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[tracking_results] docstrings added

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Xaver Roos 2022-03-28 14:35:07 +02:00
parent bf2c2d21a2
commit 5b3540e304
1 changed files with 86 additions and 46 deletions
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132
etrack/tracking_result.py
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@ -17,8 +17,19 @@ center_meter = ((center[0] - x_0) * x_factor, (center[1] - y_0) * y_factor)
class TrackingResult(object):
def __init__(self, results_file, x_0=0, y_0= 0, width_pixel=1230, height_pixel=1100, width_meter=0.81, height_meter=0.81) -> None:
def __init__(self, results_file, x_0=0, y_0= 0, width_pixel=1975, height_pixel=1375, width_meter=0.81, height_meter=0.81) -> None:
super().__init__()
"""Width refers to the "x-axis" of the tank, height to the "y-axis" of it.
Args:
results_file (_type_): Results file of the before done animal tracking.
x_0 (int, optional): . Defaults to 95.
y_0 (int, optional): _description_. Defaults to 185.
width_pixel (int, optional): Width from one lightened corner of the tank to the other. Defaults to 1975.
height_pixel (int, optional): Heigth from one lightened corner of the tank to the other. Defaults to 1375.
width_meter (float, optional): Width of the tank in meter. Defaults to 0.81.
height_meter (float, optional): Height of the tank in meter. Defaults to 0.81.
"""
if not os.path.exists(results_file):
raise ValueError("File %s does not exist!" % results_file)
self._file_name = results_file
@ -28,36 +39,51 @@ class TrackingResult(object):
self.width_m = width_meter
self.height_pix = height_pixel
self.height_m = height_meter
self.x_factor = self.width_m / self.width_pix # m/pix
self.x_factor = self.width_m / self.width_pix # m/pix
self.y_factor = self.height_m / self.height_pix # m/pix
self.center = (np.round(self.x_0 + self.width_pix/2), np.round(self.y_0 + self.height_pix/2))
self.center_meter = ((self.center[0] - self.x_0) * self.x_factor, (self.center[1] - self.y_0) * self.y_factor)
self.center = (np.round(self.x_0 + self.width_pix/2), np.round(self.y_0 + self.height_pix/2)) # middle of width and height --> center
self.center_meter = ((self.center[0] - self.x_0) * self.x_factor, (self.center[1] - self.y_0) * self.y_factor) # center in meter by multipling with factor
self._data_frame = pd.read_hdf(results_file)
self._level_shape = self._data_frame.columns.levshape
self._scorer = self._data_frame.columns.levels[0].values
self._bodyparts = self._data_frame.columns.levels[1].values if self._level_shape[1] > 0 else []
self._positions = self._data_frame.columns.levels[2].values if self._level_shape[2] > 0 else []
self._data_frame = pd.read_hdf(results_file) # read dataframe of scorer
self._level_shape = self._data_frame.columns.levshape # shape of dataframe (?)
self._scorer = self._data_frame.columns.levels[0].values # scorer of dataset
self._bodyparts = self._data_frame.columns.levels[1].values if self._level_shape[1] > 0 else [] # tracked body parts
self._positions = self._data_frame.columns.levels[2].values if self._level_shape[2] > 0 else [] # position in x and y values and the likelihood of it
def angle_to_center(self, bodypart=0, twopi=True, origin="topleft", min_likelihood=0.95):
if isinstance(bodypart, nb.Number):
bp = self._bodyparts[bodypart]
elif isinstance(bodypart, str) and bodypart in self._bodyparts:
def angle_to_center(self, bodypart=0, twopi=True, inversed_yaxis=False, min_likelihood=0.95):
"""Angel of animal position in relation to the center of the tank.
Args:
bodypart (int, optional): Bodypart of the animal. Defaults to 0.
twopi (bool, optional): _description_. Defaults to True.
inversed_yaxis (bool, optional): Inversed y-axis = True when 0 is at the top of axis. Defaults to False.
min_likelihood (float, optional): The likelihood of the position estimation. Defaults to 0.95.
Raises:
ValueError: No valid x-position values.
Returns:
phi: Angle of animal in relation to center.
"""
if isinstance(bodypart, nb.Number): # check if the instance bodypart of this class is a number
bp = self._bodyparts[bodypart]
elif isinstance(bodypart, str) and bodypart in self._bodyparts: # or if bodypart is a string
bp = bodypart
else:
raise ValueError("Bodypart %s is not in dataframe!" % bodypart)
_, x, y, _, _ = self.position_values(bodypart=bp, min_likelihood=min_likelihood)
raise ValueError("Bodypart %s is not in dataframe!" % bodypart) # or if it is existing
_, x, y, _, _ = self.position_values(bodypart=bp, min_likelihood=min_likelihood) # set x and y values, already in meter from position_values
if x is None:
print("Error: no valid angles for %s" % self._file_name)
return []
x_meter = x - self.center_meter[0]
y_meter = y - self.center_meter[1]
if origin.lower() == "topleft":
y_meter *= -1
phi = np.arctan2(y_meter, x_meter) * 180 / np.pi
x_to_center = x - self.center_meter[0] #
y_to_center = y - self.center_meter[1]
if inversed_yaxis == True:
y_to_center *= -1
phi = np.arctan2(y_to_center, x_to_center) * 180 / np.pi
if twopi:
phi[phi < 0] = 360 + phi[phi < 0]
return phi
def coordinate_transformation(self, position):
@ -85,24 +111,24 @@ class TrackingResult(object):
def positions(self):
return self._positions
def position_values(self, scorer=0, bodypart=0, framerate=30, interpolate=True, min_likelihood=0.95):
"""returns the x and y positions in m and the likelihood of the positions.
