def output_move_options(self):
for curr_dir in self.available_passageways:
print("There is a passage leading " +
str(direction(curr_dir).name))
if (self.stairway):
print("There is a stairway going up...")
def gen_path (start, end, TRANS):
prob = [0] * 8000
prev = [0] * 8000
prob[start] = 1.0
prev[start] = 0
change = True
while change:
change = False
for tran, p in TRANS.items ():
d, a, b = tran
if direction (center[a], center[end]) != d:
#print direction (center[a], center[end]), d
continue
if prob[b] < prob[a] * p:
prob[b] = prob[a] * p
prev[b] = a
change = True
pr = prev[end]
path = [end]
while pr != start and pr != 0:
path.append (pr)
pr = prev[pr]
path.append (start)
path.append (prob[end])
path.reverse ()
return path
def gen_path(start, end, TRANS):
prob = [0] * 8000
prev = [0] * 8000
markov = {}
prob[start] = 1.0
prev[start] = 0
change = True
for tran, p in TRANS.items():
d, a, b = tran
if direction(center[a], center[end]) == d:
markov[a, b] = p
while change:
change = False
for tran, p in markov.items():
a, b = tran
if prob[b] < prob[a] * p:
prob[b] = prob[a] * p
prev[b] = a
change = True
pr = prev[end]
path = [end]
while pr != start and pr != 0:
path.append(pr)
pr = prev[pr]
path.append(start)
path.append(prob[end])
path.reverse()
return path
def cut_number(img):
r, g, b = img.split()
gray = img.convert('L')
plt.figure('cut')
plt.subplot(1, 2, 1), plt.title('origin')
plt.imshow(gray)
R = np.asarray(r)
G = np.asarray(g)
B = np.asarray(b)
# print R[27][1562]
# print G[27][1562]
# print B[27][1562]
# box =(1020,0,1030,50) # 门
# box = (800,830,1230,1066) # 地板
# box1 = (1348,121,1369,160) # 第一方向
# box2 = (1198,80,1216,110) # 第二方向
if direction(img) == 1:
box = (1348, 121, 1369, 160)
else:
box = (1198, 80, 1216, 110)
roi = gray.crop(box)
# print '2'
roi.save("./roi.jpg")
plt.subplot(1, 2, 2), plt.title('roi')
plt.imshow(roi), plt.axis('off')
plt.show()
def static_markov ():
reader = csv.reader (sys.stdin)
markov = [{}] * 8
for row in reader:
end = (int (row[4][:3]), int (row[4][3:]))
row = [int (p) for p in row]
for i in range (5, len (row) - 1):
d = direction (center[row[i]], end)
if row[i] not in markov[d]:
markov[d][row[i]] = Counter ()
markov[d][row[i]][row[i+1]] += 1
return markov
def randomize(self): while True: self.opt_annotations = opt_annotations() self.opt_annotations.randomize() self.direction = direction() self.direction.randomize() self.type_ref = type_ref() self.type_ref.randomize() self.name = name() self.name.randomize() if not self.filter(): break
def static_markov():
reader = csv.reader(sys.stdin)
markov = [{}] * 8
for row in reader:
end = (int(row[4][:3]), int(row[4][3:]))
row = [int(p) for p in row]
for i in range(5, len(row) - 1):
d = direction(center[row[i]], end)
if row[i] not in markov[d]:
markov[d][row[i]] = Counter()
markov[d][row[i]][row[i + 1]] += 1
return markov
def gen_path(start, end, TRANS):
N = 5000
prob = [0] * N
prev = [0] * N
Q = set()
adj = [[]] * N
prob[start] = 1.0
prev[start] = 0
for tran, p in TRANS.items():
d, a, b = tran
if direction(center[a], center[end]) == d:
Q.add(a)
Q.add(b)
adj[a].append((b, p))
#change = True
