len(adjusted2) // 15,
len(adjusted2) // 20)
adjusted2 = adjustRoundList(adjusted2, math.floor(select[1]),
math.floor(select[0]))
#sz calculates from just select, could hapepn after the adjust round
sz = 1
if (select[0] > select[1]):
sz = select[0] - select[1]
else:
sz = len(adjusted2) - select[1] + select[0]
#choose a random fragment
index2Choose = math.floor((len(frags) * random.random()) // 1)
#NOT USED ANYWAYdstr = descrips[index2Choose]
frg = frags[index2Choose]
relation = functionMisc.relatePoints(
adjusted2[len(adjusted2) // 3], frg[0])
transdEdge = functionMisc.rotateEdgeToEdge(
adjusted2[len(adjusted2) // 3],
adjusted2[math.floor((len(adjusted2) // 3)) + math.floor(sz)],
frg)
transdEdge2 = functionMisc.ratioTransfer(
adjusted2[math.floor((len(adjusted2) // 3)) + math.floor(sz)],
transdEdge)
if len(transdEdge2) == 0:
fails = fails + 1
continue
crossing = functionMisc.ecross(
outerContour, transdEdge2) or (functionMisc.ecross(
adjusted2[0:math.floor((len(adjusted2) // 3)) - 1:1],
def completeSpline(folderName, testImBig, chosen):
numberOfPoints = 0
accPoint0 = 0
accPoint1 = 0
c = np.load("MPEG7dataset\\test\\extractions\\" + folderName +
"\\metadata.npy")
# just for dimensions
im = cv2.imread('MPEG7dataset\\test\\apple\\apple-1.png')
height, width, channels = im.shape
apples = np.load('MPEG7dataset\\test\\extractions\\apple\\Descriptors.npy')
bell = np.load('MPEG7dataset\\test\\extractions\\bell\\Descriptors.npy')
bone = np.load('MPEG7dataset\\test\\extractions\\Bone\\Descriptors.npy')
car = np.load('MPEG7dataset\\test\\extractions\\car\\Descriptors.npy')
carriage = np.load(
'MPEG7dataset\\test\\extractions\\carriage\\Descriptors.npy')
cellphone = np.load(
'MPEG7dataset\\test\\extractions\\cellular_phone\\Descriptors.npy')
child = np.load(
'MPEG7dataset\\test\\extractions\\children\\Descriptors.npy')
chopper = np.load(
'MPEG7dataset\\test\\extractions\\chopper\\Descriptors.npy')
face = np.load('MPEG7dataset\\test\\extractions\\face\\Descriptors.npy')
flatfish = np.load(
'MPEG7dataset\\test\\extractions\\flatfish\\Descriptors.npy')
fountain = np.load(
'MPEG7dataset\\test\\extractions\\fountain\\Descriptors.npy')
keys = np.load('MPEG7dataset\\test\\extractions\\key\\Descriptors.npy')
shoe = np.load('MPEG7dataset\\test\\extractions\\shoe\\Descriptors.npy')
watch = np.load('MPEG7dataset\\test\\extractions\\watch\\Descriptors.npy')
flann = FLANN()
flann.build_index(testImBig,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
#relavantest stores the fragment indicies with the highest relevance, and their indicies
relavantest = []
#looker is the bound on the length of relevantest
looker = 80
ImgNo = 0
for imager in c:
a = np.split(chosen, c[ImgNo])[1]
#num is the index of the descriptor des relative to the lowest index of the image
num = 0
for des in a:
myResult, myDistance = flann.nn_index(des,
50,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
highestNonCow = len(testImBig) - len(chosen)
inspectArray = []
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test < 0:
inspectArray = np.split(myResult[0], [0, myR])[1]
break
else:
pass
numNearerfound = 0
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (imageBound[0] <
imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
numNearerfound = numNearerfound + 1
myLower = c[ImgNo][0]
#tic is our current index in our insertion search of relavantest
tic = 0
inserted = False
#just an ordered assertion into relavantest
for pre in relavantest:
if tic > looker:
#if weve gone over the length of the thing
#just unsyrence fir the deletion part later
break
if pre[0] < numNearerfound:
#insertion at tic
relavantest.insert(tic, ([numNearerfound, num + myLower]))
inserted = True
if len(relavantest) > looker:
#to ensure we dont go over our specified length, we delete the last element
del relavantest[len(relavantest) - 1]
break
tic = tic + 1
if inserted == False and len(relavantest) < looker:
#initial filling of relecantest
relavantest.append(([numNearerfound, num + myLower]))
num = num + 1
ImgNo = ImgNo + 1
diffrelMax = 40
diffrel = []
diffdescriptors = []
for desc in relavantest:
diffdescriptors.append(chosen[desc[1]])
flann.build_index(np.array(diffdescriptors),
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
it = 0
for someDesc in diffdescriptors:
myResult, myDistance = flann.nn_index(someDesc,
2,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
nextNear = myDistance[0][1]
tic2 = 0
inserted = False
#just an ordered assertion into diffrel
for pre in diffrel:
if tic2 > diffrelMax:
#if weve gone over the length of the thing
#just unsyrence fir the deletion part later
break
if pre[0] < nextNear:
#insertion at tic
diffrel.insert(
tic2, ([nextNear, relavantest[it][0], relavantest[it][1]]))
inserted = True
if len(diffrel) > diffrelMax:
#to ensure we dont go over our specified length, we delete the last element
del diffrel[len(diffrel) - 1]
break
tic2 = tic2 + 1
if inserted == False and len(diffrel) < diffrelMax:
#initial filling of relecantest
diffrel.append(([nextNear, relavantest[it][0],
relavantest[it][1]]))
it = it + 1
lastAr = []
for thing in diffrel:
lastAr.append([thing[1], thing[2]])
relavantest = lastAr
pointsToPol = []
cowFrags = np.load('MPEG7dataset\\test\\extractions\\' + folderName +
'\\Fragments.npy')
for f in cowFrags:
for p in f:
numberOfPoints = numberOfPoints + 1
accPoint1 = accPoint1 + p[1]
accPoint0 = accPoint0 + p[0]
for top in relavantest:
number = top[1]
for x in cowFrags[number]:
xval = x[1]
yval = x[0]
pointsToPol.append(x)
#fact = 25
#fish with fewer points
#fact = 4.5
#fish with iterations
#fact = 10
#fish without iterations
#fact = 12
#applespline
#fact = 7
#tryWithIT TODO
#bellfail fact = 4
#bone
#fact = 17.34
#fact = 3.73
#car
#fact = 11
#cellphone
#fact = 3.5
#fountain
#fact = 15.75
#key
#shoe
fact = 19
pointCenter = [accPoint0 // numberOfPoints, accPoint1 // numberOfPoints]
pointRelToCenter = []
acc = 0
acc2 = 0
for p in pointsToPol:
acc = acc + 1
if acc % 5 != 0:
continue
acc2 = acc2 + 1
relation = functionMisc.relatePoints(p, pointCenter)
x = relation[1]
y = relation[0]
pointRelToCenter.append(
np.array(
[np.sqrt(x**2 + y**2) / fact,
np.arctan2(y, x) + math.pi * 2]))
pointRelToCenter.append(
np.array(
[np.sqrt(x**2 + y**2) / fact,
np.arctan2(y, x) - math.pi * 2]))
pointRelToCenter.append(
np.array([np.sqrt(x**2 + y**2) / fact,
np.arctan2(y, x)]))
polarCollect = np.array(pointRelToCenter)
sorted = polarCollect[polarCollect[:, 1].argsort()]
stX = sorted[:, 1]
stY = sorted[:, 0]
s2 = inter.pchip(stX, stY)
xs = np.arange(-3.14158 * 2.5, 3.14159 * 2.5, 0.01)
xx = np.arange(-3.14158 * 2.5, 3.14159 * 2.5, 0.01)
yUnNew = s2(xs)
xNext = []
YNext = []
for x in range(0, len(yUnNew)):
if x % (len(yUnNew) // 500) == 0:
xNext.append(xs[x])
YNext.append(yUnNew[x])
spl3 = inter.UnivariateSpline(xNext, YNext, s=650)
xx5 = np.arange(-3.14158 * 2.5, 3.14159 * 2.5, 0.01)
yy5 = spl3(xx)
plt.plot(xNext, YNext, 'r-', label='pchip fish')
plt.plot(xx5, yy5, 'b', label='evened')
splineCart = []
valsToUse = np.arange(-(math.pi), math.pi, 0.001)
rsToUse = spl3(valsToUse)
for ind in range(len(valsToUse)):
rho = rsToUse[ind] * fact
phi = valsToUse[ind]
x = rho * np.cos(phi)
y = rho * np.sin(phi)
splineCart.append([int(y), int(x)])
for x in splineCart:
#outContour:
if x[0] + pointCenter[0] > 0 and x[1] + pointCenter[1] > 0 and x[
0] + pointCenter[0] < 640 and x[1] + pointCenter[1] < 640:
xval = x[1]
yval = x[0]
slipped = []
for x in splineCart:
