def MatchAllCluster(save, maxdist=200, groups=3, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0, maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[
PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append(
[PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
# convert to np.float32
Z = np.float32(Z)
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, 3)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam * distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([
centers[j][0], centers[j][1], depth[centers[j][1], centers[j][0]]
])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
return segregatedF, centers, img, depth, FinalCenters, FinalCentersWC
def MatchAllCluster(save, maxdist=200, groups=3, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0,maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append([PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
# convert to np.float32
Z = np.float32(Z)
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, 3)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam*distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([centers[j][0],centers[j][1],depth[centers[j][1],centers[j][0]]])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
return segregatedF, centers, img, depth, FinalCenters, FinalCentersWC
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0, maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[
PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append(
[PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
if len(Z) < 20:
print "No Cups"
cv2.imshow("Cups Stream", img)
return
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [
(sum(distFromCenterAve1) / len(distFromCenterAve1)) * 1.0,
(sum(distFromCenterAve2) / len(distFromCenterAve2)) * 2.0,
(sum(distFromCenterAve3) / len(distFromCenterAve3)) * 3.0,
(sum(distFromCenterAve4) / len(distFromCenterAve4)) * 4.0,
(sum(distFromCenterAve5) / len(distFromCenterAve5)) * 5.0
]
groups = distFromCenterAveList.index(min(distFromCenterAveList)) + 1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam * distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
#remove clusters that are >= 3 points or all superimposed
i = 0
while i < groups:
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[i])):
del segregatedF[i], centers[i], distFromCenter[
i], distFromCenterAve[i]
groups -= 1
i += 1
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([
centers[j][0], centers[j][1], depth[centers[j][1], centers[j][0]]
])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
segregated = segregatedF
FC = FinalCenters
colourList = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255),
(255, 255, 0), (255, 0, 255)]
# Seperate the top of each cup in pixel space
depthimg = img.copy()
depthmask = depth.copy()
# Start Cup Classification loop
for j in xrange(groups):
centx = FC[j][0]
centy = FC[j][1]
centdepth = FC[j][2]
# Choose pixel area likley to contain a cup
w = -0.08811 * centdepth + 103.0837
h = -0.13216 * centdepth + 154.6256
h = h
cup1 = depthimg[(centy - h):(centy), (centx - w):(centx + w)]
cupDepth1 = depthmask[(centy - h):(FC[j][1]), (centx - w):(centx + w)]
# Create blank binary images to fill with depth thresholds
shape1 = np.zeros(cupDepth1.shape, dtype=np.uint8)
# Fill with threshold depths
upper = centdepth + 100
lower = centdepth - 50
depthRange = []
midDepthRange = []
for i in xrange(cupDepth1.shape[0]):
for k in xrange(cupDepth1.shape[1]):
if lower < cupDepth1[i, k] < upper:
shape1[i, k] = cupDepth1[i, k]
depthRange.append(cupDepth1[i, k])
if i == cupDepth1.shape[0] - 1:
midDepthRange.append(k)
