class Evaluator(object):
def __init__(self):
self.pos_System_Markers = {}
self.neg_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_pos_System_marker_from_clickpoints(self, path, type):
self.system_db = DataFileExtended(path)
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 self.gt_db.getImage(filename=m.image.filename) is not None])
self.pos_System_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def load_neg_System_marker_from_clickpoints(self, path, type):
self.system_db = DataFileExtended(path)
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 self.gt_db.getImage(filename=m.image.filename) is not None])
self.neg_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)
if __name__ == '__main__':
import my_plot
import numpy as np
import clickpoints
import matplotlib.pyplot as plt
from PenguTrack.DataFileExtended import DataFileExtended
# Load Data from Databases
db = DataFileExtended(
"/home/birdflight/Desktop/PT_Test_full_n3_r7_A20_filtered.cdb")
db2 = DataFileExtended("/home/birdflight/Desktop/252_GT_Detections.cdb")
LogType = db.getMarkerType(name="PT_Detection_Marker")
ImgType = db.getMarkerType(name="PT_Track_Marker")
# Positions_Log = np.asarray([[m.x, m.y, m.image.sort_index] for m in db.getMarkers(type=LogType) if not m.text.count("inf")])
Positions_Img = np.asarray([[m.x, m.y, m.image.sort_index]
for m in db.getMarkers(type=ImgType)
if m.track.markers.count() > 3])
# Do Correction for Position Data
from CameraTransform import CameraTransform
CT = CameraTransform(14, [17, 9], [4608, 2592],
observer_height=31.,
angel_to_horizon=(np.pi / 2. - 0.24) * 180 / np.pi)
orth_x, orth_y, orth_z = CT.transCamToWorld(Positions_Img.T[:2], Z=0.525)
# Calculate Histogramms
cx = cy = 2
times = np.asarray(sorted([i.timestamp for i in db.getImages()]))
scale = 1. / (cx * cy) / ((times[-1] - times[0]).seconds / 3600.)
hist, binx, biny = np.histogram2d(orth_x,
orth_y,
while True:
img, meta = getImage()
if img is not None:
print("Got img from cam")
init_buffer.append(img)
print(init_buffer[-1].shape)
print(init_buffer[-1].dtype)
break
init = np.array(np.median(init_buffer, axis=0))
# Load horizon-markers
horizont_type = db_start.getMarkerType(name="Horizon")
try:
horizon_markers = np.array(
[[m.x, m.y] for m in db_start.getMarkers(type=horizont_type)]).T
except ValueError:
raise ValueError("No markers with name 'Horizon'!")
# Load penguin-markers
penguin_type = db_start.getMarkerType(name="Penguin_Size")
try:
penguin_markers = np.array([[m.x1, m.y1, m.x2, m.y2]
for m in db_start.getLines(type="Penguin_Size")
]).T
except ValueError:
raise ValueError("No markers with name 'Horizon'!")
VB = SiAdViBeSegmentation(horizon_markers,
14e-3, [17e-3, 9e-3],
penguin_markers,
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
penguin_type = db.getMarkerType(name="Penguin_Size")
try:
penguin_markers = np.asarray([[m.x1, m.y1, m.x2, m.y2]
for m in db.getLines(type="Penguin_Size")]).T
except ValueError:
raise ValueError("No markers with name 'Horizon'!")
# Initialize segmentation with init_image and start updating the first 10 frames.
VB = SiAdViBeSegmentation(horizon_markers,
14e-3, [17e-3, 9e-3],