def __init__(self, start, end, rate, step_size=50):
self.start = start
self.end = end
self.rate = rate
now = datetime.datetime.now()
self.log_direc = "/media/data/Data/Logs/{}_{}_{}_{}_{}/".format(
now.year, now.month, now.day, now.hour, now.minute)
try:
os.mkdir(self.log_direc)
except FileExistsError as e:
print(e)
self.i = 0
self.shaker = shaker.Shaker()
self.shaker.change_duty(self.start)
self.step_size = step_size
cam_num = camera.guess_camera_number()
port = STEPPER_CONTROL
self.ard = arduino.Arduino(port)
self.motors = stepper.Stepper(self.ard)
self.motors.move_motor(1, 100, '+')
self.motors.move_motor(2, 100, '+')
self.motors.move_motor(1, 100, '-')
self.motors.move_motor(2, 100, '-')
self.cam = camera.Camera(cam_num=cam_num)
im = self.cam.get_frame()
self.hex, self.center, self.crop, self.mask = self.find_hexagon(im)
im = images.crop_and_mask(im, self.crop, self.mask)
self.im_shape = im.shape
im = images.draw_polygon(im, self.hex)
im = images.draw_circle(im, self.center[0], self.center[1], 3)
images.display(im)
def get_circles(frame):
red = frame[:, :, 0] - frame[:, :, 2]
red = images.threshold(red, 65)
opened = images.opening(red, (31, 31))
w = opened.shape[1]
im1, im2 = opened[:, :w//2], opened[:, w//2:]
images.display(im1)
m1 = list(images.center_of_mass(im1))
m2 = list(images.center_of_mass(im2))
m2[0] += w//2
return np.array([[m1[0], m1[1], 50], [m2[0], m2[1], 50]])
def __init__(self):
self.shaker = shaker.Shaker()
self.shaker.change_duty(600)
port = STEPPER_CONTROL
self.ard = arduino.Arduino(port)
self.motors = stepper.Stepper(self.ard)
cam_num = camera.guess_camera_number()
self.cam = camera.Camera(cam_num=cam_num)
im = self.cam.get_frame()
self.hex, self.center, self.crop, self.mask = self.find_hexagon(im)
im = images.crop_and_mask(im, self.crop, self.mask)
self.im_shape = im.shape
im = images.draw_polygon(im, self.hex)
im = images.draw_circle(im, self.center[0], self.center[1], 3)
images.display(im)
from particletracking import dataframes
from labvision import video, images
import numpy as np
import filehandling
file_store = "/media/data/Data/FirstOrder/Hysterisis/FlatPlate/Trial2/0.2_up_1.hdf5"
file_vid = "/media/data/Data/FirstOrder/Hysterisis/FlatPlate/Trial2/0.2_up_1.MP4"
metadata = dataframes.load_metadata(file_store)
vid = video.ReadVideo(file_vid)
box = metadata['crop']
frame = vid.read_frame(vid.num_frames - 1)
frame = images.crop(frame, box)
xmin = 750
xmax = 1250
ymin = 750
ymax = 1250
vertices = np.array([[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]])
frame = images.draw_polygon(frame, vertices, thickness=3)
images.save(
frame,
"/media/data/Data/FirstOrder/Hysterisis/FlatPlate/Trial2/HexagonFigures/box_end.png"
)
images.display(frame)
p1 = [333, 1318] p2 = [1784, 528] midpoint = np.array([p1[0] - p2[0], p1[1] - p2[1]]) h = h + midpoint[1] x = x + midpoint[0] m = (p2[1] - p1[1]) / (p2[0] - p1[0]) a = -atan(m) print(a) points = np.vstack((x, h)).T print(points.shape) points = rotate_points(points, midpoint, -a) print(points[0]) print(points[1]) points[:, 0] += -points[0, 0] + p1[0] points[:, 1] += -points[0, 1] + p1[1] points = np.int32(points) # points = points.reshape((points.shape[0], 1, points.shape[1])) # lines = [((a[0], b[0]), (a[1], b[1])) for a, b in zip(points[1:], points[:-1])] # print(lines) im = cv2.polylines(im, [points], False, images.YELLOW, 10) im = images.rotate(im, 30) # im = images.draw_contours(im, [points], images.YELLOW, 10) images.display(im) images.save(im, direc + 'boundary.jpeg') # plt.savefig(direc+'boundary.jpeg', quality=95, dpi=300) # plt.show()