while len(Q) > 0:
#change = False
maxv = 0
maxp = 0
for v in Q:
if prob[v] >= maxp:
maxv = v
maxp = p
Q.remove(maxv)
for n, p in adj[maxv]:
if prob[maxv] * p >= prob[n]:
prob[n] = prob[maxv] * p
prev[n] = maxv
#change = True
path = [end]
pr = prev[end]
print pr
while pr != start:
print pr
if pr == 0:
print >> sys.stderr, 'zero'
break
path.append(pr)
pr = prev[n]
path.append(start)
path.append(prob[end])
path.reverse()
return path
def findExogenous(self, exclude=[]):
rvList = self.rvList
rvList.sort()
accum = {}
for v in rvList:
if v not in exclude:
accum[v] = 0.0
numVars = len(rvList)
for i in range(numVars):
x = rvList[i]
if x in exclude:
continue
for j in range(i + 1, numVars):
y = rvList[j]
if x == y or y in exclude:
continue
R = direction.direction(self.data[x], self.data[y])
if R > 0:
leastCausal = y
else:
leastCausal = x
accum[leastCausal] += abs(R)
scores = [(accum[key], key) for key in accum.keys()]
scores.sort()
exos = []
for tup in scores:
var = tup[1]
if not exos:
exos.append(var)
else:
isExo = True
for exo in exos:
pval = independence.test(self.iProb, [var], [exo])
#print('ind ', var, '-', exo, ' = ', pval)
if pval < .5:
isExo = False
break
if isExo:
exos.append(var)
else:
break
return exos
def judge(path, TRANS):
prob = 1.0
for i in range(len(path) - 1):
d = direction(center[path[i]], center[path[-1]])
prob = prob * TRANS[d, path[i], path[i + 1]]
return [prob] + path
import direction
import csv
import sys
""" seperate path data by direction"""
reader = csv.reader(sys.stdin)
writer = [None] * 8
for i in range(1, 9):
f = open('path' + str(i) + '.dat', 'w')
writer[i - 1] = csv.writer(f)
for row in reader:
if len(row) < 10:
continue
start = (int(row[2][:3]), int(row[2][3:]))
end = (int(row[4][:3]), int(row[4][3:]))
d = direction.direction(start, end)
writer[d - 1].writerow(row)
def D(img1,img2):
gray_different = 100
num_different = 500
# img1=Image.open(img1)
# img2=Image.open(img2)
gray1=img1.convert('L')
gray2=img2.convert('L')
if direction(img2) == direction(img1):
if direction(img2) == 1:
box = (828, 821, 1248, 1041)
else:
box = (740, 827, 1260, 1053)
else:
print('摄像头位置改变')
box = (0,0,1,1)
roi1=gray1.crop(box)
roi2=gray2.crop(box)
roi1.save("./F.jpg")
roi2.save("./S.jpg")
plt.figure("-")
plt.subplot(1,2,1), plt.title('F')
plt.imshow(roi1),plt.axis('off')
plt.subplot(1,2,2), plt.title('S')
plt.imshow(roi2),plt.axis('off')
plt.show()
m1 = np.asarray(roi1)
m2 = np.asarray(roi2)
emptyimg = np.zeros(m1.shape,np.uint8)
def pic_sub(dest,s1,s2):
for x in range(dest.shape[0]):
for y in range(dest.shape[1]):
if(s2[x,y] > s1[x,y]):
dest[x,y] = s2[x,y] - s1[x,y]
else:
dest[x,y] = s1[x,y] - s2[x,y]
# print 'm1:',m1
# print 'm2:',m2
pic_sub(emptyimg,m1,m2)
m = emptyimg
# m = m1
# for i in range(len(m1)):
# # 迭代输出列
# for j in range(len(m1[0])):
# m[i][j] = m1[i][j] - m2[i][j]
# print 'm',m
a = m.shape[0]
b = m.shape[1]
a = int(a)
b = int(b)
c = 0
for i in range(a):
for j in range(b):
if abs(m[i][lj]) > gray_different:
c=c+1
print('people_different:',c)
if c > num_different:
diff = 1
else:
diff =0
print(diff)
return diff
def D(img1, img2):
gray_different = 100
num_different = 500
