def main(data_path=DATA_PATH):
global streamer, grapher, profile_model, drawing_model, current_graph, DISPLAY_TOGGLE
# out = cv2.VideoWriter(r'C:\Users\User\Documents\Hylife 2020\Loin Feeder\output.mp4', 0x7634706d, 30, (1680,830))
# out = cv2.VideoWriter(r'C:\Users\User\Documents\Hylife 2020\Loin Feeder\output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (1680,830))
if DISPLAY_TOGGLE:
win = "Window"
cv2.namedWindow(win)
cv2.setMouseCallback(win, on_mouse)
delay = 0
flip_flop = False
flip_flop2 = False
meats = [0]
queue1 = []
queue2 = []
times = []
saved_state = []
while (streamer.running):
start = time.time()
################################################
### Video Processing and Meat Identification ###
################################################
force_timer = time.time()
# Keeps streamer queue size within a lag free range (>0 and <128)
qsize = streamer.Q.qsize()
if qsize < 40:
streamer.sleep_time = 0
elif qsize > 88:
streamer.sleep_time = 0.005
# frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)
temp = streamer.read()
frame = bounding_box.scale(temp)
frame = cv2.copyMakeBorder(frame,
0,
300,
300,
300,
cv2.BORDER_CONSTANT,
value=0)
iH, iW, iD = frame.shape
box, _, _ = bounding_box.get_bbox(frame)
# for i in range(0, len(box)):
# cv2.drawContours(frame, [box[i][0]], 0, (255, 255, 255), 3)
# Artificially simulate camera trigger
if (box != 0):
for i in range(0, len(box)):
if delay > 50:
cX = int(box[i][1]["m10"] / box[i][1]["m00"])
cY = int(box[i][1]["m01"] / box[i][1]["m00"])
if iH / 3 - 5 < cY and iH / 3 + 5 > cY:
if flip_flop:
meats += [
meat.Meat(box[i],
side="Right",
center=[cX, cY])
]
else:
meats += [
meat.Meat(box[i], side="Left", center=[cX, cY])
]
flip_flop = not flip_flop
delay = 0
delay += 1
########################################
### Path planning and Robot Movement ###
########################################
ep1 = Point(625, 735, angle=90)
ep2 = Point(250, 735, angle=90)
if len(meats) > 3:
if len(meats) % 2 == 0 and delay == 1:
# Queue [P1 index, P2 index], so that meat can be accounted for even if robot is currently in motion
queue1 += [[len(meats) - 1, len(meats) - 2]]
queue2 += [[len(meats) - 1, len(meats) - 2]]
# # Profiler model creates motion profiles, it updates as fast as possible in a separate thread
if PROFILER_TOGGLE:
if profile_model.phase == 0 and len(
queue1) > 0 and not path_runner.running:
dist = (global_parameters['PICKUP_POINT'] -
meats[queue1[0][0]].get_center_as_point()).y
if dist > 0:
sp1 = meats[queue1[0][0]].get_center_as_point().copy(
) + Point(0, dist)
sp2 = meats[queue1[0][1]].get_center_as_point().copy(
) + Point(0, dist)
profile_model.move_meat(sp1, sp2, ep1, ep2, dist // global_parameters['CONVEYOR_SPEED'], \
meats[queue1[0][0]].width, meats[queue1[0][1]].width, phase_1_delay=False)
queue1 = queue1[1:]
# Given the start and end conditions, calculate the profile_model motor profiles
path_runner.start()
# Drawing model is just for drawing purposes, it updates at the frame rate displayed
if DISPLAY_TOGGLE:
if drawing_model.phase == 0 and len(queue2) > 0:
dist = (global_parameters['PICKUP_POINT'] -
meats[queue2[0][0]].get_center_as_point()).y
if dist > 0:
sp1 = meats[queue2[0][0]].get_center_as_point().copy(
) + Point(0, dist)
sp2 = meats[queue2[0][1]].get_center_as_point().copy(
) + Point(0, dist)
drawing_model.move_meat(sp1, sp2, ep1, ep2, dist // (global_parameters['CONVEYOR_SPEED'] * \
global_parameters['RUNTIME_FACTOR']), meats[queue2[0][0]].width, meats[queue2[0][1]].width)
queue2 = queue2[1:]
flip_flop2 = True
if PROFILER_TOGGLE:
grapher.start(path_runner, (830, 830), 'o')
else:
print(
"ERROR: Conveyor Speed too fast for current settings")
queue2 = queue2[1:]
drawing_model.update()
# Changes display chart
if DISPLAY_TOGGLE and PROFILER_TOGGLE:
if flip_flop2 and not path_runner.running and not grapher.running:
current_graph = grapher.read()
flip_flop2 = False
###############
### Display ###
###############
if DISPLAY_TOGGLE:
if (len(meats) != 1):
for i in range(1, len(meats)):
meats[i].draw(frame, color=(255, 255, 0))
drawing_model.draw(frame)
if PROFILER_TOGGLE:
frame = np.concatenate((frame, current_graph), axis=1)
cv2.imshow(win, frame)
################
### Controls ###
################
for i in range(1, len(meats)):
meats[i].step()
if DISPLAY_TOGGLE:
# k = cv2.waitKey(1) & 0xFF
k = cv2.waitKey(
max(
global_parameters['FRAME_RATE'] -
round((time.time() - force_timer) * 1000 + 1), 1)) & 0xFF
if k == ord('q'):
break
elif k == ord('p'):
cv2.waitKey(0)
elif k == ord('o'):
grapher.start(path_runner, (830, 830), 'o')
while grapher.running:
pass
current_graph = grapher.read()
elif k == ord('i'):
grapher.start(path_runner, (830, 830), 'i')
while grapher.running:
pass
current_graph = grapher.read()
elif k == ord('s'):
saved_state = drawing_model.get_model_state()
cv2.waitKey(0)
print("State saved.\n")
elif k == ord('r'):
drawing_model.set_model_state(saved_state)
print("State uploaded.")