if len(midDepthRange) < 3:
continue
cv2.imshow('s**t', shape1)
cv2.waitKey(0)
midMin = min(midDepthRange)
midMax = max(midDepthRange)
s3 = [(centx - w) + midMin, centy, centdepth]
s4 = [(centx - w) + midMax, centy, centdepth]
mid = [s3, s4]
midWorld = convertToWorldCoords(mid)
CupMidWidth = midWorld[1][0] - midWorld[0][0]
CupTopWidth = max(depthRange) - min(depthRange)
print "Mid Width", CupMidWidth
print "Top Width", CupTopWidth
if CupMidWidth > CupTopWidth:
cupOrientation = "Upsidedown"
cupFill = "Empty"
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 58:
cupType = "Large"
elif CupTopWidth > 49:
cupType = "Medium"
elif CupTopWidth > 20:
cupType = "Small"
else:
cupType = "Not a Cup"
else:
cupOrientation = "Upright"
cupFill = "Unsure"
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 81:
cupType = "Large"
elif CupTopWidth > 71:
cupType = "Medium"
elif CupTopWidth > 30:
cupType = "Small"
else:
cupType = "Not a Cup"
#Draw the top of the bounding rectangle
if cupType <> "Not a Cup":
FinalCentersWC[j].append(cupType)
FinalCentersWC[j].append(cupOrientation)
FinalCentersWC[j].append(cupFill)
# Draw the groups
deleteList = []
for j in xrange(groups):
if len(FinalCentersWC[j]) > 3:
centerst = tuple(np.array(centers[j]) + np.array([0, 50]))
cv2.putText(img, str(FinalCentersWC[j]), centerst,
cv2.FONT_HERSHEY_SIMPLEX, 0.3, colourList[j])
cv2.circle(img, centers[j], 10, colourList[j], -1)
cv2.circle(img, centers[j], 2, (0, 0, 0), -1)
for i in range(len(segregated[j])):
pt_a = (int(segregated[j][i, 0]), int(segregated[j][i, 1]))
cv2.circle(img, pt_a, 3, colourList[j])
cv2.line(img, pt_a, centers[j], colourList[j])
else:
deleteList.append(j)
FinalFinalCentersWC = [
i for j, i in enumerate(FinalCentersWC) if j not in deleteList
]
if save == 1:
cv2.imwrite('ProcessedImages/ProcessedCluster' + str(ImageNo) + '.jpg',
img)
if len(FinalFinalCentersWC) <> 0:
print FinalFinalCentersWC
cv2.imshow("Cups Stream", img)
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0,maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append([PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
if len(Z) < 30:
print "No Cups"
cv2.imshow("Cups Stream", img)
return
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [(sum(distFromCenterAve1)/len(distFromCenterAve1))*1.0,
(sum(distFromCenterAve2)/len(distFromCenterAve2))*2.0,
(sum(distFromCenterAve3)/len(distFromCenterAve3))*3.0,
(sum(distFromCenterAve4)/len(distFromCenterAve4))*4.0,
(sum(distFromCenterAve5)/len(distFromCenterAve5))*5.0]
groups = distFromCenterAveList.index(min(distFromCenterAveList))+1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam*distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
#remove clusters that are >= 3 points or all superimposed
i = 0
while i < groups:
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[i])):
del segregatedF[i], centers[i], distFromCenter[i], distFromCenterAve[i]
groups -= 1
i += 1
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([centers[j][0],centers[j][1],depth[centers[j][1],centers[j][0]]])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
segregated = segregatedF
FC = FinalCenters
colourList=[(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255), (255, 255, 0), (255, 0, 255)]
# Seperate the top of each cup in pixel space
depthimg = img.copy()
maskimg = depth.copy()
# Start Cup Classification loop
for j in xrange(groups):
# Choose pixel area likley to contain a cup
w = -0.08811*FC[j][2]+103.0837
h = -0.13216*FC[j][2]+154.6256
h = h*1.8
cup1 = depthimg[(FC[j][1]-h):(FC[j][1]), (FC[j][0]-w):(FC[j][0]+w)]
