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)
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
print(time.time() - startTime)
#cv2.imshow('frame', img)
vidSource.putFrame(image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
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:
#Change the time front camera
timestamp, image_F = sinkF.grabFrame(image_F)
#change time yellow ball camera
timestamp, image_Y = sinkY.grabFrame(image_Y)
grip_yellow.process(image_Y)
#change time shooter camera
timestamp, image_G = sinkG.grabFrame(image_G)
grip_green.process(image_G)
contours_output_green = grip_green.filter_contours_output
contours_output_yellow = grip_yellow.filter_contours_output
if (contours_output_green):
image_G = getValuesGreen(image_G)
smart_dashboard.putNumber('Vision/Threshhold/Upper/hue',
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:
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)
res_thresh = cv2.threshold(res_blur, 60, 255, cv2.THRESH_BINARY)[1]
res_eroded = cv2.erode(res_thresh, np.ones((11,11)))
res_dilated = cv2.dilate(res_eroded, np.ones((11,11)))
res_color = cv2.cvtColor(res_dilated, cv2.COLOR_GRAY2BGR)
resized = convenience.resize(res, width=300)
ratio = res.shape[0] / float(resized.shape[0])
width, height, 28)
img = np.zeros(shape=(width, height, 3), dtype=np.uint8)
count = 0
global topX
global topY
global botX
global botY
global direction
global alreadyFound
while True:
topX = -2
topY = -2
botX = -2
botY = -2
direction = 0
alreadyFound = False
GotFrame, frame = CvSink.grabFrame(img)
if GotFrame == 0:
outputStream.notifyError(CvSink.getError())
continue
SDV.putNumber(("Count"), count)
#findReflectiveTape()
print("Blue: " + str(frame[239, 0, 0]) + ", Green: " +
str(frame[239, 0, 1]) + ", Red: " + str(frame[239, 0, 2]))
count = count + 1
SDV.putNumber(("TopX"), topX)
SDV.putNumber(("TopY"), topY)
SDV.putNumber(("BotX"), botX)
SDV.putNumber(("BotY"), botY)
SDV.putNumber(("Direction"), direction)
# ~ 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)
upperbs = wall_o.getNumber("upperBoundS", 255)
upperbv = wall_o.getNumber("upperBoundV", 255)
lowerBound = np.array([lowerbh, lowerbs, lowerbv])