times += [time.time() - start]
# out.write(frame)
#Artifically slow the program to the desired frame rate
print("Average frame time:", np.average(times))
# out.release()
streamer.stop()
if PROFILER_TOGGLE:
path_runner.stop()
if DISPLAY_TOGGLE:
grapher.stop()
cv2.destroyAllWindows()
def main(data_path=DATA_PATH):
cap = cv2.VideoCapture(data_path)
# out = cv2.VideoWriter(r'C:\Users\User\Documents\Hylife 2020\Loin Feeder\output6.mp4', 0x7634706d, 30, (500,1059))
delay = 0
times = []
meats = [0]
flip_flop = False
switch = 1
while (cap.isOpened()):
start = time.time()
_, frame = cap.read()
# frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)
try:
frame = bounding_box.scale(frame)
frame = cv2.copyMakeBorder(frame,
0,
300,
300,
300,
cv2.BORDER_CONSTANT,
value=0)
except:
print("End of video")
break
iH, iW, _ = frame.shape
box, mask, _ = bounding_box.get_bbox(frame)
if (box != 0):
for i in range(0, len(box)):
if delay > 50:
try:
M = cv2.moments(box[i])
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
if iH / 1.8 - 5 < cY and iH / 1.8 + 5 > cY:
if flip_flop:
meats += [
Meat(box[i], side="Right", center=[cX, cY])
]
else:
meats += [
Meat(box[i], side="Left", center=[cX, cY])
]
flip_flop = not flip_flop
print("Meat detected")
print(meats[-1])
data = "Side:" + meats[-1].get_side() + "\n" + str(
delay)
delay = 0
except:
pass
try:
if switch == 1:
res = bounding_box.draw_results(frame, [meats[-1].get_bbox()],
"Test",
meat=meats[-1],
extra_data=data)
elif switch == 2:
filtered = bounding_box.gen_mask(frame,
bitwise_and=True,
process=False)
bounding_box.draw_results(filtered, [meats[-1].get_bbox()],
"Test",
meat=meats[-1],
extra_data=data)
elif switch == 3:
filtered = bbox.gen_mask(frame,
bitwise_and=False,
process=True)
bounding_box.draw_results(filtered, [meats[-1].get_bbox()],
"Test",
meat=meats[-1],
extra_data=data)
# res = bbox.draw_results(mask, box, "Test", meat=meats[-1])
except:
res = frame
cv2.imshow("Test", frame)
for i in range(1, len(meats)):
meats[i].step()
# filtered = bbox.gen_mask(frame, bitwise_and=True, process=True)
# mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# t1 = filtered[:,0:int(iW//3)]
# t2 = filtered[:,int(iW//3):int(iW*2//3+1)]
# t3 = filtered[:,int(iW*2//3+1):iW-1]
# # t2 = mask[1:-1,int(iW//3):int(iW*2//3+1)]
# # t3 = mask[1:-1,int(iW*2//3+1):iW-1]
# temp1 = cv2.hconcat([t1, t2])
# temp = cv2.hconcat([temp1, t3])
# # color = (31, 255, 49)
# # if (box != 0):
# # for i in range(0, len(box)):
# # cv2.drawContours(temp, box, i, color, 2)
# out.write(res)
# cv2.imshow("Split", temp)
k = cv2.waitKey(1) & 0xFF
if k == ord('q'):
break
elif k == ord('p'):
cv2.waitKey(0)
elif k == ord('1'):
switch = 1
elif k == ord('2'):
switch = 2
elif k == ord('3'):
switch = 3
# if delay == 0:
# cv2.waitKey(0)
delay += 1
times += [time.time() - start]
# cv2.waitKey(20)
print("Average frame processing time:", np.average(times))
cap.release()
# out.release()
cv2.destroyAllWindows()
def main(data_path=DATA_PATH):
global streamer
# out = cv2.VideoWriter(r'C:\Users\User\Documents\Hylife 2020\Loin Feeder\output.mp4', 0x7634706d, 30, (1680,830))
if DISPLAY_TOGGLE:
win = "Window"
cv2.namedWindow(win)
delay = 0
flip_flop = False
flip_flop2 = False
meats = [0]
queue1 = []
queue2 = []
times = []
while (streamer.running):
start = time.time()
################################################