mask1 = maskimg[(FC[j][1]-h):(FC[j][1]), (FC[j][0]-w):(FC[j][0]+w)]
cup2 = depthimg[(FC[j][1]):(FC[j][1]+h), (FC[j][0]-w):(FC[j][0]+w)]
mask2 = maskimg[(FC[j][1]):(FC[j][1]+h), (FC[j][0]-w):(FC[j][0]+w)]
# Determine the bouding rectangle of the largest contour in the top area
gray = cv2.cvtColor(cup1,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
blurDepth = cv2.blur(mask1,(5,5))
thresh1 = 200
thresh2 = 400
for i in xrange(cup1.shape[0]):
for j in xrange(cup1.shape[1]):
if blurDepth[i,j] == 0 or blurDepth[i,j]>1000:
gray[i,j] = 0
edges = cv2.Canny(gray,thresh1,thresh2)
contours,hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
print "No Top Contours"
return
else:
cnt = max(contours, key=len)
x,y,w1,h1 = cv2.boundingRect(cnt)
# Determine the bouding rectangle of the largest contour in the bottom area
gray2 = cv2.cvtColor(cup2,cv2.COLOR_BGR2GRAY)
blur2 = cv2.GaussianBlur(gray2,(5,5),0)
thresh21 = 200
thresh22 = 400
edges2 = cv2.Canny(blur2,thresh21,thresh22)
contours2,hierarchy2 = cv2.findContours(edges2,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if len(contours2) == 0:
print "No Bottom Contours"
return
else:
cnt2 = max(contours2, key=len)
x2,y2,w21,h21 = cv2.boundingRect(cnt2)
# Use brounding rectangle size to determine cup type and orientation
s1 = [(FC[j][0]-w)+x,(FC[j][1]-h)+ y,FC[j][2]]
s2 = [(FC[j][0]-w)+x+w1,(FC[j][1]-h)+ y,FC[j][2]]
s3 = [(FC[j][0]-w)+x2,(FC[j][1])+ y2 + h21,FC[j][2]]
s4 = [(FC[j][0]-w)+x2+w21,(FC[j][1])+ y2 + h21,FC[j][2]]
top = [s1,s2]
bottom = [s3,s4]
topWorld = convertToWorldCoords(top)
bottomWorld = convertToWorldCoords(bottom)
CupTopWidth = topWorld[1][0]-topWorld[0][0]
CupBottomWidth = bottomWorld[1][0]-bottomWorld[0][0]
if CupBottomWidth > CupTopWidth:
cupOrientation = "Upsidedown"
cupFill = "Empty"
CupTopWidth = CupBottomWidth
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 84.5:
cupType = "Large"
elif CupTopWidth > 71:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
else:
cupOrientation = "Upright"
cupFill = "Unsure"
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 84.5:
cupType = "Large"
elif CupTopWidth > 71:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
#Draw the top of the bounding rectangle
if cupType <> "Not a Cup":
new_cnt = cnt + [int(round((FC[j][0]-w),0)),int(round((FC[j][1]-h),0))]
cv2.line(img,(int(round(s1[0],0)),int(round(s1[1],0))),(int(round(s2[0],0)),int(round(s2[1],0))),colourList[j])
cv2.drawContours(img,[new_cnt],0,colourList[j],2)
new_cnt2 = cnt2 + [int(round((FC[j][0]-w),0)),int(round((FC[j][1]),0))]
cv2.line(img,(int(round(s3[0],0)),int(round(s3[1],0))),(int(round(s4[0],0)),int(round(s4[1],0))),colourList[j])
cv2.line(img,(int(round(s3[0],0)),int(round(s3[1],0))),(int(round(s1[0],0)),int(round(s1[1],0))),colourList[j])
cv2.line(img,(int(round(s4[0],0)),int(round(s4[1],0))),(int(round(s2[0],0)),int(round(s2[1],0))),colourList[j])
cv2.drawContours(img,[new_cnt2],0,colourList[j],2)
FinalCentersWC[j].append(cupType)
FinalCentersWC[j].append(cupOrientation)
FinalCentersWC[j].append(cupFill)
# Draw the groups
deleteList = []
for j in xrange(groups):
if len(FinalCentersWC[j]) > 3:
centerst = tuple(np.array(centers[j])+np.array([0,50]))
cv2.putText(img,str(FinalCentersWC[j]), centerst, cv2.FONT_HERSHEY_SIMPLEX, 0.3, colourList[j])
cv2.circle(img, centers[j], 10, colourList[j], -1)
cv2.circle(img, centers[j], 2, (0,0,0), -1)
for i in range(len(segregated[j])):
pt_a = (int(segregated[j][i,0]), int(segregated[j][i,1]))
cv2.circle(img, pt_a, 3, colourList[j])
else:
deleteList.append(j)
FinalFinalCentersWC = [i for j, i in enumerate(FinalCentersWC) if j not in deleteList]
if save == 1:
