def move_stage_and_record(step, N_frames, microscope, data_group, template,
dwell_time):
# move the stage by a given step, recording motion as we go.
ms = microscope
data_group.attrs['step'] = step
ms.camera.start_preview(resolution=(640, 480))
# we will use a RAM buffer to record a bunch of frames
outputs = [io.BytesIO() for i in range(N_frames)]
stop_moving_event = threading.Event()
stage_moves = []
movement_thread = threading.Thread(target=move_stage,
args=(step, dwell_time,
stop_moving_event, ms.stage,
stage_moves),
name='stage_movement_thread')
movement_thread.start()
print(
"Starting acquisition of {} frames, should take about {:.0}s.".format(
N_frames, N_frames / ms.camera.framerate))
try:
start_t = time.time()
camera.capture_sequence(outputs, 'jpeg', use_video_port=True)
end_t = time.time()
finally:
print("Stopping...")
stop_moving_event.set()
movement_thread.join()
ms.camera.stop_preview()
print("Recorded {} frames in {} seconds ({} fps)".format(
N_frames, end_t - start_t, N_frames / (end_t - start_t)))
print("Camera framerate was set to {}, and reports as {}".format(
framerate, camera.framerate))
data_group['stage_moves'] = np.array(stage_moves)
data_group['stage_moves'].attrs['description'] = "t,x,y,z data for the stage's motion during the sequence of moves " \
"time is in seconds, position is in stage steps."
# go through the captured images and process them.
data = np.zeros((N_frames, 3))
for j, k in enumerate(outputs):
frame_data = np.fromstring(k.getvalue(), dtype=np.uint8)
frame = cv2.imdecode(frame_data, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
data[j, 1:], corr = find_template(template,
frame - np.mean(frame),
return_corr=True,
fraction=0.5)
data[j, 0] = float(j) / float(framerate) + start_t
printProgressBar(j, N_frames)
print("")
data_group["camera_motion"] = data
data_group["camera_motion"].attrs['description'] = "t,x,y data of the position, as recorded by the camera." \
"t is in seconds, x/y are in pixels"
data_group["camera_motion"].attrs[
'reported_framerate'] = ms.camera.framerate
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(start_t, ms, templ8):
data = np.zeros((1, 3))
data[0, 0] = time.time() - start_t
frame = ms.rgb_image().astype(np.float32)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
data[0, 1:], corr = find_template(templ8,
frame - np.mean(frame),
return_corr=True,
fraction=0.5)
return data
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
def measure_txy(ms, start_t, templ8):
txy = np.zeros((1, 3))
txy[0, 0] = time.time() - start_t
frame = ms.rgb_image().astype(np.float32)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
txy[0, 1:], corr = find_template(templ8, frame - np.mean(frame), return_corr = True, fraction=0.5)
camera.stop_preview()
cv2.imshow("corr", corr * 255.0 / np.max(corr))
cv2.waitKey(1000)
camera.start_preview()
return txy, frame
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
try:
while loop:
data = np.zeros(
(3, N_frames)) #creates the array in which data is stored
for i in range(data.shape[1]):
#takes 100 images and compares the location of the spot with the template
frame = get_numpy_image(camera, True)
spot_coord = find_template(templ8, frame)
data[1, i] = spot_coord[0]
data[2, i] = spot_coord[1]
data[0, i] = time.time() - start_t
df.add_data(data, data_gr, "data") #writes data to .hdf5 file
imgfile_location = "/home/pi/dev/fibre_stage_characterisation/frames/drift_%s.jpg" % time.strftime(
"%Y%m%d_%H%M%S")
cv2.imwrite(imgfile_location, frame)
try:
if time.gmtime(time.time())[3] in [
0, 4, 8, 12, 16, 20
] and tweet: #tweets a picture and co-ordinates every 4 hours
api.update_with_media(
imgfile_location,
status="I'm currently at %d, %d" %
(spot_coord[0], spot_coord[1]))
img = Image.open(
"/home/pi/summer/drift/calibration/step_size_templ8.jpg")
pad = Image.new('RGB',
(352, 192)) #Tuple must be multiples of 32 and 16
pad.paste(img, (0, 0))
overlay = camera.add_overlay(pad.tobytes(), size=(352, 192))
overlay.alpha = 128
overlay.fullscreen = False
overlay.layer = 3
initial_stage_position = stage.position
frame = ms.rgb_image().astype(np.float32)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
templ8_position = np.zeros((1, 2))
templ8_position[0, :], corr = find_template(templ8,
frame - np.mean(frame),
return_corr=True,
fraction=0.5)
def move_overlay(cx, cy):
"""move the overlay to show a shift of cx,cy camera pixels"""
x = int(960 + (cx - templ8_position[0, 0] - 176) * 2.25)
y = int(540 + (cy - templ8_position[0, 1] - 96) * 2.25)
overlay.window = (x, y, int(352 * 2.25), int(192 * 2.25))
stage.move_rel([-side_length / 2, 0, -side_length / 2])
for i in range(points):
stage.move_rel([side_length / (points - 1), 0, 0])
data_stage[i, :] = stage.position
frame = ms.rgb_image().astype(np.float32)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
try:
start_t = time.time()
camera.capture_sequence(outputs, 'jpeg', use_video_port=True)
end_t = time.time()
finally:
event.set()
t.join()
stage.move_abs(initial_stage_position)
camera.stop_preview()
print("Stopping...")
