def detect(self, image):
"""Detect ellipse from image.
Args:
image: A numpy as array indicats gray scale image.
Returns:
Array of Ellipse instance that was detected from image.
"""
if len(image.shape) != 2:
raise exceptions.RuntimeException()
seg_detector = SegmentDetector()
segments = seg_detector.detect(image)
ellipse_cand_maker = EllipseCandidateMaker()
ellipse_cands = ellipse_cand_maker.make(segments)
ellipse_estimator = EllipseEstimator()
ellipses = ellipse_estimator.estimate(ellipse_cands)
ellipse_merger = EllipseMerger(image.shape[1], image.shape[0])
ellipses = ellipse_merger.merge(ellipses)
return ellipses
class TestSegmentDetector():
def setup_class(self):
self.subject = SegmentDetector(image_width=300)
def test_left(self):
assert self.subject.segment(20) == 'left'
def test_right(self):
assert self.subject.segment(250) == 'right'
def test_centre(self):
assert self.subject.segment(150) == 'centre'
class ImageProcessor:
def __init__(self):
gopigo.set_speed(50)
gopigo.stop
self._face_detector = FaceDetector('/home/pi/opencv/data/haarcascades/haarcascade_frontalface_default.xml')
self.sizes_calculated = False
self.image_height = None
self.image_width = None
self.segment_detector = None
def faces(self, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
return self._face_detector.detect(gray, scaleFactor = 1.1, minNeighbors = 5, minSize = (30, 30))
def calculate_sizes(self, image):
(self.height, self.width) = image.shape[:2]
self.segment_detector = SegmentDetector(self.width)
self.sizes_calculated = True
def process(self, stream):
start_time = time.time()
image = cv2.imdecode(np.fromstring(stream.getvalue(), dtype=np.uint8), 1)
if(self.sizes_calculated == False):
self.calculate_sizes(image)
faceRects = self.faces(image)
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
if (len(faceRects) > 0):
(x, y, w, h) = faceRects[0]
self.move(x + (w/2))
else:
print("no faces found")
gopigo.stop()
print(time.time() - start_time)
#pdb.set_trace()
cv2.line(image, (self.segment_detector.left_cutoff, 0), (self.segment_detector.left_cutoff, self.height), (255, 0, 0), 1)
cv2.line(image, (self.segment_detector.right_cutoff, 0), (self.segment_detector.right_cutoff, self.height), (255, 0, 0), 1)
return cv2.imencode('.jpg', image)[1].tostring()
def move(self, horiz_x):
segment = self.segment_detector.segment(horiz_x)
print(segment)
if(segment == 'left'):
gopigo.set_speed(10)
gopigo.right_rot()
elif(segment == 'right'):
gopigo.set_speed(10)
gopigo.left_rot()
elif(segment == 'centre'):
gopigo.set_speed(50)
gopigo.fwd()
def setup_class(self): self.subject = SegmentDetector(image_width=300)
def calculate_sizes(self, image): (self.height, self.width) = image.shape[:2] self.segment_detector = SegmentDetector(self.width) self.sizes_calculated = True