slipped.append([x[0] + pointCenter[0], x[1] + pointCenter[1]])
minW1 = min(np.array(slipped)[:, 1])
minW0 = min(np.array(slipped)[:, 0])
if (minW1 < 0):
slipped2 = []
for x in slipped:
slipped2.append([x[0], x[1] + abs(minW1) + 5])
slipped = slipped2
if (minW0 < 0):
slipped2 = []
for x in slipped:
slipped2.append([x[0] + abs(minW0) + 5, x[1]])
slipped = slipped2
final = []
for x in functionMisc.fill(functionMisc.thin(slipped + [slipped[0]])):
if x[0] > 0 and x[1] > 0 and x[0] < 640 and x[1] < 640:
final.append(x)
return final
def petsketch(img):
height, width, channels = img.shape
#img = cv2.fastNlMeansDenoisingColored(img,None,20,20,7,21)
img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
edges = cv2.Canny(img, 70, 200)
kernel = np.ones((2, 2), np.uint8)
dilation = cv2.dilate(edges, kernel, iterations=1)
im3, Ocontours, hierarchy = cv2.findContours(dilation, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)
c2 = []
forgotten = []
for cont in Ocontours:
#print(len(cont))
if len(cont) > 10:
c2.append(cont)
elif len(cont) > 4:
forgotten.append(cont[:len(cont) // 2])
contours = c2
#print(forgotten)
# top = []
# bot = []
# left = []
# right = []
# for x in range(0,width):
# top.append([x,0])
# bot.append([width-x,height])
# for y in range(0,height):
# right.append([width,y])
# left.append([0,height-y])
# outerContour = top + right + bot+ left
blank_image = np.zeros((height, width, 3), np.uint8)
# im = cv2.imread("ex3.png")
# #im = cv2.imread("circle2.png")
# height, width, channels = im.shape
# imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
# ret,thresh = cv2.threshold(imgray,127,255,0)
# height, width, channels = im.shape
# im3, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
# blank_image = np.zeros((height,width,3), np.uint8)
# # startlistOfPoints = []
# # for ind, x in enumerate(contours[1]):
# # xval = x[0][0]
# # yval = x[0][1]
# # startlistOfPoints.append([yval, xval])
# # listOfPoints= np.array(startlistOfPoints)
# #blank_image = np.zeros(((height*4)//2,((width*4)//2),3), np.uint8)
outerContour = []
for ind, x in enumerate(contours[0]):
xval = x[0][0]
yval = x[0][1]
outerContour.append([yval, xval])
# for x in outerContour:
# xval = x[1]
# yval = x[0]
# blank_image[yval][xval]=[0,255,255]
folderName = "flatfish"
frags = testFrags
for cont in range(1, len(contours)):
# print("onto ",cont," out of ",len(contours),len(contours[cont]))
# sys.stdout.flush()
startlistOfPoints = []
for ind, x in enumerate(contours[cont]):
xval = x[0][0]
yval = x[0][1]
startlistOfPoints.append([yval, xval])
listOfPoints = np.array(startlistOfPoints)
steps = 10
#maxAttempts = 1
adjusted2 = listOfPoints
fails = 0
for x in range(0, steps):
# selection = []
# distances = []
crossing = True
while crossing:
if fails > 0:
fails = 0
break
#historic = adjusted2
if (len(adjusted2) < 10):
break
elif (len(adjusted2) < 100):
select = getRandomSection(len(adjusted2),
len(adjusted2) // 3,
len(adjusted2) // 6)
else:
select = getRandomSection(len(adjusted2),
len(adjusted2) // 10,
len(adjusted2) // 15)
adjusted2 = adjustRoundList(adjusted2, math.floor(select[1]),
math.floor(select[0]))
sz = 1
if (select[0] > select[1]):
sz = select[0] - select[1]
else:
sz = len(adjusted2) - select[1] + select[0]
subfrag = adjusted2[math.floor((
len(adjusted2) // 3)):math.floor((len(adjusted2) // 3)) +
math.floor(sz):1]
subdesc = generateDescriptor(subfrag, height, width)
myResult, myDistance = flann.nn_index(subdesc,
5,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
frg = frags[myResult[0][0]]
relation = functionMisc.relatePoints(
adjusted2[len(adjusted2) // 3], frg[0])
transdEdge = functionMisc.rotateEdgeToEdge(
adjusted2[len(adjusted2) // 3],