print("Recorded {} frames in {} seconds ({} fps)".format(
N_frames, end_t - start_t, N_frames / (end_t - start_t)))
print("Camera framerate was set to {}, and reports as {}".format(
framerate, camera.framerate))
for j, k in enumerate(outputs):
frame_data = np.fromstring(k.getvalue(), dtype=np.uint8)
frame = cv2.imdecode(frame_data, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
data[j, 1:], corr = find_template(templ8,
frame - np.mean(frame),
return_corr=True,
fraction=0.5)
data[j, 0] = float(j) / float(framerate)
printProgressBar(j, N_frames)
print("")
df.add_data(data, cam_pos, "data")
"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.move_rel(stage.backlash)
for i in range(points):
stage_pos[i, 0:] = stage.position
frame = get_numpy_image(camera, True)
cam_pos[i, 0:], corr = find_template(templ8,
frame - background,
return_corr=True)
stage.move_rel([side_length / points, 0, 0])
camera.stop_preview()
cv2.imshow("corr", corr.astype(float) / np.max(corr) * 255)
cv2.waitKey(1000)
camera.start_preview()
# time.sleep(1)
for j in range(points):
stage_pos[j + points, 0:] = stage.position
frame = get_numpy_image(camera, True)
i = j + points
cam_pos[i, 0:], corr = find_template(templ8,
frame - background,
return_corr=True)
stage.move_rel([0, side_length / points, 0])
camera.start_preview(resolution=(640, 480))
#stage.move_rel([-backlash, -backlash, -backlash])
#stage.move_rel([backlash, backlash, backlash])
data_group['initial_sample_image'] = ms.rgb_image()
frame = ms.rgb_image().astype(np.float32).mean(axis=2)
#mean = np.mean(image)
#templ8 = (image - mean)[100:-100, 100:-100]
template = np.load("template.npz")['template']
data_group['template_image'] = template
d, corr = find_template(template,
frame,
return_corr=True,
fraction=0.15)
initial_stage_position = stage.position
start_time = time.time()
data_group.attrs['start_time'] = start_time
last_saved_image_time = 0
try:
while True:
for d in [1, 0, -1, 0]:
data_stage = np.zeros((n_steps + 1, 3))
data_cam = np.zeros((n_steps + 1, 2))
data_time = np.zeros((n_steps + 1, ))
for i in range(n_steps + 1):
if i > 0:
loop = True
tweet = True
image = get_numpy_image(camera, greyscale = True).astype(np.float32)
background = cv2.GaussianBlur(image, (41, 41), 0)
templ8 = (image - background)[100:-100, 100:-100]
start_t = time.time()
try:
while loop:
data = np.zeros((N_frames, 3))
for i in range(N_frames):
frame = get_numpy_image(camera, True)
data[i, 1:], corr = find_template(templ8, frame - background, return_corr = True)
cv2.imshow("corr", corr.astype(float) / np.max(corr) * 255)
cv2.waitKey(1000)
data[i, 0] = time.time() - start_t
df.add_data(data, cam_pos, "data")
imgfile_location = "/home/pi/dev/fibre_stage_characterisation/frames/drift_%s.jpg" % time.strftime("%Y%m%d_%H%M%S")
cv2.imwrite(imgfile_location, frame)
try:
if time.gmtime(time.time())[3] in [0, 4, 8, 12, 16, 20] and tweet: #tweets a picture and co-ordinates every 4 hours
api.update_with_media(imgfile_location, status = "I'm currently at %d, %d" %(spot_coord[0], spot_coord[1]))
tweet = False
elif time.gmtime(time.time())[3] not in [0, 4, 8, 12, 16, 20]:
tweet = True
except:
pass
except KeyboardInterrupt:
mean = np.mean(image)
print("Mean value of image: {}".format(mean))
w, h = image.shape
template = (image - mean)[w // 2 - 100:w // 2 + 100,
h // 2 - 100:h // 2 + 100]
np.savez("template.npz", template=template)
plt.figure()
plt.imshow(template)
plt.suptitle("Template image")
for i in [
0.1,
]:
image = ms.rgb_image().astype(np.float32)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print("Testing with another image: shape {}, dtype {}".format(
image.shape, image.dtype))
frame = image
pos, corr = find_template(template,
frame - np.mean(frame),
return_corr=True,
fraction=i)
plt.figure()
plt.imshow(corr.astype(np.float))
plt.savefig("test_correlation_fraction_{}.pdf".format(i),
bbox_inches='tight',
dpi=180)
plt.show()
