def _segmentSuperpixels(self):
img = self.new_clusters
limit = np.max(img)
superpixels = Superpixels()
for label in range(limit+1):
clusterimg = Image(255*(img == label).astype(np.uint8))
blobs = clusterimg.findBlobs()
if blobs is None:
continue
blob = blobs[-1]
blob.image = self.image & clusterimg
superpixels.append(blob)
return superpixels
def getBB(pt0, pt1):
xmax = np.max((pt0[0], pt1[0]))
xmin = np.min((pt0[0], pt1[0]))
ymax = np.max((pt0[1], pt1[1]))
ymin = np.min((pt0[1], pt1[1]))
return xmin, ymin, xmax, ymax
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 getBB(pt0,pt1):
xmax = np.max((pt0[0],pt1[0]))
xmin = np.min((pt0[0],pt1[0]))
ymax = np.max((pt0[1],pt1[1]))
ymin = np.min((pt0[1],pt1[1]))
return xmin,ymin,xmax,ymax
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