def position_values(self, scorer=0, bodypart=0, framerate=25, interpolate=True, min_likelihood=0.95):
"""Returns the x and y positions of a bodypart over time and the likelihood of it.
Args:
scorer (int, optional): [description]. Defaults to 0.
bodypart (int, optional): [description]. Defaults to 0.
framerate (int, optional): [description]. Defaults to 30.
scorer (int, optional): Scorer of dataset. Defaults to 0.
bodypart (int, optional): Bodypart of the animal. Can be seen in etrack.TrackingResults.bodyparts. Defaults to 0.
framerate (int, optional): Framerate of the video. Defaults to 25.
Raises:
ValueError: [description]
ValueError: [description]
ValueError: Scorer not existing in dataframe.
ValueError: Bodypart not existing in dataframe.
Returns:
time [np.array]: the time axis
x [np.array]: the x-position in m
y [np.array]: the y-position in m
l [np.array]: the likelihood of the position estimation
bp string: the body part
time [np.array]: The time axis.
x [np.array]: x-position in meter.
y [np.array]: y-position in meter.
l [np.array]: The likelihood of the position estimation. Originating from animal tracking done before.
bp string: The body part of the animal.
[type]: [description]
"""
@ -136,7 +162,16 @@ class TrackingResult(object):
y3 = np.interp(time, time2, y2)
return time, x3, y3, l, bp
def plot(self, scorer=0, bodypart=0, threshold=0.9, framerate=30):
def plot(self, scorer=0, bodypart=0, threshold=0.9, framerate=25):
"""Plot the position of a bodypart in the tank over time.
Args:
scorer (int, optional): Scorer of dataset. Defaults to 0.
bodypart (int, optional): Given bodypart to plot. Defaults to 0.
threshold (float, optional): Threshold below which the likelihood has to be. Defaults to 0.9.
framerate (int, optional): Framerate of the video. Defaults to 25.
"""
t, x, y, l, name = self.position_values(scorer=scorer, bodypart=bodypart, framerate=framerate)
plt.scatter(x[l > threshold], y[l > threshold], c=t[l > threshold], label=name)
plt.scatter(self.center_meter[0], self.center_meter[1], marker="*")
@ -148,31 +183,36 @@ class TrackingResult(object):
bar.set_label("time [s]")
plt.legend()
plt.show()
from IPython import embed
pass
if __name__ == '__main__':
from IPython import embed
filename = "2020.12.04_lepto48DLC_resnet50_boldnessDec11shuffle1_200000.h5"
path = "/mnt/movies/merle_verena/boldness/labeled_videos/day_4/"
tr = TrackingResult(path+filename)
time, x, y, l, bp = tr.position_values(bodypart=2)
filename = "/2022.01.12_3DLC_resnet50_efish_tracking3Mar21shuffle1_300000.h5"
path = "/home/efish/efish_tracking/efish_tracking3-Xaver-2022-03-21/videos"
tr = TrackingResult(path+filename) # usage of class with given file
time, x, y, l, bp = tr.position_values(bodypart=2) # time, x and y values, likelihood of position estimation, tracked bodypart
phi = tr.angle_to_center(0, True, False, 0.95)
thresh = 0.95
time2 = time[l>thresh]
x2 = x[l>thresh]
y2 = y[l>thresh]
x3 = np.interp(time, time2, x2)
y3 = np.interp(time, time2, y2)
time2 = time[l>thresh] # time values where likelihood of position estimation > threshold
x2 = x[l>thresh] # x values with likelihood > threshold
y2 = y[l>thresh] # y values -"-
x3 = np.interp(time, time2, x2) # x value interpolation at points where likelihood has been under threshold
y3 = np.interp(time, time2, y2) # y value -"-
fig, axes = plt.subplots(3,1, sharex=True)
axes[0].plot(time, x)
axes[0].plot(time, x)
axes[0].plot(time, x3)
axes[0].set_ylabel('x-position')
axes[1].plot(time, y)
axes[1].plot(time, y3)
axes[2].plot(time, l)
axes[1].set_ylabel('y-position')
axes[2].plot(time, l)
axes[2].set_xlabel('time [s]')
axes[2].set_ylabel('likelihood')
plt.show()
embed()