cv2.imwrite('ProcessedImages/ProcessedCluster'+str(ImageNo)+'.jpg', img)
if len(FinalFinalCentersWC)<>0:
print FinalFinalCentersWC
cv2.imshow("Cups Stream", img)
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0,maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append([PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
if len(Z) < 20:
print "No Cups"
cv2.imshow("Cups Stream", img)
return
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [(sum(distFromCenterAve1)/len(distFromCenterAve1))*1.0,
(sum(distFromCenterAve2)/len(distFromCenterAve2))*2.0,
(sum(distFromCenterAve3)/len(distFromCenterAve3))*3.0,
(sum(distFromCenterAve4)/len(distFromCenterAve4))*4.0,
(sum(distFromCenterAve5)/len(distFromCenterAve5))*5.0]
groups = distFromCenterAveList.index(min(distFromCenterAveList))+1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam*distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
#remove clusters that are >= 3 points or all superimposed
i = 0
while i < groups:
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[i])):
del segregatedF[i], centers[i], distFromCenter[i], distFromCenterAve[i]
groups -= 1
i += 1
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([centers[j][0],centers[j][1],depth[centers[j][1],centers[j][0]]])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
segregated = segregatedF
FC = FinalCenters
colourList=[(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255), (255, 255, 0), (255, 0, 255)]
# Seperate the top of each cup in pixel space
depthimg = img.copy()
depthmask = depth.copy()
# Start Cup Classification loop
for j in xrange(groups):
centx = FC[j][0]
centy = FC[j][1]
centdepth = FC[j][2]
# Choose pixel area likley to contain a cup
w = -0.08811*centdepth+103.0837
h = -0.13216*centdepth+154.6256
h = h
cup1 = depthimg[(centy-h):(centy), (centx-w):(centx+w)]
cup11 = np.copy(cup1)
cupDepth1 = depthmask[(centy-h):(FC[j][1]), (centx-w):(centx+w)]
# Create blank binary images to fill with depth thresholds
shape1 = np.zeros(cupDepth1.shape,dtype=np.uint8)
# Fill with threshold depths
upper = centdepth+100
lower = centdepth-50
depthRange = []
depthRangePos = []
midDepthRange = []
for i in xrange(cupDepth1.shape[0]):
for k in xrange(cupDepth1.shape[1]):
if lower<cupDepth1[i,k]<upper:
shape1[i,k] = cupDepth1[i,k]
depthRange.append(cupDepth1[i,k])
depthRangePos.append([k,i])
if i == cupDepth1.shape[0]-1:
midDepthRange.append(k)
if len(midDepthRange) < 3:
continue
cv2.imshow('Depth',shape1)
# Apply a median filter to the depth thresholds
shape1blur = cv2.blur(shape1,(5,5))
cv2.imshow('blur',shape1blur)
#cv2.imshow('cnt',cup2)
#cv2.waitKey(0)
# Determine the bouding rectangle of the largest contour in the top area
thresh1 = 100
thresh2 = 200
edges = cv2.Canny(shape1blur,thresh1,thresh2)
cv2.imshow('edges',edges)
contours,hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cont = np.vstack(contours)
for cnt in contours:
cv2.drawContours(cup1,[cnt],0,colourList[j],2)
cv2.imshow('cnt',cup1)
ellipse = cv2.fitEllipse(cont)
cv2.ellipse(cup1, ellipse, colourList[j+2], 2)
cv2.imshow('ellipse',cup1)
Maxpos = depthRangePos[depthRange.index(max(depthRange))]
Minpos = depthRangePos[depthRange.index(min(depthRange))]
print Maxpos
print Minpos
midMin = min(midDepthRange)
midMax = max(midDepthRange)
s3 = [(centx-w)+midMin,centy,centdepth]
s4 = [(centx-w)+midMax,centy,centdepth]
mid = [s3,s4]
midWorld = convertToWorldCoords(mid)
CupMidWidth = midWorld[1][0]-midWorld[0][0]
CupTopWidth = max(depthRange)-min(depthRange)
cv2.circle(cup11, tuple(Maxpos), 3, colourList[j+1])
cv2.circle(cup11, tuple(Minpos), 3, colourList[j+1])