# img1=Image.open(img1)
# img2=Image.open(img2)
gray1 = img1.convert('L') # 转化为灰度图
gray2 = img2.convert('L')
if direction(img2) == direction(img1):
if direction(img2) == 1:
box = (775, 0, 1113, 30)
else:
box = (628, 5, 940, 30)
else:
print('摄像头位置改变') # 输出
box = (0, 0, 1, 1)
roi1 = gray1.crop(box)
roi2 = gray2.crop(box)
roi1.save("./D_F.jpg")
roi2.save("./D_S.jpg")
plt.figure("-")
plt.subplot(1, 2, 1), plt.title('F')
plt.imshow(roi1), plt.axis('off')
plt.subplot(1, 2, 2), plt.title('S')
plt.imshow(roi2), plt.axis('off')
plt.show()
m1 = np.asarray(roi1)
m2 = np.asarray(roi2)
# print 'm1:',m1
# print 'm2:',m2
emptyimg = np.zeros(m1.shape, np.uint8)
def pic_sub(dest, s1, s2):
for x in range(dest.shape[0]):
for y in range(dest.shape[1]):
if (s2[x, y] > s1[x, y]):
dest[x, y] = s2[x, y] - s1[x, y]
else:
dest[x, y] = s1[x, y] - s2[x, y]
pic_sub(emptyimg, m1, m2)
m = emptyimg
a = m.shape[0]
b = m.shape[1]
a = int(a)
b = int(b)
c = 0
for i in range(a):
for j in range(b):
if abs(m[i][j]) > gray_different:
c = c + 1
print('door_different:', c)
if c > num_different:
diff = 1
else:
diff = 0
print(diff)
return diff
def testDirection(self, x, y):
rho = direction.direction(self.data[x], self.data[y])
return rho
def judge (path, TRANS):
prob = 1.0
for i in range (len(path) - 1):
d = direction (center[path[i]], center[path[-1]])
prob = prob * TRANS[d, path[i], path[i+1]]
return [prob] + path
if mode in ['crohns', 'multi']:
cnt = count(zip(*data))
if any(cnt, lambda(x):x==0):
continue
if chi2_contingency(count(zip(data[0],data[2])))[1]>.01:
continue
if mode=='multi' and chi2_contingency(count(zip(data[0],data[1])))[1]<.1:
continue
elif mode=='multiplat':
cnt = count(zip(*data))
if any(cnt, lambda(x):x==0):
continue
if chi2_contingency(count(zip(data[0],data[1])))[1] < chi2_contingency(count(zip(data[0],data[2])))[1]:
continue
dir=direction(data[0], data[2], data[1], n)
record(dir, True)
i += 1
print '%d fwd (took %d tries)' % (i, tries)
### Chain
i=0
tries=0
while i < runs:
net=struct()
tries += 1
if mode in ['platonic', 'multiplat', 'against-v']:
net.a = random()
# #
# left_ = Median(th1)
#
# th1 = cv.bitwise_not(th1,None)
# xiahua = Thin.Thin(th1)
# xiahua = cv.bitwise_not(xiahua,None)
# #
# gray_method_left = gray.Cvpointgray(th1)
# gray_method_right = gray.Cvpointgray(th2)
# s1 = gaosi_steger.steger(th1)
# s2 = gaosi_steger.steger(th2)
dir_method_left, left_points = direction.direction(th1)
dir_method_right, right_points = direction.direction(th2)
left_img = dir_method_left.copy()
right_img = dir_method_right.copy()
# cv.imshow("th1",left_img)
# cv.imshow("th2",right_img)
# cv.waitKey(0)
# #获取角点
# left_corners = cv.goodFeaturesToTrack(left_img, 3, 0.01, 10)
# right_corners = cv.goodFeaturesToTrack(right_img, 3, 0.01, 10)
# #亚像素角点
# criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 100, 0.001)
# left_sub_corners = cv.cornerSubPix(left_img,left_corners,(5,5),(-1,-1),criteria)
# right_sub_corners = cv.cornerSubPix(right_img,right_corners,(5,5),(-1,-1),criteria)
def gridfeatures(d):
# strokes_list = []
# l = []
# for coordinate in d:
# if coordinate[2] == 0.0:
# l.append(coordinate)
# else:
# strokes_list.append(l)
# l = []
zones = []
