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Optimized landmarks function for a 1-2ms speedup.

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Emiliana 2020-08-09 17:32:29 +02:00 • committed by GitHub
parent 395184af62
commit 6b876be623
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1 changed files with 27 additions and 15 deletions
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42
tracker.py
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@ -110,6 +110,10 @@ def logit(p, factor=16.0):
p = p/(1-p)
return float(np.log(p)) / float(factor)
def logit_arr(p, factor=16.0):
p = np.clip(p, 0.0000001, 0.9999999)
return np.log(p / (1 - p)) / float(factor)
def matrix_to_quaternion(m):
t = 0.0
q = [0.0, 0.0, 0, 0.0]
@ -613,8 +617,19 @@ class Tracker():
self.scan_every = scan_every
self.bbox_growth = bbox_growth
self.silent = silent
self.res = 224.
if model_type < 0:
self.res = 56.
self.res_i = int(self.res)
self.out_res = 27.
if model_type < 0:
self.out_res = 13.
self.out_res_i = int(self.out_res) + 1
self.logit_factor = 16.
if model_type < 0:
self.logit_factor = 8.
self.no_gaze = no_gaze
self.debug_gaze = False
self.max_feature_updates = max_feature_updates
@ -651,22 +666,19 @@ class Tracker():
def landmarks(self, tensor, crop_info):
crop_x1, crop_y1, scale_x, scale_y, _ = crop_info
avg_conf = 0
lms = []
res = self.res - 1
for i in range(0, 66):
m = int(tensor[i].argmax())
x = m // 28
y = m % 28
conf = float(tensor[i][x,y])
avg_conf = avg_conf + conf
off_x = res * ((1. * logit(tensor[66 + i][x, y])) - 0.0)
off_y = res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0)
off_x = math.floor(off_x + 0.5)
off_y = math.floor(off_y + 0.5)
lm_x = crop_y1 + scale_y * (res * (float(x) / 27.) + off_x)
lm_y = crop_x1 + scale_x * (res * (float(y) / 27.) + off_y)
lms.append((lm_x,lm_y,conf))
avg_conf = avg_conf / 66.
t_main = tensor[0:66].reshape((66,self.out_res_i * self.out_res_i))
t_m = t_main.argmax(1)
indices = np.expand_dims(t_m, 1)
t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
t_off_x = np.take_along_axis(tensor[66:132].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
t_off_y = np.take_along_axis(tensor[132:198].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
t_off_x = np.floor(res * logit_arr(t_off_x, self.logit_factor) + 0.5)
t_off_y = np.floor(res * logit_arr(t_off_y, self.logit_factor) + 0.5)
t_x = crop_y1 + scale_y * (res * np.floor(t_m / self.out_res_i) / self.out_res + t_off_x)
t_y = crop_x1 + scale_x * (res * np.floor(np.mod(t_m, self.out_res_i)) / self.out_res + t_off_y)
avg_conf = np.average(t_conf)
lms = np.stack([t_x, t_y, t_conf], 1)
return (avg_conf, np.array(lms))
def estimate_depth(self, face_info):