cv2.line(cup11, tuple(Maxpos), tuple(Minpos), colourList[j+1])
cv2.imshow('MaxMin',cup11)
print "Mid Width",CupMidWidth
print "Top Width",CupTopWidth
if CupMidWidth > CupTopWidth:
cupOrientation = "Upsidedown"
cupFill = "Empty"
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 58:
cupType = "Large"
elif CupTopWidth > 49:
cupType = "Medium"
elif CupTopWidth > 20:
cupType = "Small"
else:
cupType = "Not a Cup"
else:
cupOrientation = "Upright"
cupFill = "Unsure"
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 81:
cupType = "Large"
elif CupTopWidth > 69:
cupType = "Medium"
elif CupTopWidth > 30:
cupType = "Small"
else:
cupType = "Not a Cup"
#Draw the top of the bounding rectangle
if cupType <> "Not a Cup":
FinalCentersWC[j].append(cupType)
FinalCentersWC[j].append(cupOrientation)
FinalCentersWC[j].append(cupFill)
# Draw the groups
deleteList = []
for j in xrange(groups):
if len(FinalCentersWC[j]) > 3:
centerst = tuple(np.array(centers[j])+np.array([0,50]))
cv2.putText(img,str(FinalCentersWC[j]), centerst, cv2.FONT_HERSHEY_SIMPLEX, 0.3, colourList[j])
cv2.circle(img, centers[j], 10, colourList[j], -1)
cv2.circle(img, centers[j], 2, (0,0,0), -1)
for i in range(len(segregated[j])):
pt_a = (int(segregated[j][i,0]), int(segregated[j][i,1]))
cv2.circle(img, pt_a, 3, colourList[j])
cv2.line(img, pt_a, centers[j], colourList[j])
else:
deleteList.append(j)
FinalFinalCentersWC = [i for j, i in enumerate(FinalCentersWC) if j not in deleteList]
if save == 1:
cv2.imwrite('ProcessedImages/ProcessedCluster'+str(ImageNo)+'.jpg', img)
if len(FinalFinalCentersWC)<>0:
print FinalFinalCentersWC
cv2.imshow("Cups Stream", img)
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0,maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append([PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
if len(Z) < 30:
print "No Cups"
return
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [(sum(distFromCenterAve1)/len(distFromCenterAve1))*1.0,
(sum(distFromCenterAve2)/len(distFromCenterAve2))*2.0,
(sum(distFromCenterAve3)/len(distFromCenterAve3))*3.0,
(sum(distFromCenterAve4)/len(distFromCenterAve4))*4.0,
(sum(distFromCenterAve5)/len(distFromCenterAve5))*5.0]
groups = distFromCenterAveList.index(min(distFromCenterAveList))+1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam*distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
#remove clusters that are >= 3 points or all superimposed
i = 0
while i < groups:
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[i])):
del segregatedF[i], centers[i], distFromCenter[i], distFromCenterAve[i]
groups -= 1
i += 1
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([centers[j][0],centers[j][1],depth[centers[j][1],centers[j][0]]])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
segregated = segregatedF
FC = FinalCenters
colourList=[(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255), (255, 255, 0), (255, 0, 255)]
# Seperate the top of each cup in pixel space
depthimg = img.copy()
depthmask = depth.copy()
# Start Cup Classification loop
for j in xrange(groups):
centx = FC[j][0]
centy = FC[j][1]
centdepth = FC[j][2]
# Choose pixel area likley to contain a cup
w = -0.08811*centdepth+103.0837
h = -0.13216*centdepth+154.6256
h = h
cup1 = depthimg[(centy-h):(centy), (centx-w):(centx+w)]
cupDepth1 = depthmask[(centy-h):(FC[j][1]), (centx-w):(centx+w)]
cup2 = depthimg[(centy):(centy+h/4), (centx-w):(centx+w)]
cupDepth2 = depthmask[(centy):(centy+h/4), (centx-w):(centx+w)]
# Create blank binary images to fill with depth thresholds
shape1 = np.zeros(cupDepth1.shape,dtype=np.uint8)
