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DeepFaceLive/xlib/face/FLandmarks2D.py
iperov ae8a1e0ff4 added Face Animator module
2022-05-13 12:26:20 +04:00

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from typing import Tuple
import cv2
import numpy as np
from .. import math as lib_math
from ..math import Affine2DMat, Affine2DUniMat
from .ELandmarks2D import ELandmarks2D
from .FRect import FRect
from .IState import IState
class FLandmarks2D(IState):
def __init__(self):
"""
Describes 2D face landmarks in uniform float coordinates
"""
self._type : ELandmarks2D = None
self._ulmrks : np.ndarray = None
def restore_state(self, state : dict):
self._type = IState._restore_enum(ELandmarks2D, state.get('_type', None))
self._ulmrks = IState._restore_np_array(state.get('_ulmrks', None))
def dump_state(self) -> dict:
return {'_type' : IState._dump_enum(self._type),
'_ulmrks' : IState._dump_np_array(self._ulmrks),
}
@staticmethod
def create( type : ELandmarks2D, ulmrks : np.ndarray):
"""
ulmrks np.ndarray (*,2|3)
"""
if not isinstance(type, ELandmarks2D):
raise ValueError('type must be ELandmarks2D')
ulmrks = np.float32(ulmrks)
if len(ulmrks.shape) != 2:
raise ValueError('ulmrks shape must have rank 2')
if ulmrks.shape[1] != 2:
raise ValueError('ulmrks dim must be == 2')
ulmrks_count = ulmrks.shape[0]
if type == ELandmarks2D.L5:
if ulmrks_count != 5:
raise ValueError('ulmrks_count must be == 5')
elif type == ELandmarks2D.L68:
if ulmrks_count != 68:
raise ValueError('ulmrks_count must be == 68')
elif type == ELandmarks2D.L106:
if ulmrks_count != 106:
raise ValueError('ulmrks_count must be == 106')
elif type == ELandmarks2D.L468:
if ulmrks_count != 468:
raise ValueError('ulmrks_count must be == 468')
face_ulmrks = FLandmarks2D()
face_ulmrks._type = type
face_ulmrks._ulmrks = ulmrks
return face_ulmrks
def get_type(self) -> ELandmarks2D: return self._type
def get_count(self) -> int: return self._ulmrks.shape[0]
def as_numpy(self, w_h = None):
"""
get landmarks as np.ndarray
w_h(None) provide (w,h) to scale uniform landmarks to exact size
"""
ulmrks = self._ulmrks.copy()
if w_h is not None:
ulmrks *= w_h
return ulmrks
def transform(self, mat, invert=False) -> 'FLandmarks2D':
"""
Tranforms FLandmarks2D using affine mat and returns new FLandmarks2D()
mat : np.ndarray
"""
if not isinstance(mat, np.ndarray):
raise ValueError('mat must be an instance of np.ndarray')
if invert:
mat = cv2.invertAffineTransform (mat)
ulmrks = self._ulmrks.copy()
ulmrks = np.expand_dims(ulmrks, axis=1)
ulmrks = cv2.transform(ulmrks, mat, ulmrks.shape).squeeze()
return FLandmarks2D.create(type=self._type, ulmrks=ulmrks)
def get_FRect(self, coverage=1.6) -> FRect:
"""
create FRect from landmarks with given coverage
"""
_, uni_mat = self.calc_cut( (1,1), coverage, 1, exclude_moving_parts=False)
xlt, xlb, xrb, xrt = uni_mat.invert().transform_points([[0,0], [0,1], [1,1], [1,0]])
l = min(xlt[0], xlb[0])
t = min(xlt[1], xrt[1])
r = max(xrt[0], xrb[0])
b = max(xlb[1], xrb[1])
return FRect.from_ltrb( (l,t,r,b) )
def calc_cut(self, h_w, coverage : float, output_size : int,
exclude_moving_parts : bool = False,
head_yaw : float = None,
x_offset : float = 0, y_offset : float = 0,
freeze_z_rotation = False):
"""
Calculates affine mat for face cut.
