[tracking] tear apart the positions function, offer method to access raw pixel positions
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@ -140,13 +140,14 @@ class MarkerTask():
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if __name__ == "__main__":
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print("Hello Jan!")
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tank_task = MarkerTask("tank limits", ["bottom left corner", "top left corner", "top right corner", "bottom right corner"], "Mark tank corners")
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feeder_task = MarkerTask("Feeder positions", list(map(str, range(1, 2))), "Mark feeder positions")
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tasks = [tank_task, feeder_task]
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im = ImageMarker(tasks)
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# vid1 = "2020.12.11_lepto48DLC_resnet50_boldnessDec11shuffle1_200000_labeled.mp4"
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print(sys.argv[0])
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print (sys.argv[1])
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vid1 = sys.argv[1]
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marker_positions = im.mark_movie(vid1, 10)
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#feeder_task = MarkerTask("Feeder positions", list(map(str, range(1, 2))), "Mark feeder positions")
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#tasks = [tank_task, feeder_task]
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im = ImageMarker([tank_task])
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vid1 = "/data/personality/secondhome/fischies/lepto_03/position/lepto03_position_2021.06.07_60.mp4"
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# print(sys.argv[0])
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# print (sys.argv[1])
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# vid1 = sys.argv[1]
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marker_positions = im.mark_movie(vid1, 00)
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print(marker_positions)
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@ -20,7 +20,7 @@ def coordinate_transformation(position,x_0, y_0, x_factor, y_factor):
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return (x, y) #in m
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class TrackingResult(object):
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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:
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super().__init__()
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if not os.path.exists(results_file):
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@ -109,7 +109,11 @@ class TrackingResult(object):
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bp string: the body part
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[type]: [description]
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"""
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time, x, y, l, bp = self.pixel_positions(scorer, bodypart, framerate, interpolate, min_likelihood)
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x, y = self._to_meter(x, y)
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return time, x, y, l, bp
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def pixel_positions(self, scorer=0, bodypart=0, framerate=30, interpolate=True, min_likelihood=0.95):
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if isinstance(scorer, nb.Number):
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sc = self._scorer[scorer]
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elif isinstance(scorer, str) and scorer in self._scorer:
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@ -123,25 +127,37 @@ class TrackingResult(object):
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else:
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raise ValueError("Bodypart %s is not in dataframe!" % bodypart)
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x = self._data_frame[sc][bp]["x"] if "x" in self._positions else []
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x = (np.asarray(x) - self.x_0) * self.x_factor
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y = self._data_frame[sc][bp]["y"] if "y" in self._positions else []
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y = (np.asarray(y) - self.y_0) * self.y_factor
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l = self._data_frame[sc][bp]["likelihood"] if "likelihood" in self._positions else []
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time = np.arange(len(self._data_frame))/framerate
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time2 = time[l > min_likelihood]
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if len(l[l > min_likelihood]) < 100:
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print("%s has not datapoints with likelihood larger than %.2f" % (self._file_name, min_likelihood) )
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return None, None, None, None, None
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x = np.asarray(self._data_frame[sc][bp]["x"] if "x" in self._positions else [])
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y = np.asarray(self._data_frame[sc][bp]["y"] if "y" in self._positions else [])
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l = np.asarray(self._data_frame[sc][bp]["likelihood"] if "likelihood" in self._positions else [])
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time = np.arange(len(x))/framerate
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if interpolate:
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x, y = self.interpolate(time, x, y, l, min_likelihood)
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return time, x, y, l, bp
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def _to_meter(self, x, y):
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new_x = (np.asarray(x) - self.x_0) * self.x_factor
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new_y = (np.asarray(y) - self.y_0) * self.y_factor
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return new_x, new_y
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def _speed(self, t, x, y):
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speed = np.sqrt(np.diff(x)**2 + np.diff(y)**2) / np.diff(t)
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return speed
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def interpolate(self, t, x, y, l, min_likelihood=0.9):
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time2 = t[l > min_likelihood]
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if len(l[l > min_likelihood]) < 10:
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print("%s has less than 10 datapoints with likelihood larger than %.2f" % (self._file_name, min_likelihood) )
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return None, None
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x2 = x[l > min_likelihood]
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y2 = y[l > min_likelihood]
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x3 = np.interp(time, time2, x2)
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y3 = np.interp(time, time2, y2)
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return time, x3, y3, l, bp
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x3 = np.interp(t, time2, x2)
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y3 = np.interp(t, time2, y2)
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return x3, y3
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def plot(self, scorer=0, bodypart=0, threshold=0.9, framerate=30):
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t, x, y, l, name = self.position_values(scorer=scorer, bodypart=bodypart, framerate=framerate)
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t, x, y, l, name = self.position_values(scorer=scorer, bodypart=bodypart, framerate=framerate, min_likelihood=threshold)
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plt.scatter(x[l > threshold], y[l > threshold], c=t[l > threshold], label=name)
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plt.scatter(self.center_meter[0], self.center_meter[1], marker="*")
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plt.plot(x[l > threshold], y[l > threshold])
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@ -152,7 +168,6 @@ class TrackingResult(object):
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bar.set_label("time [s]")
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plt.legend()
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plt.show()
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from IPython import embed
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if __name__ == '__main__':
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