shape2 = np.zeros(cupDepth2.shape,dtype=np.uint8)
# Colour in upper threshold depths
upper = centdepth+80
lower = centdepth-30
for i in xrange(cupDepth1.shape[0]):
for k in xrange(cupDepth1.shape[1]):
if lower<cupDepth1[i,k]<upper:
shape1[i,k] = 255
# Colour in upper threshold depths
upper = centdepth+80
lower = centdepth
for i in xrange(cupDepth2.shape[0]):
for k in xrange(cupDepth2.shape[1]):
if lower<cupDepth2[i,k]<upper:
shape2[i,k] = 255
#cv2.imshow('depth',shape2)
#cv2.waitKey(0)
# Apply a median filter to the depth thresholds
shape1blur = cv2.blur(shape1,(5,5))
shape2blur = cv2.blur(shape2,(5,5))
#cv2.imshow('blur',shape2blur)
#cv2.waitKey(0)
# Determine the bouding rectangle of the largest contour in the top area
thresh1 = 200
thresh2 = 400
edges = cv2.Canny(shape1blur,thresh1,thresh2)
contours,hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cont = np.vstack(contours)
if len(cont) == 0:
print "No Top Contours"
return
else:
hull = cv2.convexHull(cont)
x,y,w1,h1 = cv2.boundingRect(hull)
# Determine the bouding rectangle of the largest contour in the bottom area
thresh21 = 200
thresh22 = 400
edges2 = cv2.Canny(shape2blur,thresh21,thresh22)
#cv2.imshow('edges',edges2)
#cv2.waitKey(0)
contours2,hierarchy2 = cv2.findContours(edges2,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours2:
cv2.drawContours(cup2,[cnt],0,colourList[j],2)
#cv2.imshow('cnt',cup2)
#cv2.waitKey(0)
cont2 = np.vstack(contours2)
if len(cont2) == 0:
print "No Bottom Contours"
return
else:
hull2 = cv2.convexHull(cont2)
x2,y2,w21,h21 = cv2.boundingRect(hull2)
#cv2.drawContours(cup2,[hull2],0,colourList[j+1],2)
#cv2.imshow('cntMax',cup2)
#cv2.waitKey(0)
# Use brounding rectangle size to determine cup type and orientation
s1 = [(centx-w)+x,(centy-h)+ y,centdepth]
s2 = [(centx-w)+x+w1,(centy-h)+ y,centdepth]
s3 = [(centx-w)+x2,(centy)+ y2 + h21,centdepth]
s4 = [(centx-w)+x2+w21,(centy)+ y2 + h21,centdepth]
top = [s1,s2]
bottom = [s3,s4]
topWorld = convertToWorldCoords(top)
bottomWorld = convertToWorldCoords(bottom)
CupTopWidth = topWorld[1][0]-topWorld[0][0]
CupBottomWidth = bottomWorld[1][0]-bottomWorld[0][0]
if CupBottomWidth > CupTopWidth:
cupOrientation = "Upsidedown"
cupFill = "Empty"
CupTopWidth = CupBottomWidth
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 84.5:
cupType = "Large"
elif CupTopWidth > 71:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
else:
cupOrientation = "Upright"
cupFill = "Unsure"
print CupTopWidth
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 79:
cupType = "Large"
elif CupTopWidth > 60:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
#Draw the top of the bounding rectangle
if cupType <> "Not a Cup":
new_cnt = hull + [int(round((centx-w),0)),int(round((centy-h),0))]
cv2.line(img,(int(round(s1[0],0)),int(round(s1[1],0))),(int(round(s2[0],0)),int(round(s2[1],0))),colourList[j])
cv2.drawContours(img,[new_cnt],0,colourList[j],2)
new_cnt2 = hull2 + [int(round((FC[j][0]-w),0)),int(round((FC[j][1]),0))]
cv2.line(img,(int(round(s3[0],0)),int(round(s3[1],0))),(int(round(s4[0],0)),int(round(s4[1],0))),colourList[j])
cv2.line(img,(int(round(s3[0],0)),int(round(s3[1],0))),(int(round(s1[0],0)),int(round(s1[1],0))),colourList[j])
cv2.line(img,(int(round(s4[0],0)),int(round(s4[1],0))),(int(round(s2[0],0)),int(round(s2[1],0))),colourList[j])
cv2.drawContours(img,[new_cnt2],0,colourList[j],2)
FinalCentersWC[j].append(cupType)
FinalCentersWC[j].append(cupOrientation)
FinalCentersWC[j].append(cupFill)
# Draw the groups
deleteList = []
for j in xrange(groups):
if len(FinalCentersWC[j]) > 3:
centerst = tuple(np.array(centers[j])+np.array([0,50]))
