def main():
ap = ArgumentParser()
ap.add_argument('P')
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
pic = Picture(sys.argv[4])
prevBeads = BlobFinder(pic, tau).getBeads(P)
# for every frame:
for i in sys.argv[5:]:
currBeads = BlobFinder(Picture(i), tau).getBeads(P)
# for every bead in that frame:
for currBead in range(len(currBeads)):
# intialize shortest_dist with largest possible dimension:
shortest_dist = max([pic.width(), pic.height()])
# search for currBead closest to prevBead:
for v in range(min([len(currBeads), len(prevBeads)])):
d = prevBeads[v].distanceTo(currBeads[currBead])
if d < shortest_dist:
shortest_dist = d
# confirm that displacement is within delta:
if shortest_dist <= delta:
# if yes, then show the distance:
stdio.writef('%.4f\n', shortest_dist)
stdio.writeln()
prevBeads = currBeads
def main():
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
for i in range(4,len(sys.argv) - 1):
pic1 = Picture(sys.argv[i])
pic2 = Picture(sys.argv[i+1])
blobscan = BlobFinder(pic1,tau)
blobscan2 = BlobFinder(pic2,tau)
blob = blobscan.getBeads(P)
blob2 = blobscan2.getBeads(P)
for i in range(len(blob2)):
least = 0.0000
first = True
for j in range(len(blob)):
b = blob2[i]
bb = blob[j]
distance = b.distanceTo(bb)
if(distance <= delta and first):
least = distance
first = False
elif(distance <= delta and distance < least):
least = distance
if(least>0):
stdio.writef('%s\n', round(least,4))
stdio.write('\n')
def main():
# intialize P, tau, and delta.
P, tau = int(sys.argv[1]), float(sys.argv[2])
delta = float(sys.argv[3])
firstPic = BlobFinder(Picture(sys.argv[4]), tau)
prevBeads = firstPic.getBeads(P)
for v in sys.argv[5:]:
# construct blobfinder, currBeads, and a list of beads
blobfinder = BlobFinder(Picture(v), tau)
currBeads = blobfinder.getBeads(P)
for currBead in currBeads:
# map distances
a = map(lambda x: currBead.distanceTo(x), prevBeads)
# filter the list
minDist = list(filter(lambda x: x <= delta, a))
# if the list isn't empty, write the min distance
if minDist:
minDist = min(minDist)
stdio.writef('%.4f\n', minDist)
# instialize prevBeads wiht currBeads
prevBeads = currBeads
stdio.writeln()
def main():
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
files = sys.argv[4:]
beads1 = BlobFinder(Picture(files[0]), tau).getBeads(P)
for i in range(1, len(files)):
beads0 = beads1
beads1 = BlobFinder(Picture(files[i]), tau).getBeads(P)
for bead in beads0:
dist = min(bead.distanceTo(other) for other in beads1)
if dist <= delta:
stdio.writef('%.4f\n', dist)
stdio.writeln()
def __init__(self, param):
self.param = param
self.circle_size = 5
self.circle_thickness = 1
self.blob_finder = BlobFinder(
threshold=self.param['tracking']['multiobj_threshold'],
minArea=self.param['tracking']['min_area'],
maxArea=self.param['tracking']['max_area'],
)
def __init__(self):
super(PuzzleBoxes, self).__init__()
self.blob_finder = BlobFinder(
threshold=self.param['tracking']['threshold'],
minArea=self.param['tracking']['min_area'],
maxArea=self.param['tracking']['max_area'],
)
self.data_pub = rospy.Publisher('/puzzleboxes_data',
PuzzleboxesData,
queue_size=10)
def main():
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
seq = sys.argv[4:]
test = []
for i in range(1, len(seq)):
pic = Picture(seq[i - 1])
bf = BlobFinder(pic, tau)
beads = bf.getBeads(P)
pic = Picture(seq[i])
bf = BlobFinder(pic, tau)
beads2 = bf.getBeads(P)
for q in beads2:
for a in beads:
test += [q.distanceTo(a)]
if min(test) <= delta:
stdio.writef('%.4f\n', min(test))
test = []