returns
mat, matrix to transform img space to face_image space
uni_mat matrix to transform uniform img space to uniform face_image space
"""
h,w = h_w
type = self._type
lmrks = (self._ulmrks * (w,h)).astype(np.float32)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
if type == ELandmarks2D.L106:
type = ELandmarks2D.L68
lmrks = lmrks[ lmrks_106_to_68_mean_pairs ]
lmrks = lmrks.reshape( (68,2,2)).mean(1)
if type == ELandmarks2D.L68:
mat = Affine2DMat.umeyama( np.concatenate ([ lmrks[17:36], lmrks[36:37], lmrks[39:40], lmrks[42:43], lmrks[45:46], lmrks[48:49], lmrks[54:55] ]), uni_landmarks_68)
elif type == ELandmarks2D.L468:
src_lmrks = lmrks
dst_lmrks = uni_landmarks_468
if exclude_moving_parts:
src_lmrks = np.delete(src_lmrks, landmarks_468_moving_parts_indexes, 0)
dst_lmrks = np.delete(dst_lmrks, landmarks_468_moving_parts_indexes, 0)
mat = Affine2DMat.umeyama(src_lmrks, dst_lmrks)
else:
raise NotImplementedError()
# get corner points in global space
g_p = mat.invert().transform_points ( [(0,0),(1,0),(1,1),(0,1),(0.5,0.5) ] )
g_c = g_p[4]
# calc diagonal vectors between corners in global space
tb_diag_vec = lib_math.segment_to_vector(g_p[0], g_p[2]).astype(np.float32)
bt_diag_vec = lib_math.segment_to_vector(g_p[3], g_p[1]).astype(np.float32)
mod = lib_math.segment_length(g_p[0],g_p[4])*coverage
if head_yaw is not None:
# Damp near zero
x_offset += -(head_yaw * np.abs(np.tanh(head_yaw*2)) ) * 0.5
# adjust vertical offset to cover more forehead
h_vec = (g_p[1]-g_p[0]).astype(np.float32)
v_vec = (g_p[3]-g_p[0]).astype(np.float32)
g_c += h_vec*x_offset + v_vec*y_offset
if not freeze_z_rotation:
l_t = np.array([g_c - tb_diag_vec*mod,
g_c + bt_diag_vec*mod,
g_c + tb_diag_vec*mod], np.float32 )
else:
# remove_align - face will be centered in the frame but not aligned
l_t = np.array([g_c - tb_diag_vec*mod,
g_c + bt_diag_vec*mod,
g_c + tb_diag_vec*mod,
g_c - bt_diag_vec*mod], np.float32 )
# get area of face square in global space
area = 0.5*np.abs(np.dot(l_t[:,0],np.roll(l_t[:,1],1))-np.dot(l_t[:,1],np.roll(l_t[:,0],1)))
# calc side of square
side = np.float32(np.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array([g_c + [-side,-side],
g_c + [ side,-side],
g_c + [ side, side]], np.float32 )
# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
mat = Affine2DMat.from_3_pairs ( l_t, np.float32(( (0,0),(output_size,0),(output_size,output_size) )))
uni_mat = Affine2DUniMat.from_3_pairs ( (l_t / (w,h) ).astype(np.float32), np.float32(( (0,0),(1,0),(1,1) )) )
return mat, uni_mat
def cut(self, img : np.ndarray,
coverage : float,
output_size : int,
exclude_moving_parts : bool = False,
head_yaw : float = None,
x_offset : float = 0,
y_offset : float = 0,
freeze_z_rotation : bool = False) -> Tuple[np.ndarray, Affine2DUniMat]:
"""
Cut the face to square of output_size from img using landmarks with given parameters
arguments
img np.ndarray
coverage float
output_size int
exclude_moving_parts(False) exclude moving parts of the face, such as eyebrows and jaw
head_yaw(None) float fit the head in center using provided yaw radian value.