cv2.putText(img,str(FinalCentersWC[j]), centerst, cv2.FONT_HERSHEY_SIMPLEX, 0.3, colourList[j])
cv2.circle(img, centers[j], 10, colourList[j], -1)
cv2.circle(img, centers[j], 2, (0,0,0), -1)
for i in range(len(segregated[j])):
pt_a = (int(segregated[j][i,0]), int(segregated[j][i,1]))
cv2.circle(img, pt_a, 3, colourList[j])
else:
deleteList.append(j)
FinalFinalCentersWC = [i for j, i in enumerate(FinalCentersWC) if j not in deleteList]
if save == 1:
cv2.imwrite('ProcessedImages/ProcessedCluster'+str(ImageNo)+'.jpg', img)
if len(FinalFinalCentersWC)<>0:
print FinalFinalCentersWC
cv2.imshow("Cups Stream", img)
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0,maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append([PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
print "points list length ",len(Z)
if len(Z) < 30:
print "holy ducking shit"
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [(sum(distFromCenterAve1)/len(distFromCenterAve1))*1.0,
(sum(distFromCenterAve2)/len(distFromCenterAve2))*2.0,
(sum(distFromCenterAve3)/len(distFromCenterAve3))*3.0,
(sum(distFromCenterAve4)/len(distFromCenterAve4))*4.0,
(sum(distFromCenterAve5)/len(distFromCenterAve5))*5.0]
groups = distFromCenterAveList.index(min(distFromCenterAveList))+1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam*distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
print "-"*20
print "groups ", groups
#remove clusters that are 3 points or smaller
for i in xrange(groups):
print len(segregatedF[i])
print "std ", np.std(segregatedF[j])
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[j])):
print "HOLY F*****G SHIT"
print "-"*20
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([centers[j][0],centers[j][1],depth[centers[j][1],centers[j][0]]])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
return segregatedF, centers, img, depth, FinalCenters, FinalCentersWC, groups
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0, maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[
PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append(
[PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
print "points list length ", len(Z)
if len(Z) < 30:
print "holy ducking shit"
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [
(sum(distFromCenterAve1) / len(distFromCenterAve1)) * 1.0,
(sum(distFromCenterAve2) / len(distFromCenterAve2)) * 2.0,
(sum(distFromCenterAve3) / len(distFromCenterAve3)) * 3.0,
(sum(distFromCenterAve4) / len(distFromCenterAve4)) * 4.0,
(sum(distFromCenterAve5) / len(distFromCenterAve5)) * 5.0
]
groups = distFromCenterAveList.index(min(distFromCenterAveList)) + 1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam * distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
print "-" * 20
print "groups ", groups
#remove clusters that are 3 points or smaller
for i in xrange(groups):
print len(segregatedF[i])
print "std ", np.std(segregatedF[j])
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[j])):
print "HOLY F*****G SHIT"
print "-" * 20
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([
centers[j][0], centers[j][1], depth[centers[j][1], centers[j][0]]
])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
return segregatedF, centers, img, depth, FinalCenters, FinalCentersWC, groups
def MatchAllCluster(save, maxdist=200, filtparam=2.0):
PointsList, DisList, img, depth = MatchAllCapture(0, maxdist)
PointsClusterList = []
for i in xrange(len(PointsList)):
if depth[PointsList[i].pt[1], PointsList[i].pt[0]] <> 0 and depth[
PointsList[i].pt[1], PointsList[i].pt[0]] < 1000:
PointsClusterList.append(
[PointsList[i].pt[0], PointsList[i].pt[1]])
