class Evaluator(object):
def __init__(self):
self.System_Markers = {}
self.GT_Markers = {}
self.gt_db = None
self.system_db = None
def load_GT_marker_from_clickpoints(self, path, type):
self.gt_db = DataFileExtended(path)
markers = np.asarray([[m.image.sort_index, m.x, m.y] for m in self.gt_db.getMarkers(type=type)])
self.GT_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def load_System_marker_from_clickpoints(self, path, type):
self.system_db = DataFileExtended(path)
image_filenames = set([img.filename for img in self.gt_db.getImages()]).intersection([img.filename for img in self.system_db.getImages()])
markers = np.asarray([[self.gt_db.getImage(filename=m.image.filename).sort_index, m.x, m.y] for m in self.system_db.getMarkers(type=type) if m.image.filename in image_filenames])
self.System_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def save_marker_to_GT_db(self, markers, path, type):
# if function is None:
# function = lambda x : x
self.gt_db = DataFileExtended(path)
with self.gt_db.db.atomic() as transaction:
self.gt_db.deleteMarkers(type=type)
for t in markers:
self.gt_db.setMarkers(image=self.gt_db.getImage(frame=t),x=markers[t].T[0],y=markers[t].T[1], type=type)
def save_marker_to_System_db(self, markers, path, type):
if function is None:
function = lambda x : x
self.system_db = DataFileExtended(path)
with self.system_db.db.atomic() as transaction:
self.system_db.deleteMarkers(type=type)
for t in markers:
self.system_db.setMarkers(image=self.system_db.getImage(frame=t),x=markers[t].T[0],y=markers[t].T[1], type=type)
[min(orth_y),
max(orth_y)]])
hist *= scale
hist[hist == 0.] = np.nan
cx = cy = 6
hist_img, binx_img, biny_img = np.histogram2d(Positions_Img.T[0],
Positions_Img.T[1],
bins=[4608 / cx, 2592 / cy],
range=[[0, 4608], [0, 2592]])
hist_img *= scale * cx * cy
hist_img[hist_img == 0.] = np.nan
# beauty-corrections for background image
from skimage.exposure import equalize_adapthist, adjust_gamma
image = equalize_adapthist(db.getImage(0).data)
image = adjust_gamma(image, gamma=.5)
# Plotting
fig, ax = plt.subplots()
ax.set_title("Position Heatmap")
ax.set_xlabel("X-Distance to camera in m")
ax.set_ylabel("Y-Distance to camera in m")
ax.imshow(CT.getTopViewOfImage(
image, [-max(binx), -min(binx),
min(biny), max(biny)]),
extent=[-max(binx), -min(binx),
min(biny), max(biny)])
hist_plot = ax.imshow(
hist.T[::-1, ::-1],
extent=[-max(binx), -min(binx),
State_Dist = ss.multivariate_normal(cov=Q) # Initialize Distributions for Filter
Meas_Dist = ss.multivariate_normal(cov=R) # Initialize Distributions for Filter
# Initialize Filter
MultiKal = MultiFilter(KalmanFilter, model, np.diag(Q),
np.diag(R), meas_dist=Meas_Dist, state_dist=State_Dist)
# MultiKal.LogProbabilityThreshold = -300.
MultiKal.MeasurementProbabilityThreshold = 0.
# MultiKal = MultiFilter(Filter, model)
print("Initialized Tracker")
# Init_Background from Image_Median
# Initialize segmentation with init_image and start updating the first 10 frames.
N = db.getImages().count()
init = np.array(np.median([np.asarray(db.getImage(frame=j).data, dtype=np.int)
for j in np.arange(0,10)], axis=0), dtype=np.int)
# VB = ViBeSegmentation(n=2, init_image=init, n_min=2, r=25, phi=1)
VB = ViBeSegmentation(n=3, init_image=init, n_min=3, r=40, phi=1)
print("Debug")
for i in range(10,20):
mask = VB.detect(db.getImage(frame=i).data, do_neighbours=False)
print("Detecting!")
import matplotlib.pyplot as plt
# for i in range(10306,10311):
# mask = VB.detect(db.getImage(frame=i).data, do_neighbours=False)
# fig, ax = plt.subplots(1)
# # ax.imshow(np.vstack((mask*2**8, db.getImage(frame=i).data)))
# ax.imshow(np.vstack((mask[:,16000:18000]*2**8, db.getImage(frame=i).data[:,16000:18000])))
cov=Q) # Initialize Distributions for Filter
Meas_Dist = ss.multivariate_normal(
cov=R) # Initialize Distributions for Filter
# Initialize Filter
MultiKal = MultiFilter(KalmanFilter,
model,
np.diag(Q),
np.diag(R),
meas_dist=Meas_Dist,
state_dist=State_Dist)
# Init_Background from Image_Median
N = db.getImages().count()
init = np.asarray(np.median([
np.asarray(rgb2gray(db.getImage(frame=j).data), dtype=np.int)
for j in np.random.randint(0, N, 10)
],
axis=0),
dtype=np.int)
# Load horizon-markers
horizont_type = db.getMarkerType(name="Horizon")
try:
horizon_markers = np.asarray([[m.x, m.y]
for m in db.getMarkers(type=horizont_type)
]).T
except ValueError:
raise ValueError("No markers with name 'Horizon'!")
# Load penguin-markers