76390857f8
will need to test it, it seems to work better than I rememebr, if it keeps working that good I might juse make it the default
119 lines
5.8 KiB
Python
119 lines
5.8 KiB
Python
import numpy as np
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import time
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import math
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import emilianaFeature
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def angle(p1, p2):
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p1 = np.array(p1)
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p2 = np.array(p2)
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a = np.arctan2(*(p2 - p1)[::-1])
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return (a % (2 * np.pi))
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def rotate(origin, point, a):
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a = -a
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ox, oy = origin
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px, py = point
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qx = ox + math.cos(a) * (px - ox) - math.sin(a) * (py - oy)
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qy = oy + math.sin(a) * (px - ox) + math.cos(a) * (py - oy)
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return qx, qy
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class FeatureExtractor():
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def __init__(self, max_feature_updates=0):
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self.eye_l = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.eye_r = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_updown_l = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_updown_r = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_quirk_l = emilianaFeature.Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_quirk_r = emilianaFeature.Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_steepness_l = emilianaFeature.Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_steepness_r = emilianaFeature.Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.mouth_corner_updown_l = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.mouth_corner_updown_r = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.mouth_corner_inout_l = emilianaFeature.Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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self.mouth_corner_inout_r = emilianaFeature.Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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self.mouth_open = emilianaFeature.Feature(max_feature_updates=max_feature_updates)
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self.mouth_wide = emilianaFeature.Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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def align_points(self, a, b, pts):
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a = tuple(a)
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b = tuple(b)
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alpha = angle(a, b)
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alpha = np.rad2deg(alpha)
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if alpha >= 90:
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alpha = - (alpha - 180)
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if alpha <= -90:
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alpha = - (alpha + 180)
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alpha = np.deg2rad(alpha)
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aligned_pts = []
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for pt in pts:
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aligned_pts.append(np.array(rotate(a, pt, alpha)))
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return alpha, np.array(aligned_pts)
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def update(self, pts, full=True):
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features = {}
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now = time.perf_counter()
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norm_distance_x = np.mean([pts[0, 0] - pts[16, 0], pts[1, 0] - pts[15, 0]])
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norm_distance_y = np.mean([pts[27, 1] - pts[28, 1], pts[28, 1] - pts[29, 1], pts[29, 1] - pts[30, 1]])
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a1, f_pts = self.align_points(pts[42], pts[45], pts[[43, 44, 47, 46]])
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f = abs((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y)
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features["eye_l"] = self.eye_l.update(f, now)
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a2, f_pts = self.align_points(pts[36], pts[39], pts[[37, 38, 41, 40]])
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f = abs((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y)
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features["eye_r"] = self.eye_r.update(f, now)
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if full:
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a3, _ = self.align_points(pts[0], pts[16], [])
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a4, _ = self.align_points(pts[31], pts[35], [])
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norm_angle = np.mean(list(map(np.rad2deg, [a1, a2, a3, a4])))
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a, f_pts = self.align_points(pts[22], pts[26], pts[[22, 23, 24, 25, 26]])
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features["eyebrow_steepness_l"] = self.eyebrow_steepness_l.update(-np.rad2deg(a) - norm_angle, now)
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f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
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features["eyebrow_quirk_l"] = self.eyebrow_quirk_l.update(f, now)
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a, f_pts = self.align_points(pts[17], pts[21], pts[[17, 18, 19, 20, 21]])
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features["eyebrow_steepness_r"] = self.eyebrow_steepness_r.update(np.rad2deg(a) - norm_angle, now)
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f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
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features["eyebrow_quirk_r"] = self.eyebrow_quirk_r.update(f, now)
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else:
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features["eyebrow_steepness_l"] = 0.
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features["eyebrow_steepness_r"] = 0.
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features["eyebrow_quirk_l"] = 0.
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features["eyebrow_quirk_r"] = 0.
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f = (np.mean([pts[22, 1], pts[26, 1]]) - pts[27, 1]) / norm_distance_y
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features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f, now)
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f = (np.mean([pts[17, 1], pts[21, 1]]) - pts[27, 1]) / norm_distance_y
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features["eyebrow_updown_r"] = self.eyebrow_updown_r.update(f, now)
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upper_mouth_line = np.mean([pts[49, 1], pts[50, 1], pts[51, 1]])
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center_line = np.mean([pts[50, 0], pts[60, 0], pts[27, 0], pts[30, 0], pts[64, 0], pts[55, 0]])
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f = (upper_mouth_line - pts[62, 1]) / norm_distance_y
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features["mouth_corner_updown_l"] = self.mouth_corner_updown_l.update(f, now)
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if full:
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f = abs(center_line - pts[62, 0]) / norm_distance_x
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features["mouth_corner_inout_l"] = self.mouth_corner_inout_l.update(f, now)
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else:
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features["mouth_corner_inout_l"] = 0.
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f = (upper_mouth_line - pts[58, 1]) / norm_distance_y
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features["mouth_corner_updown_r"] = self.mouth_corner_updown_r.update(f, now)
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if full:
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f = abs(center_line - pts[58, 0]) / norm_distance_x
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features["mouth_corner_inout_r"] = self.mouth_corner_inout_r.update(f, now)
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else:
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features["mouth_corner_inout_r"] = 0.
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f = abs(np.mean(pts[[59,60,61], 1], axis=0) - np.mean(pts[[63,64,65], 1], axis=0)) / norm_distance_y
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features["mouth_open"] = self.mouth_open.update(f, now)
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f = abs(pts[58, 0] - pts[62, 0]) / norm_distance_x
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features["mouth_wide"] = self.mouth_wide.update(f, now)
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return features
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