def parent():
if not os.path.exists(pipe_name):
print("No pipe exists, exiting")
return
# generate some fake data
stamped_data = [utils.timestamp(), np.random.randn(3, 3)]
outobj = pickle.dumps(stamped_data)
#Make sure that the pipe is cleared before putting new data out
try:
pipe = os.open(pipe_name, os.O_RDONLY | os.O_NONBLOCK)
line = os.read(pipe, 0)
except OSError as err:
print(err)
if err.errno == 11:
# this error indicates that the pipe has no data in it
pass
else:
raise err
finally:
os.close(pipe)
# Dump latest data to pipe
try:
pipe = os.open(pipe_name, os.O_WRONLY | os.O_NONBLOCK)
os.write(pipe, outobj)
os.close(pipe)
except OSError as err:
if err.errno == 6:
# This error indicates no consumer is connected to pipe
pass
else:
raise err
def find_rect(self, image, depth):
"""Single rectangular buoy detection"""
markers = 0
threshold = 10
# find buoy by changing blue channel threshold
while (np.amax(markers) == 0):
threshold += 5
temp_t, img = cv2.threshold(image[:, :, 0], threshold, 255,
cv2.THRESH_BINARY_INV)
cv2.imshow('img', img)
_, markers = cv2.connectedComponents(img)
# define kernal and remove noise
kernal = np.ones((5, 5), np.uint8)
img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernal)
img = cv2.morphologyEx(img, cv2.MORPH_DILATE, kernal)
# get pixels of buoy and relay information
nonzero = cv2.findNonZero(img)
x = 0
y = 0
w = 0
h = 0
x, y, w, h = cv2.boundingRect(nonzero)
# determine if detected object is ideal
if ((x != 0 and y != 0) or (h > 20 and w > 20)):
# ideal detected object was found
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
x_c = int(x)
y_c = int(y)
h_c = int(h)
w_c = int(w)
bb_ul = (x_c - w_c, y_c - h_c)
bb_lr = (x_c + w_c, y_c + h_c)
self.detections = []
self.frame_num += 1
self.buoy_detection(timestamp(), bb_ul, bb_lr)
else:
self.detections = None
pass
def standard_output(detection_in):
"""Return up to three objects"""
processing_timestamp = utils.timestamp()
tstring = [image_timestamp] + detection_in + [processing_timestamp]
detect_string = ", ".join(tstring)
return detect_string
.......v...-----------....................
..........................................
..........................................
timestamp format: 19_067_11_50_05_561
year_JulianDay_Hour_Min_Sec_MS
"""
import time
import datetime
from zoidberg import utils
object_001 = ['001', '463', '0432', '034', '056']
object_002 = ['002', '023', '0123', '347', '021']
object_003 = ['003', '765', '1002', '745', '102']
# Each image will have a time based ID
image_ID = 'img_' + utils.timestamp()
def standard_output(detection_in):
"""Return up to three objects"""
processing_timestamp = utils.timestamp()
tstring = [image_timestamp] + detection_in + [processing_timestamp]
detect_string = ", ".join(tstring)
return detect_string
for ob in [object_001, object_002, object_003]:
print(standard_output(ob))
time.sleep(1)
def gate_detection(self, gate_legs):
"""Gate detection object creation"""
r_g_ul_x = None
r_g_ul_y = None
r_g_br_x = None
r_g_br_y = None
r_g_w = None
r_g_h = None
l_g_ul_x = None
l_g_ul_y = None
l_g_br_x = None
l_g_br_y = None
l_g_w = None
l_g_h = None
gate_ul_x = None
gate_ul_y = None
gate_br_x = None
gate_br_y = None
gate_w = None
gate_h = None
r = Detection()
l = Detection()
g = Detection()
if (gate_legs[0].x > gate_legs[1].x):
# (x, y, w, h)1 = right gate --> [0]
r_g_ul_x = gate_legs[0].x
r_g_ul_y = gate_legs[0].y
r_g_w = gate_legs[0].w
r_g_h = gate_legs[0].h
r_g_br_x = r_g_ul_x + r_g_w
r_g_br_y = r_g_ul_y + r_g_h
l_g_ul_x = gate_legs[1].x
l_g_ul_y = gate_legs[1].y
l_g_w = gate_legs[1].w
l_g_h = gate_legs[1].h
l_g_br_x = l_g_ul_x + l_g_w
l_g_br_y = l_g_ul_y + l_g_h
else:
# (x, y, w, h)1 = right gate --> [1]
l_g_ul_x = gate_legs[0].x
l_g_ul_y = gate_legs[0].y
l_g_w = gate_legs[0].w
