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test: add some filtering
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@ -127,6 +127,8 @@ class LEAP_C(object):
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min_cutoff = 0.1
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min_cutoff = 0.1
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beta = 15.0
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beta = 15.0
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self.one_euro_filter = OneEuroFilter(np.random.rand(12, 2), min_cutoff=min_cutoff, beta=beta)
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self.one_euro_filter = OneEuroFilter(np.random.rand(12, 2), min_cutoff=min_cutoff, beta=beta)
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self.one_euro_filter_float = OneEuroFilter(np.random.rand(1, 2), min_cutoff=5, beta=0.007)
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self.dmax = 0
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self.dmax = 0
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self.dmin = 0
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self.dmin = 0
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self.openlist = []
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self.openlist = []
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@ -239,8 +241,9 @@ class LEAP_C(object):
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per = 1 - per
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per = 1 - per
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per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
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per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
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per = min(per, 1.0) # clamp to 1.0 max
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per = min(per, 1.0) # clamp to 1.0 max
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per = max(per, 0.0) # clamp to 1.0 max
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print("new: ", per, "vs old: ", oldper)
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# print("new: ", per, "vs old: ", oldper)
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except:
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except:
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per = 0.8
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per = 0.8
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@ -250,7 +253,12 @@ class LEAP_C(object):
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y = pre_landmark[6][1]
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y = pre_landmark[6][1]
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self.last_lid = per
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self.last_lid = per
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calib_array = np.array([per, per]).reshape(1, 2)
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per = self.one_euro_filter_float(calib_array)
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per = per[0][0]
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print(per)
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if per <= 0.2: # TODO: EXPOSE AS SETTING
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if per <= 0.2: # TODO: EXPOSE AS SETTING
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per == 0.0
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per == 0.0
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# this should be tuned, i could make this auto calib based on min from a list of per values.
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# this should be tuned, i could make this auto calib based on min from a list of per values.
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