x_offset
y_offset float uniform x/y offset
returns face_image,
uni_mat uniform affine matrix to transform uniform img space to uniform face_image space
"""
h,w = img.shape[0:2]
mat, uni_mat = self.calc_cut( (h,w), coverage, output_size, exclude_moving_parts, head_yaw=head_yaw, x_offset=x_offset, y_offset=y_offset, freeze_z_rotation=freeze_z_rotation)
face_image = cv2.warpAffine(img, mat, (output_size, output_size), cv2.INTER_CUBIC )
return face_image, uni_mat
def draw(self, img : np.ndarray, color, radius=1):
"""
draw landmarks on the img scaled by img.wh
color tuple of values should be the same as img color channels
"""
h,w = img.shape[0:2]
pts = self.as_numpy(w_h=(w,h)).astype(np.int32)
for x, y in pts:
cv2.circle(img, (x, y), radius, color, lineType=cv2.LINE_AA)
def get_convexhull_mask(self, h_w, color=(1,), dtype=np.float32) -> np.ndarray:
"""
"""
h, w = h_w
ch = len(color)
lmrks = (self._ulmrks * h_w).astype(np.int32)
mask = np.zeros( (h,w,ch), dtype=dtype)
cv2.fillConvexPoly( mask, cv2.convexHull(lmrks), color)
return mask
lmrks_106_to_68_mean_pairs = [1,9, 10,11, 12,13, 14,15, 16,2, 3,4, 5,6, 7,8, 0,0, 24,23, 22,21, 20,19, 18,32, 31,30, 29,28, 27,26,25,17,
43,43, 48,44, 49,45, 51,47, 50,46,
102,97, 103,98, 104,99, 105,100, 101,101,
72,72, 73,73, 74,74, 86,86, 77,78, 78,79, 80,80, 85,84, 84,83,
35,35, 41,40, 40,42, 39,39, 37,33, 33,36,
89,89, 95,94, 94,96, 93,93, 91,87, 87,90,
52,52, 64,64, 63,63, 71,71, 67,67, 68,68, 61,61, 58,58, 59,59, 53,53, 56,56, 55,55, 65,65, 66,66, 62,62, 70,70, 69,69, 57,57, 60,60, 54,54]
uni_landmarks_68 = np.float32([
[ 0.000213256, 0.106454 ], #17
[ 0.0752622, 0.038915 ], #18
[ 0.18113, 0.0187482 ], #19
[ 0.29077, 0.0344891 ], #20
[ 0.393397, 0.0773906 ], #21
[ 0.586856, 0.0773906 ], #22
[ 0.689483, 0.0344891 ], #23
[ 0.799124, 0.0187482 ], #24
[ 0.904991, 0.038915 ], #25
[ 0.98004, 0.106454 ], #26
[ 0.490127, 0.203352 ], #27
[ 0.490127, 0.307009 ], #28
[ 0.490127, 0.409805 ], #29
[ 0.490127, 0.515625 ], #30
[ 0.36688, 0.587326 ], #31
[ 0.426036, 0.609345 ], #32
[ 0.490127, 0.628106 ], #33
[ 0.554217, 0.609345 ], #34
[ 0.613373, 0.587326 ], #35
[ 0.121737, 0.216423 ], #36
#[ 0.187122, 0.178758 ], #37
#[ 0.265825, 0.179852 ], #38
[ 0.334606, 0.231733 ], #39
#[ 0.260918, 0.245099 ], #40
#[ 0.182743, 0.244077 ], #41
[ 0.645647, 0.231733 ], #42
#[ 0.714428, 0.179852 ], #43
#[ 0.793132, 0.178758 ], #44
[ 0.858516, 0.216423 ], #45
#[ 0.79751, 0.244077 ], #46
#[ 0.719335, 0.245099 ], #47
[ 0.254149, 0.780233 ], #48
[ 0.726104, 0.780233 ], #54
])
uni_landmarks_468 = np.float32([
[0.499976992607117, 0.652534008026123],
[0.500025987625122, 0.547487020492554],
[0.499974012374878, 0.602371990680695],
[0.482113003730774, 0.471979022026062],
[0.500150978565216, 0.527155995368958],
[0.499909996986389, 0.498252987861633],
[0.499523013830185, 0.40106201171875],
[0.289712011814117, 0.380764007568359],
[0.499954998493195, 0.312398016452789],
[0.499987006187439, 0.269918978214264],
[0.500023007392883, 0.107050001621246],
[0.500023007392883, 0.666234016418457],
[0.5000159740448, 0.679224014282227],
[0.500023007392883, 0.692348003387451],
[0.499976992607117, 0.695277988910675],
[0.499976992607117, 0.70593398809433],
[0.499976992607117, 0.719385027885437],
[0.499976992607117, 0.737019002437592],
[0.499967992305756, 0.781370997428894],
[0.499816000461578, 0.562981009483337],
[0.473773002624512, 0.573909997940063],
[0.104906998574734, 0.254140973091125],
[0.365929991006851, 0.409575998783112],
[0.338757991790771, 0.41302502155304],
[0.311120003461838, 0.409460008144379],
[0.274657994508743, 0.389131009578705],
