def measure_txy(n, start_time, camera, templ8):
"""Measure position n times and return a t,x,y array."""
pos = np.zeros((3, samples)) #create some variables
for j in range(samples):
frame = get_numpy_image(camera, True)
pos[1:, j] = find_template(templ8,
frame) #measures the initial position
pos[0, j] = time.time() - start_time
return pos
def measure_txy(
n, start_time, camera, templ8
): #everything used in a definition should be put in as an argument
"""Measure position n times and return a t,x,y array."""
result = np.zeros((3, n)) #creates an empty array of zeros
for i in range(n):
frame = get_numpy_image(camera, True) #get frame
result[1:, i] = find_template(templ8, frame) #measures position
result[0, i] = time.time() - start_time #measures time
return result
640, 480)) as camera, OpenFlexureStage('/dev/ttyUSB0') as stage:
#authorises the program to acces the twitter account
auth = tweepy.OAuthHandler(twitter_keys.consumer_key,
twitter_keys.consumer_secret)
auth.set_access_token(twitter_keys.access_token,
twitter_keys.access_token_secret)
api = tweepy.API(auth)
N_frames = 100
counter = 0
#loads camera from picamera and set the stage as the arduino, lower resolution can speed up the programme
ms = microscope.Microscope(camera, stage)
ms.freeze_camera_settings()
frame = get_numpy_image(camera, True)
df = data_file.Datafile(filename="drift_81217.hdf5"
) #creates the .hdf5 file to save the data
data_gr = df.new_group(
"test_data", "A file of data collected to test this code works")
#puts the data file into a group with description
image = get_numpy_image(camera, greyscale=True) #takes template photo
templ8 = image[100:-100, 100:-100] #crops image
loop = True
tweet = True
start_t = time.time() #measure starting time
from contextlib import closing
if __name__ == "__main__":
with picamera.PiCamera(resolution = (640, 480)) as camera, \
OpenFlexureStage('/dev/ttyUSB0') as stage, \
closing(data_file.Datafile(filename = "orthogonality_15123.hdf5")) as df:
side_length = 4000
points = 10
stage.backlash = 256
camera.start_preview()
image = get_numpy_image(camera, greyscale=True).astype(np.float32)
background = cv2.GaussianBlur(image, (41, 41), 0)
templ8 = (image - background)[100:-100, 100:-100]
stage_centre = stage.position
data_stage = df.new_group(
"stage position", "orthogonality measurments, moves in a square")
data_cam = df.new_group(
"camera position", "orthogonality measurments, moves in a square")
stage_pos = np.zeros((2 * points, 3))
cam_pos = np.zeros((2 * points, 2))
stage.move_rel(
np.array([-1, -1, 0]) * side_length / 2 - stage.backlash)
stage.backlash = 0
#increased backlash correction value to get more accurate data
move = 5000
#maximum move distance without the templete travelling off field of view of the lens
#larger move distance can reduce error
stage_position = stage.position
df = data_file.Datafile(filename="step_size.hdf5")
data_step = df.new_group("step size", "step size measurments")
data = np.zeros((2, 50))
camera.start_preview()
image = get_numpy_image(camera, greyscale=True)
templ8 = image[100:-100, 100:-100]
stage.move_rel([(-move), 0, 0])
frame = get_numpy_image(camera, True)
spot_coord = find_template(templ8, frame)
x = spot_coord[0]
y = spot_coord[1]
for i in range(data.shape[1]):
stage.move_rel([(2 * move / data.shape[1]), 0, 0])
#a range of move distances to see how step size changes as the move distance increases
time.sleep(1)
#sleeps before frame saves to make sure the right image is saved
frame = get_numpy_image(camera, True)
stage.backlash = 248 #corrects for backlash
side_length = 500 #defines distance moved for each side of the sqaure
camera.start_preview() #shows preview of camera
stage_centre = stage.position #saves the starting position of stage
stage.move_rel(
[side_length / 2, side_length / 2,
0]) #moves the stage to the 'top left' corner of the square
top_left = stage.position
try:
for step in [
1, 10, 25
]: #runs for the given number of steps per side of the square
start_time = time.time()
image = get_numpy_image(camera, greyscale=True) #gets template
templ8 = image[100:-100, 100:-100] #crops image
data = sqre_move(step, side_length, start_time, camera, templ8)
sq_group = df.new_group("step_%d" % step)
df.add_data(data, sq_group, "Square with %d steps each side" %
step) #writes data to .hdf5 file
stage.move_abs(top_left)
finally:
stage.move_abs(
stage_centre) #reuturns stage to centre once program has run