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz)], frg)
transdEdge2 = functionMisc.ratioTransfer(
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz)], transdEdge)
if len(transdEdge2) == 0:
fails = fails + 1
#print("lenfail length of frg ",len(frg), " sz ",sz)
#break
if fails < 1:
#crossing = functionMisc.ecross(outerContour,transdEdge2) or (functionMisc.ecross(adjusted2[0 : math.floor((len(adjusted2)//3))-1 : 1],transdEdge2) or functionMisc.ecross(adjusted2[math.floor((len(adjusted2)//3))+math.floor(sz)+1 : len(adjusted2) : 1], transdEdge2))
#CAUSE WE NO LONGER CARE
crossing = functionMisc.ecross(outerContour, transdEdge2)
if not crossing:
#transdEdge2
#adjusted2[0 : math.floor((len(adjusted2)//3)) : 1]
partReplacing = adjusted2[math.floor((
len(adjusted2) //
3)):math.floor((len(adjusted2) // 3)) +
math.floor(sz):1]
newTransdEdge = []
# maxp = 5
# petx = random.randint(-maxp,maxp) * random.randint(-maxp,maxp)
# pety = random.randint(-maxp,maxp) * random.randint(-maxp,maxp)
petx = 0
pety = 0
for ind in range(0, len(transdEdge2)):
p = transdEdge2[ind]
q = partReplacing[(len(partReplacing) * ind) //
len(transdEdge2)]
newTransdEdge.append([
petx + q[0] - (-p[0] + q[0]) // 1,
pety + q[1] - (-p[1] + q[1]) // 1
])
transdEdge2 = newTransdEdge
adjusted2 = np.concatenate(
(np.concatenate(
(adjusted2[0:math.floor((len(adjusted2) //
3)):1], transdEdge2)),
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz):len(adjusted2):1]))
for x in adjusted2:
xval = x[1]
yval = x[0]
blank_image[yval][xval] = [255, 255, 255]
#print("fails: ",fails)
for line in forgotten:
for x in line:
yval = x[0][1]
xval = x[0][0]
blank_image[yval][xval] = [255, 255, 255]
# cv2.imshow('Edges',blank_image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return blank_image
def complete():
truths = 0
folderName = "flatfish"
c = np.load("MPEG7dataset\\test\\extractions\\" + folderName +
"\\metadata.npy")
# just for dimensions
im = cv2.imread('MPEG7dataset\\test\\apple\\apple-1.png')
height, width, channels = im.shape
blank_image = np.zeros(((height * 5) // 2, ((width * 5) // 2), 3),
np.uint8)
#finding most relevant in cow using cat as vs
apples = np.load('MPEG7dataset\\test\\extractions\\apple\\Descriptors.npy')
bell = np.load('MPEG7dataset\\test\\extractions\\bell\\Descriptors.npy')
bone = np.load('MPEG7dataset\\test\\extractions\\Bone\\Descriptors.npy')
car = np.load('MPEG7dataset\\test\\extractions\\car\\Descriptors.npy')
carriage = np.load(
'MPEG7dataset\\test\\extractions\\carriage\\Descriptors.npy')
cellphone = np.load(
'MPEG7dataset\\test\\extractions\\cellular_phone\\Descriptors.npy')
child = np.load(
'MPEG7dataset\\test\\extractions\\children\\Descriptors.npy')
chopper = np.load(
'MPEG7dataset\\test\\extractions\\chopper\\Descriptors.npy')
face = np.load('MPEG7dataset\\test\\extractions\\face\\Descriptors.npy')
flatfish = np.load(
'MPEG7dataset\\test\\extractions\\flatfish\\Descriptors.npy')
fountain = np.load(
'MPEG7dataset\\test\\extractions\\fountain\\Descriptors.npy')
keys = np.load('MPEG7dataset\\test\\extractions\\key\\Descriptors.npy')
shoe = np.load('MPEG7dataset\\test\\extractions\\shoe\\Descriptors.npy')
watch = np.load('MPEG7dataset\\test\\extractions\\watch\\Descriptors.npy')
testImBig = np.concatenate([
apples, child, carriage, face, chopper, watch, bell, bone, car,
cellphone, fountain, keys, shoe, flatfish
],
axis=0)
chosen = flatfish
flann = FLANN()
flann.build_index(testImBig,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
#relavantest stores the fragment indicies with the highest relevance, and their indicies
relavantest = []
#looker is the bound on the length of relevantest
looker = 30
ImgNo = 0
for imager in c:
a = np.split(chosen, c[ImgNo])[1]
