def detect_objects(self, s: Settings) -> None:
"""
Uses the in Settings specified method to detect the object
:param s: Settings object used to store thresholds and camera used data
:type s: Settings
:return: None
"""
m = Markers(s)
self.frame = self._get_video(s, self._cap)
marker_frame, warped_frame = m.remove_warp(s, self.frame)
self.grayscale = self._convert_to_grayscale(warped_frame)
width, height = self.frame.shape[0:2]
if s.edge_detection_type == 1: # Only threshold detection
self.thrash = self._edge_threshold(s, self.grayscale)
self.video_output = self._draw_contour(
s, self.frame, self._edge_contour(self.thrash))
elif s.edge_detection_type == 2: # Threshold + Canny shape detection
gausian_blur = cv.GaussianBlur(self.grayscale, (5, 5), sigmaX=0)
self.thrash = self._edge_threshold(s, gausian_blur)
self.canny = self._edge_canny(s, self.thrash)
self.dilate = self._dilate_edges(self.canny,
iterations=s.dilate_iterations)
self.erode = self._erode_edges(self.dilate,
iterations=s.erode_iterations)
self.blur = self._blur_edges(s, self.erode)
contours = self._edge_contour(self.blur)
self.video_output = self._draw_contour(s, warped_frame, contours)
cv.imwrite('./output.bmp', self.video_output)
elif s.edge_detection_type == 3: # Blob + Canny shape detection
self.blob = self._edge_blob(self.grayscale)
self.canny = self._edge_canny(s, self.blob)
else: # Simple Canny method
self._edge_canny(s, self.grayscale)
def __init__(self):
# noinspection PyArgumentList
self.cap = cv2.VideoCapture(0)
self.cap.set(15, -7)
self.frames_to_display = {}
self.display = Display()
self.markers = Markers()
self.markers_cache = None
def __init__(self):
# initialise markers
self.markers = Markers()
# initialise textures
self.marker_one_textures, self.marker_two_textures = self._load_textures()
# initialise video
self.video_capture = cv2.VideoCapture()
def align(self, options):
"""Create MSA from marker genes."""
check_dir_exists(options.identify_dir)
make_sure_path_exists(options.out_dir)
if not hasattr(options, 'outgroup_taxon'):
options.outgroup_taxon = None
markers = Markers(options.cpus)
markers.align(options.identify_dir, options.taxa_filter,
options.min_perc_aa, options.custom_msa_filters,
options.consensus, options.min_perc_taxa,
options.out_dir, options.prefix, options.outgroup_taxon)
self.logger.info('Done.')
def identify(self, options):
"""Identify marker genes in genomes."""
if options.genome_dir:
check_dir_exists(options.genome_dir)
if options.batchfile:
check_file_exists(options.batchfile)
make_sure_path_exists(options.out_dir)
genomes = self._genomes_to_process(options.genome_dir,
options.batchfile,
options.extension)
markers = Markers(options.cpus)
markers.identify(genomes, options.out_dir, options.prefix)
self.logger.info('Done.')
def loop():
frame = capture.get_frame()
if frame is None:
return
(cap_height, cap_width, cap_channels) = frame.shape
# print(width, height, channels)
global markerDict
(corners, ids, rejects) = aruco.detectMarkers(frame, markerDict)
# print(corners)
frame = aruco.drawDetectedMarkers(frame, corners, ids)
detected_markers = np.array([]).reshape(0, 3)
for i in range(len(corners)):
corner = corners[i][0]
p1 = corner[0]
p2 = corner[1]
p3 = corner[2]
p4 = corner[3]
x = p1[0] - ((p1[0] - p3[0]) / 2)
y = p1[1] - ((p1[1] - p3[1]) / 2)
x = int(x)
y = int(y)
# print(x, y, cap_width, cap_height)
detected_markers = np.vstack(
(detected_markers,
np.array([ids[i][0], x / cap_width, y / cap_height])))
cv2.circle(frame, (x, y), 10, (255, 255, 255), 2)
global sock
if not sock.is_available():
sock.connect()
else:
send_data = Markers()
for row in detected_markers:
marker = Marker(int(row[0]), row[1], row[2])
send_data.append(marker)
sock.send(str(send_data))
def __init__(self):
# initialise config
self.config_provider = ConfigProvider()
# initialise robots
self.rocky_robot = RockyRobot()
self.sporty_robot = SportyRobot()
# initialise webcam
self.webcam = Webcam()
# initialise markers
self.markers = Markers()
self.markers_cache = None
# initialise features
self.features = Features(self.config_provider)
# initialise texture
self.texture_background = None