def main():
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
bf = BlobFinder(Picture(sys.argv[4]), tau)
prevBeads = bf.getBeads(P)
for i in range(5, len(sys.argv)):
bf = BlobFinder(Picture(sys.argv[i]), tau)
currBeads = bf.getBeads(P)
for currBead in currBeads:
min_dist = float('inf')
for prevBead in prevBeads:
d = currBead.distanceTo(prevBead)
if d <= delta and d < min_dist:
min_dist = d
if min_dist != float('inf'):
stdio.writef('%.4f\n', min_dist)
stdio.writeln()
prevBeads = currBeads
def main():
P = int(sys.argv[1])
tau = float(sys.argv[2])
delta = float(sys.argv[3])
frame = BlobFinder(Picture(sys.argv[4]), tau)
prevBeads = frame.getBeads(P)
for i in range(5, len(sys.argv)):
frame = BlobFinder(Picture(sys.argv[i]), tau)
currBeads = frame.getBeads(P)
for currBead in currBeads:
distance = float('inf')
for prevBead in prevBeads:
d = currBead.distanceTo(prevBead)
if d <= delta and d < distance:
distance = d
if distance != float('inf'):
stdio.writef('%.4f\n', distance)
stdio.writeln()
prevBeads = currBeads
def run(self):
blob_finder = BlobFinder(
threshold=self.tracking_threshold,
minArea=self.tracking_min_area,
maxArea=self.tracking_max_area,
)
while not rospy.is_shutdown():
try:
new_image = self.image_queue.get_nowait()
except Queue.Empty:
continue
current_time = rospy.Time.now().to_time()
elapsed_time = current_time - self.start_time
diff_image = cv2.absdiff(new_image, self.bg_image)
blob_list, blob_image = blob_finder.find(diff_image)
# DEVEL Temporary
# --------------------------------
on_food = False
# --------------------------------
if (elapsed_time >= self.pretrial_duration) and (
elapsed_time <
self.pretrial_duration + self.experiment_duration):
for scheduler in self.led_schedulers:
scheduler.update(rospy.Time.now().to_time(), on_food)
if elapsed_time > (self.pretrial_duration +
self.experiment_duration +
self.posttrial_duration):
if not self.logger_killed:
os.system('rosnode kill experiment_logger')
self.logger_killed = True
new_image_bgr = cv2.cvtColor(new_image, cv2.COLOR_GRAY2BGR)
for region_dict in self.region_param:
cx, cy = region_dict['food_pos']
x0 = int(cx - self.food_width / 2.0)
y0 = int(cy - self.food_height / 2.0)
x1 = int(cx + self.food_width / 2.0)
y1 = int(cy + self.food_height / 2.0)
cv2.rectangle(new_image_bgr, (x0, y0), (x1, y1), (0, 0, 255),
1)
cv2.imshow('blob image', blob_image)
cv2.imshow('walking arena 1d', new_image_bgr)
cv2.waitKey(1)
def __init__(self):
self.use_compressed = False
rospy.init_node('disco_tracker')
self.image_queue = Queue.Queue()
self.bridge = CvBridge()
self.roi = {
'x': (135, 485),
'y': (60, 405),
}
self.blob_finder = BlobFinder(threshold=20, min_area=2, max_area=200)
if self.use_compressed:
self.image_topic = '/raspicam_node/image/compressed'
self.image_sub = rospy.Subscriber(self.image_topic,
CompressedImage,
self.on_image,
queue_size=1)
else:
self.image_topic = '/raspicam_node/image'
self.image_sub = rospy.Subscriber(self.image_topic,
Image,
self.on_image,
queue_size=1)
def getAviFrameToSigDict(fileName, coord='x'):
# Zeroing region - for removing frame counter number from image
zeroN, zeroM = 100, 100
# Point tol
ptTol = 4
# Blob finder parameters
threshold = 50
filterByArea = True
minArea = 20
maxArea = None
blobFinder = BlobFinder(threshold=threshold,
filterByArea=filterByArea,
minArea=minArea,
maxArea=maxArea)
# Read frames from video and get list of blob centroid
cap = cv2.VideoCapture(fileName)
frmNum = 0
numBlobTest = True
frmToPtList = {}
while (cap.isOpened()):
ret, frame = cap.read()
