def run(self, td):
currgp, currlp = self.ambimin.gp, self.ambimin.lp
n = 1
while n <= self.ambimin.mincnt:
amb, sen, trees = self.find_ambiguity(currgp, currlp)
assert amb
ambi_parse = AmbiParse.parse(self, trees)
gp = os.path.join(td, "%s.acc" % n)
lp = os.path.join(td, "%s.lex" % n)
print "currgp: %s, gp: %s " % (currgp, gp)
MiniUtils.write_cfg_lex(ambi_parse.min_cfg, gp, currlp, lp)
self.write_stat(gp)
currgp = gp
currlp = lp
n += 1
# run ambidexter on the minimised grammar
_gp = self.ambidxt.filter(currgp, str(self.ambimin.duration))
self.write_stat(_gp)
if _gp is not None:
return _gp, currlp
return currgp, currlp
def run_accent(self, sen, gp, lp, td):
""" build parser in td using gp+lp, and parse sentence sen."""
parser = Accent.compile(gp, lp)
out = Accent.run(parser, sen)
ambiparse = AmbiParse.parse(self, out)
_gp = tempfile.mktemp('.acc', dir=td)
_lp = tempfile.mktemp('.lex', dir=td)
MiniUtils.write_cfg_lex(ambiparse.min_cfg, _gp, lp, _lp)
return _gp, _lp
def run(self, td):
currgp, currlp = self.ambimin.gp, self.ambimin.lp
n = 1
while n <= self.ambimin.mincnt:
amb, sen, trees = self.find_ambiguity(currgp, currlp)
assert amb
ambi_parse = AmbiParse.parse(self, trees)
gp = os.path.join(td, "%s.acc" % n)
lp = os.path.join(td, "%s.lex" % n)
print "currgp: %s, gp: %s " % (currgp, gp)
MiniUtils.write_cfg_lex(ambi_parse.min_cfg, gp, currlp, lp)
self.write_stat(gp)
currgp = gp
currlp = lp
n += 1
# run ambidexter on the minimised grammar
fltr = '-%s' % self.ambimin.fltr
fltr_outg = '-%s' % self.ambimin.fltr_cfg_outfmt
opts = ['-h', fltr, fltr_outg]
t1 = time.time()
_gp = self.ambidxt.filter(currgp, str(self.ambimin.ambi_duration), opts)
t2 = time.time()
self.write_stat(_gp)
if _gp is None:
return currgp, currlp
print "=> filtered grammar : " , _gp
tp = self.fix_sym_tokens_bug(currgp, _gp, td)
# run ambidexter on the minimised grammar
opts = ['-pg', '-ik', '0']
t = self.ambimin.ambi_duration - (t2 - t1)
print "time remaining: " , t
ambisen, accsen = self.ambidxt.ambiguous(tp, currlp, str(t), opts)
print "accsen: " , accsen
if accsen is not None:
__gp, __lp = self.run_accent(accsen, tp, currlp, td)
self.write_stat(__gp)
return __gp, __lp
# AmbiDexter didn't find anything
self.write_stat(None)
return tp, currlp
def run(self, td):
gp, lp = self.ambimin.gp, self.ambimin.lp
amb, sen, trees = self.find_ambiguity(gp, lp)
assert amb
ambi_parse = AmbiParse.parse(self, trees)
# save the minimised cfg, lex to target files
_gp = os.path.join(td, "%s.acc" % 0)
_lp = os.path.join(td, "%s.lex" % 0)
print "gp: %s, _gp: %s " % (gp, _gp)
MiniUtils.write_cfg_lex(ambi_parse.min_cfg, _gp, lp, _lp)
# run ambidexter on the minimised grammar
sen, r = self.ambidexter(_gp)
if r != 0:
return _gp, _lp, None
return _gp, _gp, sen
def checkPositionDepthAndRGB(rgb, depth_transformed, depth):
imgConts, conts = MiniUtils.getContours(rgb, depth_transformed)
for i in conts:
x, y, w, h = i[3]
#print('RGB:%d DepthTrans: %d', np.size(rgb), np.size(depth_transformed))
print(depth_transformed[x][y])
'''
def prune_cfg(self, cfg, lex):
""" Given a grammar (gp), prune the unreachable rules.
The cfg is directly manipulated.
