def read_image(self, imgfile, bgfile=None):
""" imgfile and bgfile should be ('/path/to/folder','filename.tif') """
self.imgfile, self.bgfile = imgfile, bgfile
self.img = TIFF(os.path.join(imgfile))
if bgfile:
self.img.data -= TIFF(os.path.join(bgfile)).data
self.minmax = self.img.minmax
self.percentiles = self.img.percentiles([1,5,99,99.995])
self.blobs = find_blobs(self.img.data)
def process_frame():
# Get the raw frames
raw_image = freenect.sync_get_video()[0]
raw_depth = freenect.sync_get_depth()[0]
# Downsample and convert the image frame
cv.Resize(cv.fromarray(raw_image), cv.fromarray(image), cv.CV_INTER_AREA)
cv.CvtColor(cv.fromarray(image), cv.fromarray(image), cv.CV_RGB2HSV)
# Downsample the depth frame using nearest-neighbor to make sure
# invalid pixels are handled properly.
cv.Resize(cv.fromarray(raw_depth), cv.fromarray(depth), cv.CV_INTER_NN)
# Do the object recognition
color.identify(image, constants, colors)
global balls, yellow_walls, green_walls
balls = blobs.find_blobs(colors, depth, color=0)
yellow_walls = blobs.find_blobs(colors, depth, color=1, min_size=100)
green_walls = blobs.find_blobs(colors, depth, color=2, min_size=10)
def __find_landmarks(self, points):
all_blobs = blobs.find_blobs(points)
walls = filters.find_walls(all_blobs)
# We could use spikes here, but that is unreliable in environments with people
# in them.
# Extract landmark points from wall landmarks.
landmark_points = []
for wall in walls:
point = utilities.landmark_point((0, 0), wall[0], wall[1])
landmark_points.append(point)
return landmark_points
def __find_landmarks(self, points):
all_blobs = blobs.find_blobs(points)
walls = filters.find_walls(all_blobs)
# We could use spikes here, but that is unreliable in environments with people
# in them.
# Extract landmark points from wall landmarks.
landmark_points = []
for wall in walls:
point = utilities.landmark_point((0, 0), wall[0], wall[1])
landmark_points.append(point)
return landmark_points
def align_to_wall(self, scan):
# Find walls in the scan.
all_blobs = blobs.find_blobs(scan)
walls = filters.find_walls(all_blobs)
if not walls:
log.error("Could not find any walls.")
return
# Line up with the best wall we have.
best_quality = -sys.maxint
best_wall = None
for wall in walls:
if wall[2] > best_quality:
best_quality = wall[2]
best_wall = wall
# Find the angle of the wall.
angle = math.degrees(math.atan(best_wall[0]))
log.info("Got angle of %f degrees." % (angle))
self.wheels.turn(angle)