processor.upper_thresh[0])
smart_dashboard.putNumber('Vision/Threshhold/Upper/saturation',
processor.upper_thresh[1])
smart_dashboard.putNumber('Vision/Threshhold/Upper/value',
processor.upper_thresh[2])
smart_dashboard.putNumber('Vision/Threshhold/Lower/hue',
processor.lower_thresh[0])
smart_dashboard.putNumber('Vision/Threshhold/Lower/saturation',
processor.lower_thresh[1])
smart_dashboard.putNumber('Vision/Threshhold/Lower/value',
processor.lower_thresh[2])
while True:
timestamp, img = cvsink.grabFrame(img)
if timestamp == 0:
error_msg = 'Error: ' + str(cvsink.getError())
print(error_msg)
if USE_SMART_DASHBOARD:
smart_dashboard.putString('Vision/errors', error_msg)
continue
# The elapsed time is really only for testing to determine the
# optimal image resolution and speed.
start_time = time.time()
try:
result, img = processor.process_image(img)
except:
continue
if USE_MODIFIED_IMAGE:
cvsource.putFrame(img)
end_time = time.time()
ntinst.startClientTeam(7539)
SmartDashBoardValues = ntinst.getTable('SmartDashboard')
#Start camera
print("Connecting to camera")
cs = CameraServer.getInstance()
cs.enableLogging()
Camera = UsbCamera('Cam 0', 0)
Camera.setExposureManual(5)
Camera.setResolution(160, 120)
cs.addCamera(Camera)
print("connected")
CvSink = cs.getVideo()
outputStream = cs.putVideo("Processed Frames", 160, 120)
#buffer to store img data
img = np.zeros(shape=(160, 120, 3), dtype=np.uint8)
# loop forever
while True:
GotFrame, img = CvSink.grabFrame(img)
if GotFrame == 0:
outputStream.notifyError(CvSink.getError())
continue
img = TrackTheTape(img, SmartDashBoardValues)
outputStream.putFrame(img)
name = 'cvsource: ' + vision_camera[2]['name']
width = vision_camera[2]['height']
height = vision_camera[2]['width']
fps = vision_camera[2]['fps']
cvsource = CvSource(name, VideoMode.PixelFormat.kMJPEG, width, height,
fps)
mjpeg_server.setSource(cvsource)
processor = TargetProcessing()
# Allways preallocate, it runs a lot faster
img = np.zeros(shape=(config['height'], config['width'], 3),
dtype=np.uint8)
while True:
timestamp, img = cvsink.grabFrame(img)
if timestamp == 0:
print('Error:', cvsink.getError())
continue
# The elapsed time is really only for testing to determine the
# optimal image resolution and speed.
start_time = time.time()
result, img = processor.process_image(img)
if use_modified_image:
cvsource.putFrame(img)
end_time = time.time()
result = timestamp, *result, round(end_time - start_time, 5)
# Result key:
# timestamp, success, distance, angle1, angle2, elapsedtime
print(result)
# smart_dashboard.putNumberArray('Vision/result', result)
# ntinst.flush()