[0.393361985683441, 0.403706014156342],
[0.345234006643295, 0.344011008739471],
[0.370094001293182, 0.346076011657715],
[0.319321990013123, 0.347265005111694],
[0.297903001308441, 0.353591024875641],
[0.24779200553894, 0.410809993743896],
[0.396889001131058, 0.842755019664764],
[0.280097991228104, 0.375599980354309],
[0.106310002505779, 0.399955987930298],
[0.2099249958992, 0.391353011131287],
[0.355807989835739, 0.534406006336212],
[0.471751004457474, 0.65040397644043],
[0.474155008792877, 0.680191993713379],
[0.439785003662109, 0.657229006290436],
[0.414617002010345, 0.66654098033905],
[0.450374007225037, 0.680860996246338],
[0.428770989179611, 0.682690978050232],
[0.374971002340317, 0.727805018424988],
[0.486716985702515, 0.547628998756409],
[0.485300987958908, 0.527395009994507],
[0.257764995098114, 0.314490020275116],
[0.401223003864288, 0.455172002315521],
[0.429818987846375, 0.548614978790283],
[0.421351999044418, 0.533740997314453],
[0.276895999908447, 0.532056987285614],
[0.483370006084442, 0.499586999416351],
[0.33721199631691, 0.282882988452911],
[0.296391993761063, 0.293242990970612],
[0.169294998049736, 0.193813979625702],
[0.447580009698868, 0.302609980106354],
[0.392390012741089, 0.353887975215912],
[0.354490011930466, 0.696784019470215],
[0.067304998636246, 0.730105042457581],
[0.442739009857178, 0.572826027870178],
[0.457098007202148, 0.584792017936707],
[0.381974011659622, 0.694710969924927],
[0.392388999462128, 0.694203019142151],
[0.277076005935669, 0.271932005882263],
[0.422551989555359, 0.563233017921448],
[0.385919004678726, 0.281364023685455],
[0.383103013038635, 0.255840003490448],
[0.331431001424789, 0.119714021682739],
[0.229923993349075, 0.232002973556519],
[0.364500999450684, 0.189113974571228],
[0.229622006416321, 0.299540996551514],
[0.173287004232407, 0.278747975826263],
[0.472878992557526, 0.666198015213013],
[0.446828007698059, 0.668527007102966],
[0.422762006521225, 0.673889994621277],
[0.445307999849319, 0.580065965652466],
[0.388103008270264, 0.693961024284363],
[0.403039008378983, 0.706539988517761],
[0.403629004955292, 0.693953037261963],
[0.460041999816895, 0.557139039039612],
[0.431158006191254, 0.692366003990173],
[0.452181994915009, 0.692366003990173],
[0.475387006998062, 0.692366003990173],
[0.465828001499176, 0.779190003871918],
[0.472328990697861, 0.736225962638855],
[0.473087012767792, 0.717857003211975],
[0.473122000694275, 0.704625964164734],
[0.473033010959625, 0.695277988910675],
[0.427942007780075, 0.695277988910675],
[0.426479011774063, 0.703539967536926],
[0.423162013292313, 0.711845993995667],
[0.4183090031147, 0.720062971115112],
[0.390094995498657, 0.639572978019714],
[0.013953999616206, 0.560034036636353],
[0.499913990497589, 0.58014702796936],
[0.413199990987778, 0.69539999961853],
[0.409626007080078, 0.701822996139526],
[0.468080013990402, 0.601534962654114],
[0.422728985548019, 0.585985004901886],
[0.463079988956451, 0.593783974647522],
[0.37211999297142, 0.47341400384903],
[0.334562003612518, 0.496073007583618],
[0.411671012639999, 0.546965003013611],
[0.242175996303558, 0.14767599105835],
[0.290776997804642, 0.201445996761322],
[0.327338010072708, 0.256527006626129],
[0.399509996175766, 0.748921036720276],
[0.441727995872498, 0.261676013469696],
[0.429764986038208, 0.187834024429321],
[0.412198007106781, 0.108901023864746],
[0.288955003023148, 0.398952007293701],
[0.218936994671822, 0.435410976409912],
[0.41278201341629, 0.398970007896423],
[0.257135003805161, 0.355440020561218],
[0.427684992551804, 0.437960982322693],
[0.448339998722076, 0.536936044692993],
