def update(self):
info = self.data.df.loc[self.frame_no, ['x', 'y', 'r']]
features = statistics.order.order_process(info,
self.param_dict['rad_t'][0])
self.im0 = images.crop_img(self.im0, self.data.metadata['crop'])
self._display_img(
images.add_colorbar(
images.draw_circles(
self.im0, features[['x', 'y', 'r', 'order_mag']].values)))
def analyse_frame(self):
if self.tracking:
frame = self.cap.read_next_frame()
else:
frame = self.cap.find_frame(self.parameters['frame'][0])
new_frame, boundary, cropped_frame = self.ip.process(frame)
circles = tp.locate(new_frame, 21, characterize=False)
circles['r'] = 15
if self.tracking:
return circles[['x', 'y', 'r']], boundary, ['x', 'y', 'r']
else:
annotated_frame = images.draw_circles(cropped_frame, circles)
return new_frame, annotated_frame
def tracking(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_img(vid.find_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 order(filename):
core_name = os.path.splitext(filename)[0]
vid_name = filename
data_name = core_name + '.hdf5'
out_name = core_name + '_check_order.png'
data = dataframes.DataStore(data_name)
crop = data.metadata['crop']
vid = video.ReadVideo(vid_name)
frame = 100
im = images.crop_img(vid.find_frame(frame), crop)
circles = data.df.loc[frame, ['x', 'y', 'r', 'order_r', 'order_i']]
circles['order_mag'] = np.abs(circles.order_r + 1j * circles.order_i)
circles = circles[['x', 'y', 'r', 'order_mag']].values
out = images.draw_circles(im, circles)
out = images.add_colorbar(out)
images.display(out)
images.save(out, out_name)
def analyse_frame(self):
if self.tracking:
frame = self.cap.read_next_frame()
else:
frame = self.cap.find_frame(self.parameters['frame'][0])
new_frame, boundary, cropped_frame = self.ip.process(frame)
circles = images.find_circles(new_frame,
self.parameters['min_dist'][0],
self.parameters['p_1'][0],
self.parameters['p_2'][0],
self.parameters['min_rad'][0],
self.parameters['max_rad'][0])
circles = get_points_inside_boundary(circles, boundary)
circles = check_circles_bg_color(circles, new_frame, 150)
if self.tracking:
return circles, boundary, ['x', 'y', 'r']
else:
annotated_frame = images.draw_circles(cropped_frame, circles)
return new_frame, annotated_frame
def analyse_frame(self):
"""
Change steps in this method.
This is done every frame
Returns
-------
info:
(N, X) array containing X values for N tracked objects
boundary:
from preprocessor
info_headings:
List of X strings describing the values in circles
"""
if self.tracking:
frame = self.cap.read_next_frame()
else:
frame = self.cap.find_frame(self.parameters['frame'][0])
new_frame, boundary, cropped_frame = self.ip.process(frame)
### ONLY EDIT BETWEEN THESE COMMENTS
info =tp.locate(
new_frame,
self.parameters['min_dist'][0],
self.parameters['p_1'][0],
self.parameters['p_2'][0],
self.parameters['min_rad'][0],
self.parameters['max_rad'][0])
info_headings = ['x', 'y', 'r']
### ONLY EDIT BETWEEN THESE COMMENTS
if self.tracking:
return info, boundary, info_headings
else:
# THIS NEXT LINE CAN BE EDITED TOO
annotated_frame = images.draw_circles(cropped_frame, info)
return new_frame, annotated_frame