if not ret:
break
frmNum += 1
print('processing frame: {0}'.format(frmNum))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray[:zeroN, :zeroM] = numpy.zeros((zeroN, zeroM))
blobList, blobImage = blobFinder.find(gray)
ptList = []
for blob in blobList:
if coord == 'x':
val = blob['centroidX']
else:
val = blob['centroidY']
ptList.append(val)
frmToPtList[frmNum] = ptList
cv2.imshow('frame', blobImage)
if cv2.waitKey(2) & 0xff == ord('q'):
break
# -----------------------------------------------
# Temp - for development, stop and examine frame
# -----------------------------------------------
#if frmNum == 2086:
# ans = raw_input('paused')
cap.release()
cv2.destroyAllWindows()
# Get unique set of point values - based
ptSet = set()
for frmNum, ptList in frmToPtList.iteritems():
for val in ptList:
found = False
for pt in ptSet:
if abs(pt - val) < ptTol:
found = True
if not found:
ptSet.add(val)
print(ptSet)
if len(ptSet) > 3:
raise ValueError, 'more than three unique pts found'
# Create pt to signal number dictionary
ptList = list(ptSet)
ptList.sort()
ptToSigNum = dict([(x, ptList.index(x)) for x in ptList])
# Create frame number to signal dictionary
frmToSig = {}
for frmNum, ptList in frmToPtList.iteritems():
sig = [0, 0, 0]
for x in ptList:
closest = min([(abs(pt - x), sigNum)
for pt, sigNum in ptToSigNum.iteritems()])
ind = closest[1]
sig[ind] = 1
frmToSig[frmNum] = sig
return frmToSig
def run(self):
cap = cv2.VideoCapture(self.input_video_name)
bg_model = MedianBackground(
window_size=self.param['bg_window_size'],
threshold=self.param['fg_threshold']
)
blob_finder = BlobFinder(
filter_by_area=True,
min_area=self.param['min_area'],
max_area=self.param['max_area'],
open_kernel_size = self.param['open_kernel_size'],
close_kernel_size = self.param['close_kernel_size'],
kernel_shape = self.param['kernel_shape'],
#---------KJL 2017_12_15
min_interblob_spacing = self.param['min_interblob_spacing'])
# Output files
vid = None
blob_fid = None
if self.param['blob_file_name'] is not None:
blob_fid = open(self.param['blob_file_name'], 'w')
frame_count = -1
while True:
print('frame count: {0}'.format(frame_count))
# Get frame, mask and convert to gray scale
ret, frame = cap.read()
if not ret:
break
frame_count += 1
frame = self.apply_datetime_mask(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if frame_count == 0 and self.param['output_video_name'] is not None:
vid = cv2.VideoWriter(
self.param['output_video_name'],
0x00000021, # hack for cv2.VideoWriter_fourcc(*'MP4V')
self.param['output_video_fps'],
(frame.shape[1], frame.shape[0]),
)
# Update background model
bg_model.update(frame)
if not bg_model.ready:
continue
# Find blobs and add data to blob file
blob_list, blob_image, circ_image = blob_finder.find(frame,bg_model.foreground_mask)
if vid is not None:
vid.write(circ_image)
if blob_fid is not None:
frame_data = {'frame': frame_count, 'blobs' : blob_list}
frame_data_json = json.dumps(frame_data)
blob_fid.write('{0}\n'.format(frame_data_json))
# Display preview images
if self.param['show_dev_images']:
cv2.imshow('original',frame)
cv2.imshow('background', bg_model.background)
cv2.imshow('foreground mask', bg_model.foreground_mask)
cv2.imshow('blob_image', blob_image)
cv2.imshow('circ_image', circ_image)
else:
cv2.imshow('circ_image', circ_image)
wait_key_val = cv2.waitKey(1) & 0xFF
if wait_key_val == ord('q'):
break
# Clean up
cap.release()
cv2.destroyAllWindows()
if vid is not None:
vid.release()
if blob_fid is not None:
blob_fid.close()