[0.178560003638268, 0.45755398273468],
[0.247308000922203, 0.457193970680237],
[0.286267012357712, 0.467674970626831],
[0.332827985286713, 0.460712015628815],
[0.368755996227264, 0.447206974029541],
[0.398963987827301, 0.432654976844788],
[0.476410001516342, 0.405806005001068],
[0.189241006970406, 0.523923993110657],
[0.228962004184723, 0.348950982093811],
[0.490725994110107, 0.562400996685028],
[0.404670000076294, 0.485132992267609],
[0.019469000399113, 0.401564002037048],
[0.426243007183075, 0.420431017875671],
[0.396993011236191, 0.548797011375427],
[0.266469985246658, 0.376977026462555],
[0.439121007919312, 0.51895797252655],
[0.032313998788595, 0.644356966018677],
[0.419054001569748, 0.387154996395111],
[0.462783008813858, 0.505746960639954],
[0.238978996872902, 0.779744982719421],
[0.198220998048782, 0.831938028335571],
[0.107550002634525, 0.540755033493042],
[0.183610007166862, 0.740257024765015],
[0.134409993886948, 0.333683013916016],
[0.385764002799988, 0.883153975009918],
[0.490967005491257, 0.579378008842468],
[0.382384985685349, 0.508572995662689],
[0.174399003386497, 0.397670984268188],
[0.318785011768341, 0.39623498916626],
[0.343364000320435, 0.400596976280212],
[0.396100014448166, 0.710216999053955],
[0.187885001301765, 0.588537991046906],
[0.430987000465393, 0.944064974784851],
[0.318993002176285, 0.898285031318665],
[0.266247987747192, 0.869701027870178],
[0.500023007392883, 0.190576016902924],
[0.499976992607117, 0.954452991485596],
[0.366169989109039, 0.398822009563446],
[0.393207013607025, 0.39553701877594],
[0.410373002290726, 0.391080021858215],
[0.194993004202843, 0.342101991176605],
[0.388664990663528, 0.362284004688263],
[0.365961998701096, 0.355970978736877],
[0.343364000320435, 0.355356991291046],
[0.318785011768341, 0.35834002494812],
[0.301414996385574, 0.363156020641327],
[0.058132998645306, 0.319076001644135],
[0.301414996385574, 0.387449026107788],
[0.499987989664078, 0.618434011936188],
[0.415838003158569, 0.624195992946625],
[0.445681989192963, 0.566076993942261],
[0.465844005346298, 0.620640993118286],
[0.49992299079895, 0.351523995399475],
[0.288718998432159, 0.819945991039276],
[0.335278987884521, 0.852819979190826],
[0.440512001514435, 0.902418971061707],
[0.128294005990028, 0.791940987110138],
[0.408771991729736, 0.373893976211548],
[0.455606997013092, 0.451801002025604],
[0.499877005815506, 0.908990025520325],
[0.375436991453171, 0.924192011356354],
[0.11421000212431, 0.615022003650665],
[0.448662012815475, 0.695277988910675],
[0.4480200111866, 0.704632043838501],
[0.447111994028091, 0.715808033943176],
[0.444831997156143, 0.730794012546539],
[0.430011987686157, 0.766808986663818],
[0.406787008047104, 0.685672998428345],
[0.400738000869751, 0.681069016456604],
[0.392399996519089, 0.677703022956848],
[0.367855995893478, 0.663918972015381],
[0.247923001646996, 0.601333022117615],
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landmarks_468_moving_parts_indexes = [0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 46, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 95, 96, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 118, 124, 130, 132, 133, 135, 136, 138, 139, 140, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 168, 169, 170, 171, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 189, 190, 191, 192, 193, 194, 199, 200, 201, 202, 204, 208, 210, 211, 212, 213, 214, 215, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 243, 244, 245, 246, 247, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 276, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 324, 325, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 346, 347, 353, 359, 361, 362, 364, 365, 367, 368, 369, 372, 373, 374, 375, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 413, 414, 415, 416, 417, 418, 421, 422, 424, 428, 430, 431, 432, 433, 434, 435, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 463, 464, 465, 466, 467]