### Video Processing and Meat Identification ###
################################################
force_timer = time.time()
# Keeps streamer queue size within a lag free range (>0 and <128)
qsize = streamer.Q.qsize()
if qsize < 40:
streamer.sleep_time = 0
elif qsize > 88:
streamer.sleep_time = 0.005
temp = streamer.read()
frame = bounding_box.scale(temp)
frame = cv2.copyMakeBorder(frame,
0,
300,
300,
300,
cv2.BORDER_CONSTANT,
value=0)
iH, iW, iD = frame.shape
box, _, _ = bounding_box.get_bbox(frame)
# Artificially simulate camera trigger
if (box != 0):
for i in range(0, len(box)):
if delay > 50:
cX = int(box[i][1]["m10"] / box[i][1]["m00"])
cY = int(box[i][1]["m01"] / box[i][1]["m00"])
if iH / 3 - 5 < cY and iH / 3 + 5 > cY:
if flip_flop:
meats += [
meat.Meat(box[i],
side="Right",
center=[cX, cY])
]
else:
meats += [
meat.Meat(box[i], side="Left", center=[cX, cY])
]
flip_flop = not flip_flop
delay = 0
delay += 1
###############
### Display ###
###############
if DISPLAY_TOGGLE:
cv2.imshow(win, frame)
################
### Controls ###
################
for i in range(1, len(meats)):
meats[i].step()
if DISPLAY_TOGGLE:
k = cv2.waitKey(1) & 0xFF
if k == ord('q'):
break
elif k == ord('p'):
cv2.waitKey(0)
times += [time.time() - start]
# out.write(frame)
#Artifically slow the program to the desired frame rate
# cv2.waitKey(max(global_parameters['FRAME_RATE'] - round((time.time() - force_timer )*1000 + 1), 1))
print("Average frame time:", np.average(times))
# out.release()
streamer.stop()
cv2.destroyAllWindows()
def main(input_path=DATA_PATH):
h = [0, 180]
s = [0, 255]
v = [0, 255]
hlow = 0
hhigh = 15
slow = 51
shigh = 204
vlow = 51
vhigh = 255
flag = False
for i in range(START_INDEX, END_INDEX + 1):
temp = input_path + str(i) + FILE_TYPE
og = bounding_box.scale(cv2.imread(temp), width=500)
src = 'Source'
cv2.namedWindow(src)
cv2.createTrackbar('H Low', src, hlow, 180, n)
cv2.createTrackbar('H High', src, hhigh, 180, n)
cv2.createTrackbar('S Low', src, slow, 255, n)
cv2.createTrackbar('S High', src, shigh, 255, n)
cv2.createTrackbar('V Low', src, vlow, 255, n)
cv2.createTrackbar('V High', src, vhigh, 255, n)
while (1):
k = cv2.waitKey(1) & 0xFF
if (k == ord('n')):
break
elif (k == ord('q')):
flag = True
break
hlow = cv2.getTrackbarPos('H Low', src)
hhigh = cv2.getTrackbarPos('H High', src)
slow = cv2.getTrackbarPos('S Low', src)
shigh = cv2.getTrackbarPos('S High', src)
vlow = cv2.getTrackbarPos('V Low', src)
vhigh = cv2.getTrackbarPos('V High', src)
LOWER_MASK = np.array([hlow, slow, vlow])
UPPER_MASK = np.array([hhigh, shigh, vhigh])
box, contours, _ = bounding_box.get_bbox(og,
source=src,
lower_mask=LOWER_MASK,
upper_mask=UPPER_MASK)
temp = og.copy()
if contours != 0:
for i in range(0, len(box)):
cv2.drawContours(temp, [box[i][0]], 0, (255, 255, 255), 3)
for i in range(0, len(contours)):
cv2.drawContours(temp, contours, i,
((i * 17) % 255, (i * 57) % 255,
(i * 3) % 255), 2)
cv2.imshow(src, temp)
cv2.destroyAllWindows()
if (hlow < h[1]):
h[1] = hlow
if (slow < s[1]):
s[1] = slow
if (vlow < v[1]):
v[1] = vlow
if (hhigh > h[0]):
h[0] = hhigh
if (shigh > s[0]):
s[0] = shigh
if (vhigh > v[0]):
v[0] = vhigh
if flag:
break
st = ""
st += "H: (" + str(h[1]) + "," + str(h[0]) + ")\n"
st += "S: (" + str(s[1]) + "," + str(s[0]) + ")\n"
st += "V: (" + str(v[1]) + "," + str(v[0]) + ")"
print(st)