continue
ratio = (float)(w) / h
if (ratio < min_ratio or ratio > max_ratio):
continue
output.append(contour)
return output
gLine = Example()
camera = UsbCamera("CammyBoi", 0)
camera.setExposureManual(10)
#camera.setConfigJson(json.dumps(json.load(open(configFile, "rt", encoding="utf-8"))))
vidSink = CvSink("Camera")
vidSink.setSource(camera)
vidSource = CvSource("Processed", VideoMode.PixelFormat.kMJPEG, 640, 480, 30)
networkStream = MjpegServer("Stream", 1181)
networkStream.setSource(vidSource)
img = np.zeros(shape=(480, 640, 3), dtype=np.uint8)
while(True):
ret, src = vidSink.grabFrame(img)
startTime = time.time()
gLine.process(src)
image = gLine.mask_output
ntinst.startClientTeam(team)
# start cameras
for config in cameraConfigs:
cameras.append(startCamera(config))
# start switched cameras
for config in switchedCameraConfigs:
startSwitchedCamera(config)
#Init Grip
grip_green = GripPipelineGreen()
grip_yellow = GripPipelineYellow()
sinkY = CvSink("vision Yellow")
sinkF = CvSink("vision Front")
sinkY.setSource(cameras[0]) #Was 1, trying 0
sinkF.setSource(cameras[2]) #Was 1, trying 0
sinkG = CvSink("vision Green")
sinkG.setSource(cameras[1]) #Was 1, trying 0
image_G = numpy.ndarray((320, 240, 3), dtype=numpy.uint8) #Mid val was 360
image_F = numpy.ndarray((320, 240, 3), dtype=numpy.uint8) #Mid val was 360
image_Y = numpy.ndarray((320, 240, 3), dtype=numpy.uint8) #Mid val was 360
camservInst = CameraServer.getInstance()
dashSource1 = camservInst.putVideo("UI Active Cam", 320, 240)
#dashSource2 = camservInst.putVideo("UI Green Cam", 320, 240)
# loop forever
sd = ntinst.getTable('SmartDashboard')
while True:
import convenience
class Client(object):
centerLoc = ntproperty("/SmartDashboard/centerLoc", (0,0))
cl = Client()
if (__name__ == "__main__"):
team = 801
ntinst = NetworkTablesInstance.getDefault()
ntinst.startClientTeam(team)
usbCamera = UsbCamera("USB Camera 0", 0)
mjpegServer1 = MjpegServer("serve_USB Camera 0", 1181)
mjpegServer1.setSource(usbCamera)
cvSink = CvSink("opencv_USB Camera 0")
cvSink.setSource(usbCamera)
outputStream = CvSource("Blur", VideoMode.PixelFormat.kMJPEG, 320, 240, 15)
mjpegServer2 = MjpegServer("serve_Blur Camera 1", 1182)
mjpegServer2.setSource(outputStream)
frame = np.zeros((320,240,3), 'uint8')
iter = 0
while(True):
_, frame = cvSink.grabFrame(frame)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array([110, 50, 50])
upper_blue = np.array([130, 255, 255])
mask = cv2.inRange(hsv, lower_blue, upper_blue)
res = cv2.bitwise_and(frame,frame, mask=mask)
res_gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
res_blur = cv2.GaussianBlur(res_gray, (5,5), 0)
smart_dashboard.getEntry(
'Vision/Threshhold/Lower/saturation').addListener(
set_threshhold_value('lower', 'saturation'),
ntcore.constants.NT_NOTIFY_UPDATE)
smart_dashboard.getEntry('Vision/Threshhold/Lower/value').addListener(
set_threshhold_value('lower', 'value'),
ntcore.constants.NT_NOTIFY_UPDATE)
# Find primary and secondary cameras
cameras = []
for config in camera_configs:
camera = UsbCamera(config.name, config.path)
camera.setConfigJson(json.dumps(config.config))
cvsink = CvSink('cvsink: ' + config.name)
cvsink.setSource(camera)
cvsink.setEnabled(False) # disable frame fetching by default
# Add 3 things to the list of cameras: the camera object itself,
# the cvsink object, and the camera configuration dictionary.
# The first two all the only ones actually needed. Two stream
# the camera you can use in server.setsource(cam), and the
# cvsink is used to enable fetching frames. config.config is
# only used to access configuration information like the name
# and resolution.
cameras.append((camera, cvsink, config.config))
vision_camera = None
driver_cameras = []
for camera in cameras:
# The camera wanted for vision must be named 'Vision' exactly,
ntinst.startClientTeam(team)
# start cameras
# ~ these are VideoSources
cameras = []
for cameraConfig in cameraConfigs:
cameras.append(startCamera(cameraConfig))
height = 320
width = 240
inst = CameraServer.getInstance()
videoOutput = inst.putVideo("Rasp PI Output", height, width)
videoSink = CvSink("Rasp PI Converter")
img = np.ndarray((height, width, 3))
videoSink.setSource(cameras[0])
# loop forever
while True:
timestamp, img = videoSink.grabFrame(img) # this outputs a CvImage
if not timestamp:
continue
# hsl filter
# convert from float to uint8
img = img.astype(dtype="uint8")
# get binary mask
hsv_frame = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lowerbh = wall_o.getNumber("lowerBoundH", 15)
lowerbs = wall_o.getNumber("lowerBoundS", 100)
lowerbv = wall_o.getNumber("lowerBoundV", 130)
upperbh = wall_o.getNumber("upperBoundH", 60)