stroke_wise_zones = []
zone_features = []
# fr stroke in strokes_list:
zone1 = []
zone2 = []
zone3 = []
zone4 = []
zone5 = []
zone6 = []
zone7 = []
zone8 = []
zone9 = []
zone_list = []
sz = len(d) # no. of rows in d, i.e, no. of coordinates
x_coor = []
y_coor = []
for j in range(sz): # this loop makes a list of all x coordinates
x_coor.append((d[j][0]))
max_x = max(x_coor) # max and min values of x coordinate
min_x = min(x_coor)
for j in range(sz): # this loop makes a list of all y coordinates
y_coor.append((d[j][1]))
max_y = max(y_coor) # max and min values of y coordinate
min_y = min(y_coor)
#print(min_x, max_x, min_y, max_y)
dx = max_x - min_x # Width of the grid
dy = max_y - min_y # Height of the grid
l = dx / 3
m = 2 * (dx / 3)
a = dy / 3
s = l * a
b = 2 * (dy / 3)
#print(min_x, l + min_x, m + min_x, min_y, a + min_y, b + min_y)
for point in d:
if point[0] <= min_x + l:
if point[1] <= min_y + a:
zone1.append(point)
if min_y + a < point[1] <= min_y + b:
zone4.append(point)
if point[1] > min_y + b:
zone7.append(point)
if min_x + l < point[0] <= min_x + m:
if point[1] <= min_y + a:
zone2.append(point)
if min_y + a < point[1] <= min_y + b:
zone5.append(point)
if point[1] > min_y + b:
zone8.append(point)
if point[0] > min_x + m:
if point[1] <= min_y + a:
zone3.append(point)
if min_y + a < point[1] <= min_y + b:
zone6.append(point)
if point[1] > min_y + b:
zone9.append(point)
zone_list.append(zone1)
zone_list.append(zone2)
zone_list.append(zone3)
zone_list.append(zone4)
zone_list.append(zone5)
zone_list.append(zone6)
zone_list.append(zone7)
zone_list.append(zone8)
zone_list.append(zone9)
# print([zone1, zone2, zone3, zone4, zone5, zone6, zone7, zone8, zone9])
zones = []
px_density = 0
for zone in zone_list:
vicinity = []
bin_curl = [0 for x in range(8)]
bin_lin = [0 for x in range(8)]
bin_sl = [0 for x in range(8)]
bin_dir = [0 for x in range(8)]
bin_curv = [0 for x in range(8)]
if len(zone) != 0:
count = len(zone)
px_density = s / count
for i in range(0, len(zone), 11):
j = i
while j < (i + 11) and j < len(zone):
vicinity.append(zone[j])
j = j + 1
bin = quantizer(math.acos(curliness(vicinity)))
bin_curl[bin] = bin_curl[bin] + 1
bin = quantizer(math.atan(linearity(vicinity)))
bin_lin[bin] += 1
bin = quantizer(pixel_angle(vicinity))
bin_sl[bin] += 1
vicinity = []
vicinity = []
for i in range(0, len(zone), 3):
j = i
while j < (i + 3) and j < len(zone):
vicinity.append(zone[j])
j = j + 1
bin = quantizer(direction(vicinity))
bin_dir[bin] += 1
vicinity = []
vicinity = []
for i in range(0, len(zone), 5):
j = i
while j < (i + 5) and j < len(zone):
vicinity.append(zone[j])
j = j + 1
bin = quantizer(curvature(vicinity))
bin_curv[bin] += 1
vicinity = []
for i in range(8):
bin_curv[i] /= len(zone)
bin_dir[i] /= len(zone)
bin_sl[i] /= len(zone)
bin_lin[i] /= len(zone)
bin_curl[i] /= len(zone)
zone_features.append(sum(bin_dir))
zone_features.append(sum(bin_curv))
zone_features.append(sum(bin_sl))
zone_features.append(sum(bin_lin))
zone_features.append(sum(bin_curl))
zone_features.append(px_density)
return zone_features