"""
unreachable = MiniUtils.unreachable_rules(cfg)
if len(unreachable) > 0:
print "=> unreachable: ", unreachable
_rules = []
for rule in cfg.rules:
if rule.name not in unreachable:
_rules.append(rule)
return CFG.CFG(lex, cfg.sym_tokens, _rules)
return cfg
def main():
k4a = PyK4A(
Config(
color_resolution=pyk4a.ColorResolution.RES_720P,
color_format=pyk4a.ImageFormat.COLOR_BGRA32,
depth_mode=pyk4a.DepthMode.NFOV_UNBINNED,
synchronized_images_only=True,
))
k4a.start()
# getters and setters directly get and set on device
k4a.whitebalance = 4500
assert k4a.whitebalance == 4500
k4a.whitebalance = 4510
assert k4a.whitebalance == 4510
windowID = '' #Give id to the windows to know which to destroy when not needed
BODY_PARTS = {
"Nose": 0,
"Neck": 1,
"RShoulder": 2,
"RElbow": 3,
"RWrist": 4,
"LShoulder": 5,
"LElbow": 6,
"LWrist": 7,
"RHip": 8,
"RKnee": 9,
"RAnkle": 10,
"LHip": 11,
"LKnee": 12,
"LAnkle": 13,
"REye": 14,
"LEye": 15,
"REar": 16,
"LEar": 17,
"Background": 18
}
POSE_PAIRS = [["Neck", "RShoulder"], ["Neck", "LShoulder"],
["RShoulder", "RElbow"], ["RElbow", "RWrist"],
["LShoulder", "LElbow"], ["LElbow", "LWrist"],
["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"],
["Neck", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"],
["Neck", "Nose"], ["Nose", "REye"], ["REye", "REar"],
["Nose", "LEye"], ["LEye", "LEar"]]
net = cv2.dnn.readNetFromTensorflow("graph_opt.pb") ##weights
inWidth = 654
inHeight = 368
thr = 0.2
while 1:
capture = k4a.get_capture()
if np.any(capture.color) and np.any(capture.depth):
checker, frame, length = bodyTracking(capture.color, net, inWidth,
inHeight, BODY_PARTS,
POSE_PAIRS, thr)
frame_depth = np.clip(capture.depth, 0, 2**10 - 1)
frame_depth >>= 2
num_fingers, img_draw = handEstimator(frame_depth)
imgConts, conts = MiniUtils.getContours(capture.color,
capture.transformed_depth,
filter=4)
cv2.imshow('Hand', img_draw)
cv2.imshow('rgb', capture.color)
'''if checker:
if length < 11:
if windowID != '01' and windowID != '':
cv2.destroyAllWindows()
cv2.imshow('Hand', img_draw)
windowID = '01'
else:
if windowID != '10' and windowID != '':
cv2.destroyAllWindows()
cv2.imshow('Body', frame)
windowID = '10'
else:
cv2.imshow('Conts', imgConts)
if windowID != '11' and windowID != '':
cv2.destroyAllWindows()
windowID = '11'
'''
key = cv2.waitKey(1)
if key == ord('q'):
cv2.destroyAllWindows()
break
k4a.stop()
def valid_cfg(self, cfg):
if MiniUtils.cyclic(cfg):
return False
return True
def main():
'''cv2.namedWindow("Trackbars")
cv2.createTrackbar("L-H", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("L-S", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("L-V", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("U-H", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("U-S", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("U-V", "Trackbars", 0, 255, nothing)'''
wP = 210
hP = 297
scale = 3
img = cv2.imread('colorimg.bmp')
depth = cv2.imread('depth.bmp')
#cv2.rectangle(img, (489, 269), (612, 344), (0, 255, 0), 2)
slot0 = [[489, 269], [612, 269], [489, 344], [612, 344]] #slot0
slot0_mP = [550.5, 306.5]
#cv2.rectangle(img, (645, 273), (780, 350), (0, 255, 0), 2)
slot1 = [[645, 273], [780, 273], [645, 350], [780, 350]]#slot1
slot1_mP = [712.5, 311.5]
#cv2.rectangle(img, (805, 271), (920, 346), (0, 255, 0), 2)
slot2= [[805, 271], [920, 271], [805, 346], [920, 346]]#slot2
slot2_mP = [862.5, 308.5]
#cv2.rectangle(img, (483, 377), (599, 470), (0, 255, 0), 2)
slot3 = [[483, 377], [599, 377], [483, 470], [599, 470]]#slot3
slot3_mP = [541, 423.5]
#cv2.rectangle(img, (645, 377), (792, 474), (0, 255, 0), 2)
slot4 = [[645, 377], [792, 377], [645, 474], [792, 474]]#slot4
slot4_mP = [718.5, 425.5]
#cv2.rectangle(img, (825, 382), (949, 475), (0, 255, 0), 2)
slot5 = [[825, 382], [949, 382], [825, 475], [949, 475]]#slot5
slot5_mP = [887, 428.5]
slots = [slot0, slot1, slot2, slot3, slot4, slot5]
midpointsSlots = [slot0_mP, slot1_mP, slot2_mP, slot3_mP, slot4_mP, slot5_mP]
imgConts, conts = MiniUtils.getContours(img, depth, filter=4, showCanny=True)
midpointsConts = MiniUtils.midPoints(conts)
'''frame_depth = np.clip(capture.depth, 0, 2 ** 10 - 1)
frame_depth >>= 2
num_fingers, img_draw = handEstimator(frame_depth)
if len(conts) > 0:
switcher = {
0: goalPosition[0] = [midpointsSlots[0], midpointsConts.pop(0)]
1: goalPosition[1] = [midpointsSlots[1], midpointsConts.pop(0)]
2: goalPosition[2] = [midpointsSlots[2], midpointsConts.pop(0)]
3: goalPosition[3] = [midpointsSlots[3], midpointsConts.pop(0)]
4: goalPosition[4] = [midpointsSlots[4], midpointsConts.pop(0)]
5: goalPosition[5] = [midpointsSlots[5], midpointsConts.pop(0)]
}
switcher.get(num_fingers, 'Error')
'''
#print(len(conts))
while 1:
if len(conts) != 0:
for obj in conts:
cv2.polylines(imgConts, [obj[2]], True, (0, 255, 0), 2)
#cv2.imshow('contss', imgConts)
for slot in slots:
cv2.rectangle(imgConts, (slot[0][0], slot[0][1]), (slot[3][0], slot[3][1]), (0, 255, 0), 2)
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key == ord('q'):
cv2.destroyAllWindows()
break