from labvision import video, images
vid = video.ReadVideo("/home/ppxjd3/Code/labvision/labvision/data/numbered_video.mp4", frame_range=(0, 100, 10))
for f in range(vid.num_frames):
frame = vid.read_next_frame()
images.display(frame, f"{f}")
def test(self):
filepath = os.path.join(data_dir, "maxresdefault.jpg")
im = images.read_img(filepath)
im = images.rotate(im, 90)
images.display(im)
def run(direc, lattice_spacing=5):
files = filehandling.get_directory_filenames(direc + '/*.png')
print(files)
savename = direc + '/data.hdf5'
N = len(files)
# load images
ims = [images.load(f, 0) for f in tqdm(files, 'Loading images')]
images.display(ims[0])
# Find Circles
ims = [images.bgr_to_gray(im) for im in ims]
circles = [
images.find_circles(im, 27, 200, 7, 16, 16)
for im in tqdm(ims, 'Finding Circles')
]
# Save data
data = dataframes.DataStore(savename, load=False)
for f, info in tqdm(enumerate(circles), 'Adding Circles'):
data.add_tracking_data(f, info, ['x', 'y', 'r'])
# Calculate order parameter
calc = statistics.PropertyCalculator(data)
calc.order()
# Get the course graining width
cgw = get_cgw(data.df.loc[0]) / 2
# Create the lattice points
x = np.arange(0, max(data.df.x), lattice_spacing)
y = np.arange(0, max(data.df.y), lattice_spacing)
x, y = np.meshgrid(x, y)
# Calculate the coarse order fields
fields = [
coarse_order_field(data.df.loc[f], cgw, x, y)
for f in tqdm(range(N), 'Calculating Fields')
]
# Calculate the field threshold
field_threshold = get_field_threshold(fields, lattice_spacing, ims[0])
# Find the contours representing the boundary in each frame
contours = [
find_contours(f, field_threshold)
for f in tqdm(fields, 'Calculating contours')
]
# Multiply the contours by the lattice spacing
contours = [c * lattice_spacing for c in contours]
# Find the angle of the image to rotate the boundary to the x-axis
a, c, p1, p2 = get_angle(ims[0])
print(p1)
print(p2)
# Rotate the selection points and the contours by the angle
p1 = rotate_points(np.array(p1), c, a)
p2 = rotate_points(np.array(p2), c, a)
contours = [rotate_points(contour.squeeze(), c, a) for contour in contours]
xmin = int(p1[0])
xmax = int(p2[0])
h = int(p1[1])
# Get the heights of the fluctuations from the straight boundary
hs = [
get_h(contour, ims[0].shape, xmin, xmax, h)
for contour in tqdm(contours, 'Calculating heights')
]
# Calculate the fourier transforms for all the frames
L = xmax - xmin
pixels_to_mms = 195 / L
print("pixels_to_mms = ", pixels_to_mms)
# convert to mm
hs = [h * pixels_to_mms for h in hs]
L = L * pixels_to_mms
x = np.linspace(0, L, len(hs[0]))
np.savetxt(direc + '/x.txt', x)
np.savetxt(direc + '/hs.txt', hs)
# k, yplot = get_fourier(hs, L)
return k, yplot
from labvision import video, images
vid = video.ReadVideo(
"/home/ppxjd3/Code/labvision/labvision/data/numbered_video.mp4")
frame_numbers = [0, 1, 2, 3, 10, 11, 20, 21, 20, 19, 18]
for f in frame_numbers:
frame1 = vid.read_frame(f)
frame2 = vid.read_frame(f)
frame = images.hstack(frame1, frame2)
images.display(frame, title=f'{f}')