def run(self):
self.start_time = time.time()
duty = self.D0
duties = []
for step in range(self.STEPS):
duties.append(duty)
self.s.change_duty(duty)
if step % self.duty_interval == 0:
if duty > self.D1:
duty -= 1
frame = self.get_frame()
# if step % self.IMSAVE_INTERVAL == 0:
images.save(frame, f"{self.im_save}/{step}.png")
circles = self.get_circles(frame)
self.x.append(circles[0, 0])
self.x.append(circles[1, 0])
self.update_image(circles, frame)
self.update_hist()
self.update_plot()
self.update_duty_plot(duties)
self.fig.canvas.flush_events()
self.fig.canvas.start_event_loop(0.5)
if step % self.DATASAVE_INTERVAL == 0:
np.savetxt(self.data_save, self.x)
if step % 20 == 0:
plt.savefig(self.plot_save, dpi=600)
def run(self):
self.start_time = time.time()
for step in range(self.STEPS):
frame = self.get_frame()
if step % self.IMSAVE_INTERVAL == 0:
images.save(frame, f"{self.im_save}/{step}.png")
circles = self.get_circles(frame)
self.x.append(circles[0])
self.y.append(circles[1])
# self.x.append(circles[1, 0])
self.update_image(circles, frame)
# self.update_hist()
self.update_plot()
self.fig.canvas.flush_events()
self.fig.canvas.start_event_loop(0.5)
if step % self.DATASAVE_INTERVAL == 0:
np.savetxt(self.data_save_x, self.x)
np.savetxt(self.data_save_y, self.y)
if step % 20 == 0:
plt.savefig(self.plot_save, dpi=600)
def im(self):
im = self.cam.get_frame()
for f in range(10):
im = self.cam.get_frame()
im = images.crop_and_mask(im, self.crop, self.mask)
im = images.bgr_to_gray(im)
images.save(im, self.log_direc + '{}_original.png'.format(self.i))
return im
def mean_im(self):
ims = []
for f in range(8):
im = self.cam.get_frame()
im = images.crop_and_mask(im, self.crop, self.mask)
im = images.bgr_to_gray(im)
ims.append(im)
images.save(ims[0], self.log_direc + '{}_original.png'.format(self.i))
mean_im = images.mean(ims)
# mean_im = images.mask(mean_im, ring_mask)
images.save(mean_im, self.log_direc + '{}_mean.png'.format(self.i))
return mean_im
def find_center(self, im):
im0 = im.copy()
im = images.threshold(im, 80)
im = images.opening(im, (6, 6))
center = images.center_of_mass(im)
im0 = images.gray_to_bgr(im0)
im0 = images.draw_circle(im0, center[0], center[1], 5)
im = images.gray_to_bgr(im)
im = images.draw_circle(im, center[0], center[1], 5)
images.save(images.hstack(im, im0),
self.log_direc + '{}.png'.format(self.i))
self.i += 1
return center
def mean_im(self):
ims = []
for f in range(8):
im = ~self.cam.get_frame()
im = images.crop_and_mask(im, self.crop, self.mask)
# if f == 0:
# ring_mask = images.inrange(images.bgr_to_lab(im), (0, 113, 0),
# (255, 152, 255))
im = images.bgr_to_gray(im)
ims.append(im)
images.save(ims[0], self.log_direc + '{}_original.png'.format(self.i))
mean_im = images.mean(ims)
# mean_im = images.mask(mean_im, ring_mask)
images.save(mean_im, self.log_direc + '{}_mean.png'.format(self.i))
return mean_im
def check(filename):
core_name = os.path.splitext(filename)[0]
vid_name = core_name + '.MP4'
data_name = core_name + '.hdf5'
out_name = core_name + '_check.png'
data = dataframes.DataStore(data_name)
crop = data.metadata['crop']
vid = video.ReadVideo(vid_name)
print(vid_name)
frames = np.arange(4)*vid.num_frames//4
ims = [images.crop(vid.read_frame(f), crop) for f in frames]
circles = [data.get_info(f, ['x', 'y', 'r']) for f in frames]
new_ims = [images.draw_circles(im, c) for im, c in zip(ims, circles)]
out = images.vstack(images.hstack(new_ims[0], new_ims[1]),
images.hstack(new_ims[2], new_ims[3]))
images.save(out, out_name)
def find_center(self, im):
im0 = im.copy()
im = images.gaussian_blur(im, (5, 5))
circles = trackpy.locate(im, 5)
center = circles[['x', 'y']].values.mean(axis=0)
im0 = images.gray_to_bgr(im0)
im0 = images.draw_circle(im0, center[0], center[1], 5)
im1 = images.gray_to_bgr(im)
