import numpy as np
import seaborn as sn
import matplotlib.pyplot as plt
from PenguTrack.DataFileExtended import DataFileExtended
file_path = "/home/user/Desktop/Birdflight.cdb"
db = DataFileExtended(file_path)
PT_Track_Type = db.getMarkerType(name="PT_Track_Marker")
PT_Detection_Type = db.getMarkerType(name="PT_Detection_Marker")
PT_Prediction_Type = db.getMarkerType(name="PT_Prediction_Marker")
GT_Type = db.getMarkerType(name="GT")
Tracks = db.getTracks(type=PT_Track_Type)
mean_phi = []
phies = []
std_phi = []
mean_len = []
lens = []
for track in Tracks:
if track.markers.count() < 3:
continue
x = np.asarray([[m.x, m.y] for m in track.markers])
del_x = x[1:] - x[:-1]
del_x_norm = np.linalg.norm(del_x, axis=1)
try:
# phi = np.tensordot(del_x[1:],del_x[:-1].T, axes=1)/del_x_norm[1:]/del_x_norm[:-1]
phi = np.diag(np.tensordot(del_x[1:], del_x[:-1].T,
axes=1)) / del_x_norm[1:] / del_x_norm[:-1]
print("--------------------------")
print(VB.__dict__)
print(AD.__dict__)
print([f.__dict__ for f in AD.Filters])
print(MultiKal.__dict__)
print(MultiKal.Model.__dict__)
print("--------------------------")
print("--------------------------")
# Set Mask Type
if db.getMaskType(name="PT_Mask_Type"):
PT_Mask_Type = db.getMaskType(name="PT_Mask_Type")
else:
PT_Mask_Type = db.setMaskType(name="PT_Mask_Type", color="#FF6633")
# Define ClickPoints Marker
if db.getMarkerType(name="PT_Detection_Marker"):
PT_Detection_Type = db.getMarkerType(name="PT_Detection_Marker")
else:
PT_Detection_Type = db.setMarkerType(name="PT_Detection_Marker", color="#FFFF00", style='{"scale":0.8}')
if db.getMarkerType(name="PT_Track_Marker"):
PT_Track_Type = db.getMarkerType(name="PT_Track_Marker")
else:
PT_Track_Type = db.setMarkerType(name="PT_Track_Marker", color="#00FF00", mode=db.TYPE_Track)
if db.getMarkerType(name="PT_Prediction_Marker"):
PT_Prediction_Type = db.getMarkerType(name= "PT_Prediction_Marker")
else:
PT_Prediction_Type = db.setMarkerType(name="PT_Prediction_Marker", color="#0000FF")
if db.getMarkerType(name="PT_Stitch_Marker"):
PT_Stitch_Type = db.getMarkerType(name="PT_Stitch_Marker")
else:
PT_Stitch_Type = db.setMarkerType(name="PT_Stitch_Marker", color="#FF8800", mode=db.TYPE_Track)
# Initialize segmentation with init_image and start updating the first 10 frames.
init_buffer = []
for i in range(10):
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'!")
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,
# # 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])))
# plt.show()
# Initialize Detector
AD = AreaDetector(object_area, object_number, upper_limit=10, lower_limit=1)
print('Initialized')
# SetMaskType
if db.getMaskType(name="PT_Mask_Type"):
PT_Mask_Type = db.getMaskType(name="PT_Mask_Type")
else:
PT_Mask_Type = db.setMaskType(name="PT_Mask_Type", color="#FF6633")
# Define ClickPoints Marker
if db.getMarkerType(name="PT_Detection_Marker"):
marker_type = db.getMarkerType(name="PT_Detection_Marker")
else:
marker_type = db.setMarkerType(name="PT_Detection_Marker", color="#FFFF00", style='{"scale":0.8}')
if db.getMarkerType(name="PT_Track_Marker"):
marker_type2 = db.getMarkerType(name="PT_Track_Marker")
else:
marker_type2 = db.setMarkerType(name="PT_Track_Marker", color="#00FF00", mode=db.TYPE_Track)
if db.getMarkerType(name="PT_Prediction_Marker"):
marker_type3 = db.getMarkerType(name= "PT_Prediction_Marker")
else:
marker_type3 = db.setMarkerType(name="PT_Prediction_Marker", color="#0000FF")
if db.getMarkerType(name="PT_Stitch_Marker"):
marker_type4 = db.getMarkerType(name="PT_Stitch_Marker")
else:
marker_type4 = db.setMarkerType(name="PT_Stitch_Marker", color="#FF8800", mode=db.TYPE_Track)
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
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'!")