounding box is predicted using
k-means.
"""
eps_val = 0.69
min_samples = 5
distance = 100
for key in kwargs:
if key == 'eps_val':
eps_val = kwargs[key]
elif key == 'min_samples':
min_samples = kwargs[key]
elif key == 'dist':
distance = kwargs[key]
from scipy.spatial import distance as Dis
from sklearn.cluster import DBSCAN
if len(ts) == 0:
# Get template keypoints
bb = (int(bb[0]), int(bb[1]), int(bb[2]), int(bb[3]))
templateImg = img
detector = cv2.FeatureDetector_create("SURF")
descriptor = cv2.DescriptorExtractor_create("SURF")
templateImg_cv2 = templateImg.getNumpyCv2()[bb[1]:bb[1]+bb[3], bb[0]:bb[0]+bb[2]]
tkp = detector.detect(templateImg_cv2)
tkp, td = descriptor.compute(templateImg_cv2, tkp)
else:
templateImg = ts[-1].templateImg
tkp = ts[-1].tkp
td = ts[-1].td
detector = ts[-1].detector
descriptor = ts[-1].descriptor
newimg = img.getNumpyCv2()
# Get image keypoints
skp = detector.detect(newimg)
skp, sd = descriptor.compute(newimg, skp)
if td is None:
print "Descriptors are Empty"
return None
if sd is None:
track = SURFTracker(img, skp, detector, descriptor, templateImg, skp, sd, tkp, td)
return track
# flann based matcher
flann_params = dict(algorithm=1, trees=4)
flann = cv2.flann_Index(sd, flann_params)
idx, dist = flann.knnSearch(td, 1, params={})
del flann
# filter points using distnace criteria
dist = (dist[:,0]/2500.0).reshape(-1,).tolist()
idx = idx.reshape(-1).tolist()
indices = sorted(range(len(dist)), key=lambda i: dist[i])
dist = [dist[i] for i in indices]
idx = [idx[i] for i in indices]
skp_final = []
skp_final_labelled=[]
data_cluster=[]
for i, dis in itertools.izip(idx, dist):
if dis < distance:
skp_final.append(skp[i])
data_cluster.append((skp[i].pt[0], skp[i].pt[1]))
#Use Denstiy based clustering to further fitler out keypoints
n_data = np.asarray(data_cluster)
D = Dis.squareform(Dis.pdist(n_data))
S = 1 - (D/np.max(D))
db = DBSCAN(eps=eps_val, min_samples=min_samples).fit(S)
core_samples = db.core_sample_indices_
labels = db.labels_
for label, i in zip(labels, range(len(labels))):
if label==0:
skp_final_labelled.append(skp_final[i])
track = SURFTrack(img, skp_final_labelled, detector, descriptor, templateImg, skp, sd, tkp, td)
return track
def __init__(self, img, new_pts, detector, descriptor, templateImg, skp,
sd, tkp, td):
"""
**SUMMARY**
Initializes all the required parameters and attributes of the class.
**PARAMETERS**
* *img* - SimpleCV.Image
* *new_pts* - List of all the tracking points found in the image. - list of cv2.KeyPoint
* *detector* - SURF detector - cv2.FeatureDetector
* *descriptor* - SURF descriptor - cv2.DescriptorExtractor
* *templateImg* - Template Image (First image) - SimpleCV.Image
* *skp* - image keypoints - list of cv2.KeyPoint
* *sd* - image descriptor - numpy.ndarray
* *tkp* - Template Imaeg keypoints - list of cv2.KeyPoint
* *td* - Template image descriptor - numpy.ndarray
**RETURNS**
SimpleCV.Tracking.TrackClass.SURFTrack object
**EXAMPLE**
>>> track = SURFTracker(image, pts, detector, descriptor, temp, skp, sd, tkp, td)
"""
if td is None:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
return
if len(new_pts) < 1:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
self.pts = None
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.detector = detector
self.descriptor = descriptor
return
if sd is None:
bb = (1, 1, 1, 1)
self = Track.__init__(self, img, bb)
self.pts = None
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.detector = detector
self.descriptor = descriptor
return
np_pts = np.asarray([kp.pt for kp in new_pts])
t, pts, center = cv2.kmeans(
np.asarray(np_pts, dtype=np.float32),
K=1,
bestLabels=None,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1,
10),
attempts=1,
flags=cv2.KMEANS_RANDOM_CENTERS)
max_x = int(max(np_pts[:, 0]))
min_x = int(min(np_pts[:, 0]))
max_y = int(max(np_pts[:, 1]))
min_y = int(min(np_pts[:, 1]))
bb = (min_x - 5, min_y - 5, max_x - min_x + 5, max_y - min_y + 5)
self = Track.__init__(self, img, bb)
self.templateImg = templateImg
self.skp = skp
self.sd = sd
self.tkp = tkp
self.td = td
self.pts = np_pts
self.detector = detector
self.descriptor = descriptor