l_g_h = gate_legs[0].h
l_g_br_x = l_g_ul_x + l_g_w
l_g_br_y = l_g_ul_y + l_g_h
r_g_ul_x = gate_legs[1].x
r_g_ul_y = gate_legs[1].y
r_g_w = gate_legs[1].w
r_g_h = gate_legs[1].h
r_g_br_x = r_g_ul_x + r_g_w
r_g_br_y = r_g_ul_y + r_g_h
gate_ul_x = l_g_ul_x
gate_ul_y = l_g_ul_y
gate_br_x = r_g_br_x
gate_br_y = r_g_br_y
gate_w = int(abs(gate_ul_x - gate_br_x))
gate_h = int(abs(gate_ul_y - gate_br_y))
"""
cv2.rectangle(origin, (gate_ul_x, gate_ul_y), (gate_br_x, gate_br_y), \
(255, 255, 255), 10)
cv2.imshow('origin', origin)
"""
r.write_gate(self.frame_num, self.r_gate_ID, timestamp(), r_g_ul_x, r_g_ul_y, r_g_br_x, r_g_br_y, \
r_g_w, r_g_h)
l.write_gate(self.frame_num, self.l_gate_ID, timestamp(), l_g_ul_x, l_g_ul_y, l_g_br_x, l_g_br_y, \
l_g_w, l_g_h)
g.write_gate(self.frame_num, self.g_gate_ID, timestamp(), gate_ul_x, gate_ul_y, gate_br_x, gate_br_y, \
gate_w, gate_h)
self.frame_num += 1
self.detections.append(r)
self.detections.append(l)
self.detections.append(g)
def find_buoy(self, img):
"""Find objects by contour"""
# set the minimum and maximum distance ratios for valid buoys
ratioMax = 0.5
ratioMin = 0.2
# Step the frame number
self.frame_num += 1
img_color = img.copy()
scan_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# initial function scan to find all contours
scan_img = cv2.Sobel(scan_img, cv2.CV_8U, 0, 1, ksize=5)
scan_img = cv2.threshold(scan_img, 200, 255, cv2.THRESH_BINARY)[1]
scan_img = cv2.erode(scan_img, None, iterations=1)
scan_img = cv2.dilate(scan_img, None, iterations=1)
# find contours
contours = cv2.findContours(scan_img,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
contours = contours[1]
# initialize buoy array and counter
buoys = []
for contour in contours:
# if the length of the contour is long enough to be a potential buoy
if (len(contour) > 45):
# find coordinates in the frame for buoy validation
max_Pt = [9999999, 9999999] # highest y point, huge values for safety
endPt1 = [9999999999, 0] # the highest y point with lowest x coordinate
endPt2 = [-1, 0] # point with the highest x coordinate
for Point in contour:
# temporary coordinates for comparison
temp_y = Point[0][1]
temp_x = Point[0][0]
# make decision based on location of temporary x and y coords
if (temp_y < max_Pt[1]):
max_Pt = Point[0]
if (temp_x < endPt1[0]):
endPt1 = Point[0]
if (temp_x > endPt2[0]):
endPt2 = Point[0]
# store endPt1's coordinates into finalEndPt for comparison
finalEndPt = endPt1;
if (endPt1[1] > endPt2[1]):
finalEndPt = endPt2;
# calculate width and height of contour
w = abs(max_Pt[0] - finalEndPt[0])
h = abs(max_Pt[1] - finalEndPt[1])
# compute ratio necessary for a contour to be considered a buoy,
# noise can still be considered a buoy
if (w != 0):
ratio = (h / (2 * w))
else:
ratio = 0
if ratio >= ratioMin and ratio <= ratioMax:
buoys.append(contour)
# initialize object array and ID count
self.detections = []
for buoy in buoys:
# find inital (x, y) coordinates and radius of contour
(x,y), radius = cv2.minEnclosingCircle(buoy)
x_coord = int(x)
y_coord = int(y)
center = (x_coord, y_coord)
radius = int(radius)
# *** remove contours that are just noise ***
# find arbitrary distance away from the center
dist = radius / 4
# for storing pixels that could potentially be noise
outliers = 0;
for point in buoy:
point = point[0]
# determine if a pixel is an outlier based on y-coord position from center
if point[1] > center[1] and (point[0] > center[0] - dist and \
point[0] < center[0] + dist):
outliers += 1
# if too many outliers were found, it's noise
if (outliers > 1):
continue
# compute bounding boxes
bb_ul = (x_coord - radius, y_coord - radius)
bb_lr = (x_coord + radius, y_coord + radius)
# relay information to detection creator
self.buoy_detection(timestamp(),
bb_ul,
bb_lr)