#num is the index of the descriptor des relative to the lowest index of the image
num = 0
for des in a:
#print(len(a))
myResult, myDistance = flann.nn_index(des,
50,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
highestNonCow = len(testImBig) - len(chosen)
inspectArray = []
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test < 0:
inspectArray = np.split(myResult[0], [0, myR])[1]
break
else:
pass
numNearerfound = 0
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (imageBound[0] <
imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
numNearerfound = numNearerfound + 1
myLower = c[ImgNo][0]
tic = 0
inserted = False
#just an ordered assertion into relavantest
for pre in relavantest:
if tic > looker:
#if weve gone over the length of the thing
#just unsyrence fir the deletion part later
break
if pre[0] < numNearerfound:
#insertion at tic
relavantest.insert(tic, ([numNearerfound, num + myLower]))
inserted = True
if len(relavantest) > looker:
#to ensure we dont go over our specified length, we delete the last element
del relavantest[len(relavantest) - 1]
break
tic = tic + 1
if inserted == False and len(relavantest) < looker:
#initial filling of relecantest
relavantest.append(([numNearerfound, num + myLower]))
num = num + 1
ImgNo = ImgNo + 1
fragList = []
cowFrags = np.load('MPEG7dataset\\test\\extractions\\' + folderName +
'\\Fragments.npy')
for top in relavantest:
number = top[1]
fragList.append(cowFrags[number])
#phase 2===============================================================================================================================================================================
#=================================================================================================================
#=================================================================================================================
fullShape = AllRelGet()
im = cv2.imread("exampleImage.png")
height, width, channels = im.shape
flann.build_index(testImBig,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
height, width, channels = im.shape
im3, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)
blank_image = np.zeros((height, width, 3), np.uint8)
c = np.load("MPEG7dataset\\test\\extractions\\" + folderName +
"\\metadata.npy")
outerContour = []
for ind, x in enumerate(contours[0]):
xval = x[0][0]
yval = x[0][1]
outerContour.append([yval, xval])
for x in outerContour:
xval = x[1]
yval = x[0]
blank_image[yval][xval] = [0, 255, 255]
frags = fragList
listOfPoints = fullShape
steps = 0
maxAttempts = 5
adjusted2 = listOfPoints
fails = 0
for x in range(0, steps):
selection = []
distances = []
print("\n")
for x in range(0, maxAttempts):
crossing = True
while crossing:
if fails > 8:
fails = 0
break
historic = adjusted2
select = getRandomSection(len(adjusted2),
len(adjusted2) // 12,
len(adjusted2) // 20)
adjusted2 = adjustRoundList(adjusted2, math.floor(select[1]),
math.floor(select[0]))
adjustedRot = adjusted2
#sz calculates from just select, could hapepn after the adjust round
sz = 1
if (select[0] > select[1]):
sz = select[0] - select[1]
else:
sz = len(adjusted2) - select[1] + select[0]
#choose a random fragment
index2Choose = math.floor((len(frags) * random.random()) // 1)
frg = frags[index2Choose]
relation = functionMisc.relatePoints(
adjusted2[len(adjusted2) // 3], frg[0])
transdEdge = functionMisc.rotateEdgeToEdge(
adjusted2[len(adjusted2) // 3],
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz)], frg)
transdEdge2 = functionMisc.ratioTransfer(
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz)], transdEdge)
if len(transdEdge2) == 0:
fails = fails + 1
continue
crossing = functionMisc.ecross(
outerContour, transdEdge2) or (functionMisc.ecross(