uni_landmarks_468 = np.array(
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[ 0.8855709 , 0.20027623]], dtype=np.float32)
# import numpy as np
# import cv2
# pts = uni_landmarks_468
# res = 900
# pts -= pts.min(axis=0)
# pts /= pts.max(axis=0)
# pts *= [res, res]
# pts = pts.astype(np.int)
# img = np.zeros( (res,res,3), np.uint8 )
# wnd_name = 'asd'
# selected = [False]*len(pts)
# sel = [0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 46, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 95, 96, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 118, 124, 130, 132, 133, 135, 136, 138, 139, 140, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 168, 169, 170, 171, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 189, 190, 191, 192, 193, 194, 199, 200, 201, 202, 204, 208, 210, 211, 212, 213, 214, 215, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 243, 244, 245, 246, 247, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 276, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 324, 325, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 346, 347, 353, 359, 361, 362, 364, 365, 367, 368, 369, 372, 373, 374, 375, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 413, 414, 415, 416, 417, 418, 421, 422, 424, 428, 430, 431, 432, 433, 434, 435, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 463, 464, 465, 466, 467]
# print(len(sel))
# for i in sel:
# selected[i] = True
# select_holding = False
# unselect_holding = False
# def onMouse(event, x, y, flags, _):
# global select_holding
# global unselect_holding
# if event == cv2.EVENT_LBUTTONDOWN:
# select_holding = True
# elif event == cv2.EVENT_LBUTTONUP:
# select_holding = False
# elif event == cv2.EVENT_RBUTTONDOWN:
# unselect_holding = True
# elif event == cv2.EVENT_RBUTTONUP:
# unselect_holding = False
# elif event == cv2.EVENT_MBUTTONDOWN:
# print([ i for i, x in enumerate(selected) if x == True ])
# pt_idx = np.argsort( np.linalg.norm(pts - [x,y], axis=1) )[0]
# if select_holding:
# selected[pt_idx] = True
# if unselect_holding:
# selected[pt_idx] = False
# cv2.namedWindow(wnd_name)
# cv2.setMouseCallback(wnd_name, onMouse)
# while True:
# for pt_idx, (x,y) in enumerate(pts):
# if selected[pt_idx]:
# color = (255,0,0)
# else:
# color = (255,255,255)
# cv2.circle(img, (x,y), 1, color, 1 )
# cv2.imshow(wnd_name,img)
# cv2.waitKey(5)
# import multiprocessing
# import threading
# import time
# def proc1(ev = multiprocessing.Event()):
# while True:
# ev.wait(timeout=0.001)
# # def proc2(obj : ClassWithEvent):
# # print('before wait')
# # obj.ev.wait(timeout=0.005)
# # print('after wait')
# if __name__ == '__main__':
# multiprocessing.set_start_method('spawn', force=True)
# ev = multiprocessing.Event()
# ev.set()
# p = multiprocessing.Process(target=proc1, args=(ev,), daemon=True)
# threading.Thread(target=lambda: p.start(), daemon=True).start()
# time.sleep(1.0)
# p.terminate()
# p.join()
# del p
# # p = multiprocessing.Process(target=proc2, args=(obj,), daemon=True)
# # threading.Thread(target=lambda: p.start(), daemon=True).start()
# # time.sleep(1.0)
# import code
# code.interact(local=dict(globals(), **locals()))
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