def main():
# setup arguments and parse to get config files
script_desc = 'Record waypoints to JSON file'
args = setup_arg_parser(script_desc)
vid_conf = args.camera
maze_conf = args.maze
vid_settings = read_yaml(vid_conf)
maze_settings = read_yaml(maze_conf)
window_name = vid_settings['window_name']
camera = webcam(vid_settings)
d = detector(vid_settings, maze_settings)
r = recorder(vid_settings)
# setup mouse events
cv.setMouseCallback(window_name, r.mouse_event)
# monitor webcam, and record waypoints to file
while True:
# get camera frame and crop down to usable area
ret, frame = camera.read_frame()
if not ret:
print('End of video stream, exiting...')
break
frame, pts = d.crop_and_transform(frame)
# frame, pts = d.crop_no_transform(frame)
# draw circles
r.display_points(frame)
# display frame
cv.imshow(window_name, frame)
if cv.waitKey(1) == ord('q'):
break
camera.print_camera_settings()
camera.vid.release()
cv.destroyAllWindows()
# record waypoints to file
prefix = 'paths'
with open(f'{prefix}/path.json', 'w') as file:
json.dump(r.points, file)
def main():
# setup arguments and parse to get config files
script_desc = 'Display feature detection to screen'
args = setup_arg_parser(script_desc)
vid_conf = args.camera
maze_conf = args.maze
vid_settings = read_yaml(vid_conf)
maze_settings = read_yaml(maze_conf)
window_name = vid_settings['window_name']
camera = webcam(vid_settings)
d = detector(vid_settings, maze_settings)
file = args.path
# p = path(file, cycle=True)
p = path(file, cycle=False)
l = logger()
c = position_controller(vid_settings, maze_settings)
time_begin = timer()
# Main loop - object detection and labeling for each video frame
while True:
frame_time = timer() # time from when frame was taken
### Step 1: Get video frame
ret, frame = camera.read_frame()
if not ret:
print("Error: video frame not loaded.")
break
d.frame_count += 1
start = timer() # time at which frame was ready
### Step 2: crop and transform to get final maze image
frame, pts = d.crop_and_transform(frame)
# frame, pts = d.crop_no_transform(frame)
### Step 3: detect objects
d.detect_objects(frame)
end = timer() # time after all calculation were completed
### Step 4: Draw detected objects and message text to video frame
d.annotate_ball(frame)
# draw table tilt magnitude where ball is located
c.process_update(d.ball_pos)
if d.ball_pos is not None:
# print(c.output)
draw_magnitude(frame, d.ball_pos, c.output,
vid_settings['magnitude_scalar'],
color_map['brightorange'])
if pts is not None:
draw_corners(frame, pts)
display_performance(frame, d.text_tr, d.text_spacing, start, end,
frame_time, vid_settings['text_size'])
# update and show path
if d.ball_pos is not None:
p.process_update(d.ball_pos)
c.set_target(p.pts[p.idx])
p.draw_waypoints(frame, d.ball_pos)
### Step 5: Display video on screen
cv.imshow(window_name, frame)
### log for later graphing
if d.ball_pos and c.target is not None:
l.log_new_data(d.ball_pos, c.target, np.round(end - time_begin, 2))
### Step 6: Check for exit command
wait = cv.waitKey(1)
if wait == ord('q'):
break
elif wait == ord('b'):
p.prev_pt()
elif wait == ord('n'):
p.next_pt()
# clean up
camera.vid.release()
cv.destroyAllWindows()
# write logs
l.write_log()
# Print statistics to terminal
print(f'frames captured: {d.frame_count}')
print(f"Number of frames where ball was missed: {d.missed_frames_ball}")
print(
f"Ball detection rate: {np.around((1 - d.missed_frames_ball / d.frame_count), decimals=4) * 100}%"
)
def main():
script_desc = 'Interact with ball velocity control realtime via controller joystick'
args = setup_arg_parser(script_desc)
vid_conf = args.camera
maze_conf = args.maze
vid_settings = read_yaml(vid_conf)
maze_settings = read_yaml(maze_conf)
window_name = vid_settings['window_name']
camera = webcam(vid_settings)
d = detector(vid_settings, maze_settings)
c = velocity_controller(vid_settings, maze_settings)
ps4 = ps4_controller()
# Main loop - object detection and labeling for each video frame
while True:
frame_time = timer() # time from when frame was taken
### Step 1: Get video frame
ret, frame = camera.read_frame()
if not ret:
print("Error: video frame not loaded.")
break
d.frame_count += 1
c.set_target_velocity(ps4.read_joystick())
start = timer() # time at which frame was ready
### Step 2: crop and transform to get final maze image
frame, pts = d.crop_and_transform(frame)
# frame, pts = d.crop_no_transform(frame)
### Step 3: detect objects
if frame is not None:
d.detect_objects(frame)
#update PID control
c.process_update(d.ball_pos, d.kf)
end = timer() # time after all calculation were completed
### Step 4: Draw detected objects and message text to video frame
d.annotate_ball(frame)
# draw table tilt magnitude where ball is located
if d.ball_pos is not None:
draw_magnitude(frame, d.ball_pos, c.output,
vid_settings['magnitude_scalar'],
color_map['brightorange'])
if d.ball_pos is not None:
draw_magnitude(frame, d.ball_pos, ps4.axis_data,
vid_settings['magnitude_scalar'],
color_map['green'])
# draw error line
# if d.ball_pos and c.target is not None:
# draw_line(frame, (c.target[0], c.target[1]), (d.ball_pos[0], d.ball_pos[1]), BGR_color=color_map['red'])
if frame is not None:
display_performance(frame, d.text_tr, d.text_spacing, start, end,
frame_time, vid_settings['text_size'])
# display mouse event to screen
# if c.target is not None:
# draw_circles(frame, [c.target], num=1, BGR_color=color_map['green'])
### Step 5: Display video on screen
if frame is not None:
cv.imshow(window_name, frame)
### Step 6: Check for key command
if cv.waitKey(1) == ord('q'):
break
# clean up
camera.vid.release()
cv.destroyAllWindows()
# Print statistics to terminal
print(f'frames captured: {d.frame_count}')
print(f"Number of frames where ball was missed: {d.missed_frames_ball}")
print(
f"Ball detection rate: {np.around((1 - d.missed_frames_ball / d.frame_count), decimals=4) * 100}%"
)