adjusted2[0:math.floor((len(adjusted2) // 3)) - 1:1],
transdEdge2) or functionMisc.ecross(
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz) + 1:len(adjusted2):1],
transdEdge2))
if not crossing:
adjusted2 = np.concatenate(
(np.concatenate(
(adjusted2[0:math.floor((len(adjusted2) // 3)):1],
transdEdge2)),
adjusted2[math.floor((len(adjusted2) // 3)) +
math.floor(sz):len(adjusted2):1]))
targetLength = len(frg)
rtdd = functionMisc.createDescriptorSet(
adjusted2, height, width, targetLength, 2.2)
totalSum = 0
for descr in rtdd:
#======= replaced with full descriptor
# myResult, myDistance = flann.nn_index(descr, 5, algorithm="kmeans", branching=32, iterations=7, checks=16)
# intersum = 0
# for indist in myDistance[0]:
# intersum = intersum + indist
# totalSum = totalSum + intersum
#============
#full relevance \/\/\/
#=============
myResult, myDistance = flann.nn_index(
descr,
50,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
highestNonCow = len(testImBig) - len(chosen)
#the place in our index of the last noncow, the remainder of the index is cows //^^^
inspectArray = []
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test < 0:
inspectArray = np.split(myResult[0],
[0, myR])[1]
break
else:
pass
numNearerfound = 0
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (
imageBound[0] < imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
print("!")
numNearerfound = numNearerfound + 1
if numNearerfound == 0:
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test > 0:
inspectArray = np.split(
myResult[0], [0, myR])[1]
break
else:
pass
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (
imageBound[0] < imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
#print("!")
numNearerfound = numNearerfound - 1
totalSum = totalSum - numNearerfound
#==============
selection.append(adjusted2)
rtdd = functionMisc.createDescriptorSet(
adjustedRot, height, width, targetLength, 2.2)
totalSumOld = 0
for descr in rtdd:
myResult, myDistance = flann.nn_index(
descr,
50,
algorithm="kmeans",
branching=32,
iterations=7,
checks=16)
highestNonCow = len(testImBig) - len(chosen)
inspectArray = []
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test < 0:
inspectArray = np.split(myResult[0],
[0, myR])[1]
break
else:
pass
numNearerfound = 0
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (
imageBound[0] < imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
numNearerfound = numNearerfound + 1
if numNearerfound == 0:
for myR in range(len(myResult[0])):
test = myResult[0][myR] - highestNonCow
if test > 0:
inspectArray = np.split(
myResult[0], [0, myR])[1]
break
else:
pass
if len(inspectArray) > 1:
dict = {'init': 'fake'}
for cand in inspectArray:
imgPlace = cand - (highestNonCow + 1)
imgID = 0
for imageBound in c:
if (imageBound[1] > imgPlace) and (
imageBound[0] < imgPlace):
imgID = imageBound[0]
break
if imgID in dict:
pass
else:
dict[imgID] = 1
#print("!")
numNearerfound = numNearerfound - 1
totalSumOld = totalSumOld - numNearerfound
adjusted2 = historic
print('NewNearer:', -totalSum, " OldNearer:", -totalSumOld)
distances = distances + [totalSum - totalSumOld]
indexI = 0
best = distances[0]
for distInd in range(0, len(distances)):
if (distances[distInd] < best):
indexI = distInd
best = distances[distInd]
if (best < 0):
adjusted2 = selection[indexI]
print("tic")
print("fails: ", fails)
#================================================================
for x in fullShape:
xval = x[1]
yval = x[0]
blank_image[yval][xval] = [255, 0, 0]
for x in adjusted2:
xval = x[1]
yval = x[0]
blank_image[yval][xval] = [255, 255, 255]
cv2.imshow('Edges', blank_image)
print("ok finish")
print("truth", truths)
plt.minorticks_on()
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()