im1 = images.draw_circle(im1, center[0], center[1], 5)
im2 = images.gray_to_bgr(im)
im2 = images.draw_circles(im2, circles[['x', 'y', 'size']].values)
images.save(images.hstack(im1, im0, im2),
self.log_direc + '{}.png'.format(self.i))
self.i += 1
return center
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)
fig.show()
import os
if os.path.exists(data_save):
x = np.loadtxt(data_save).tolist()
else:
x = []
t = []
i = 0
start_time = time.time()
for step in range(3000):
for repeat in range(20):
frame = get_frame()
if i % 5 == 0:
images.save(cam.get_frame(), f"{im_save}/{i}.png")
circles = get_circles(frame)
x.append(circles[0])
current_time = time.time() - start_time
c1.remove()
c1 = get_plot_circles(circles)
ax[0].add_artist(c1)
im_artist.set_data(frame)
ax[0].set_title(f"Elapsed Time: {current_time:.1f}, data: {len(x)//2}")
freq, _ = np.histogram(x, bins=bins)
for rect, h, in zip(hist, freq):
rect.set_height(h)
ax[1].set_ylim([0, max(freq)])
ax[2].set_ylim([0, len(x)])
plot.set_xdata(x)
plot.set_ydata(np.arange(len(x)))
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()
vid = video.ReadVideo(vid_file)
metadata = dataframes.load_metadata(data_file)
print(metadata['boundary'].shape)
start = vid.read_frame(0)
middle = vid.read_frame(vid.num_frames // 2)
end = vid.read_frame(vid.num_frames - 1)
start = crop_then_mask(start)
middle = crop_then_mask(middle)
end = crop_then_mask(end)
save_direc = "/media/data/Data/FirstOrder/Hysterisis/5Repeats/RedTrayDense/HexagonFigures"
images.save(start, f'{save_direc}/start.png')
images.save(middle, f'{save_direc}/middle.png')
images.save(end, f'{save_direc}/end.png')
new_corners = get_crop_region(start, metadata['boundary'])
# start = images.draw_polygon(start, new_corners, color=images.GREEN, thickness=8)
# middle = images.draw_polygon(middle, new_corners, color=images.GREEN, thickness=8)
# end = images.draw_polygon(end, new_corners, color=images.GREEN, thickness=8)
new_corners = np.hstack((new_corners, np.ones((6, 1))*3))
start = images.draw_circles(start, new_corners, color=images.GREEN)
middle = images.draw_circles(middle, new_corners, color=images.GREEN)
end = images.draw_circles(end, new_corners, color=images.GREEN)
images.save(start, f'{save_direc}/start_hex.png')
images.save(middle, f'{save_direc}/middle_hex.png')
with open(save_direc + 'bbox.txt', 'w') as f:
f.writelines([
'BBOX xmin, xmax, ymin, ymax',
str(bbox.xmin),
str(bbox.xmax),
str(bbox.ymin),
str(bbox.ymax)
])
frame = images.draw_polygon(
frame,
np.array([[bbox.xmin, bbox.ymin], [bbox.xmin, bbox.ymax],
[bbox.xmax, bbox.ymax], [bbox.xmax, bbox.ymin]],
dtype=np.float32),
color=images.RED,
thickness=2)
images.save(frame, save_direc + 'im.png')
down_df = down_data.df[down_data.df.x.between(bbox.xmin, bbox.xmax)]
down_df = down_df[down_df.y.between(bbox.ymin, bbox.ymax)]
up_df = up_data.df[up_data.df.x.between(bbox.xmin, bbox.xmax)]
up_df = up_df[up_df.y.between(bbox.ymin, bbox.ymax)]
# down_duty, down_density = density_duty_mapped(down_df)
# up_duty, up_density = density_duty_mapped(up_df)
down_duty, down_density = density_duty_mapped(down_df)
up_duty, up_density = density_duty_mapped(up_df)
# np.savetxt(save_direc+'duty_down_rate={}'.format(rate),
# down_duty)
from labequipment import shaker
from labvision import camera, images
import time
s = shaker.Shaker()
cam_num = camera.guess_camera_number()
cam = camera.Camera(cam_num=cam_num)
save_dir = "/media/data/Data/August2020/balance_test/"
for i in range(20):
s.ramp(650, 600, 0.2)
im = cam.get_frame()
images.save(im, save_dir + '{}.png'.format(i))
time.sleep(5)
def save_img(self):
home = os.getenv("HOME")
filename, ok = QFileDialog.getSaveFileName(self, "", home + "/Pictures/",self.tr("*.jpg;; *.png;; *.tiff"))
if filename:
img = self.readvid.read_frame(n=self.framenum_slider.value)
save(img, filename)