Z = np.array(PointsClusterList)
if len(Z) < 30:
print "No Cups"
return
# convert to np.float32
Z = np.float32(Z)
# Determine how many cups there are
segregated, centers, distFromCenter, distFromCenterAve1 = Cluster(Z, 1)
segregated, centers, distFromCenter, distFromCenterAve2 = Cluster(Z, 2)
segregated, centers, distFromCenter, distFromCenterAve3 = Cluster(Z, 3)
segregated, centers, distFromCenter, distFromCenterAve4 = Cluster(Z, 4)
segregated, centers, distFromCenter, distFromCenterAve5 = Cluster(Z, 5)
distFromCenterAveList = [
(sum(distFromCenterAve1) / len(distFromCenterAve1)) * 1.0,
(sum(distFromCenterAve2) / len(distFromCenterAve2)) * 2.0,
(sum(distFromCenterAve3) / len(distFromCenterAve3)) * 3.0,
(sum(distFromCenterAve4) / len(distFromCenterAve4)) * 4.0,
(sum(distFromCenterAve5) / len(distFromCenterAve5)) * 5.0
]
groups = distFromCenterAveList.index(min(distFromCenterAveList)) + 1
segregated, centers, distFromCenter, distFromCenterAve = Cluster(Z, groups)
#Create List for reduced points
segregatedF = []
for i in xrange(groups):
segregatedF.append([])
#Remove points which are not close to centroid
for j in xrange(groups):
for i in range(len(segregated[j])):
if distFromCenter[j][i] < filtparam * distFromCenterAve[j]:
segregatedF[j].append(segregated[j][i])
#remove clusters that are >= 3 points or all superimposed
i = 0
while i < groups:
if len(segregatedF[i]) <= 5 or np.isnan(np.std(segregatedF[i])):
del segregatedF[i], centers[i], distFromCenter[
i], distFromCenterAve[i]
groups -= 1
i += 1
for j in xrange(groups):
segregatedF[j] = np.array(segregatedF[j])
# Create a centriod depth list
FinalCenters = []
for j in xrange(groups):
FinalCenters.append([
centers[j][0], centers[j][1], depth[centers[j][1], centers[j][0]]
])
# Convert to world coordinates
FinalCentersWC = convertToWorldCoords(FinalCenters)
segregated = segregatedF
FC = FinalCenters
colourList = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255),
(255, 255, 0), (255, 0, 255)]
# Seperate the top of each cup in pixel space
depthimg = img.copy()
depthmask = depth.copy()
# Start Cup Classification loop
for j in xrange(groups):
centx = FC[j][0]
centy = FC[j][1]
centdepth = FC[j][2]
# Choose pixel area likley to contain a cup
w = -0.08811 * centdepth + 103.0837
h = -0.13216 * centdepth + 154.6256
h = h
cup1 = depthimg[(centy - h):(centy), (centx - w):(centx + w)]
cupDepth1 = depthmask[(centy - h):(FC[j][1]), (centx - w):(centx + w)]
cup2 = depthimg[(centy):(centy + h / 4), (centx - w):(centx + w)]
cupDepth2 = depthmask[(centy):(centy + h / 4), (centx - w):(centx + w)]
# Create blank binary images to fill with depth thresholds
shape1 = np.zeros(cupDepth1.shape, dtype=np.uint8)
shape2 = np.zeros(cupDepth2.shape, dtype=np.uint8)
# Colour in upper threshold depths
upper = centdepth + 80
lower = centdepth - 30
for i in xrange(cupDepth1.shape[0]):
for k in xrange(cupDepth1.shape[1]):
if lower < cupDepth1[i, k] < upper:
shape1[i, k] = 255
# Colour in upper threshold depths
upper = centdepth + 80
lower = centdepth
for i in xrange(cupDepth2.shape[0]):
for k in xrange(cupDepth2.shape[1]):
if lower < cupDepth2[i, k] < upper:
shape2[i, k] = 255
#cv2.imshow('depth',shape2)
#cv2.waitKey(0)
# Apply a median filter to the depth thresholds
shape1blur = cv2.blur(shape1, (5, 5))
shape2blur = cv2.blur(shape2, (5, 5))
#cv2.imshow('blur',shape2blur)
#cv2.waitKey(0)
# Determine the bouding rectangle of the largest contour in the top area
thresh1 = 200
thresh2 = 400
edges = cv2.Canny(shape1blur, thresh1, thresh2)
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cont = np.vstack(contours)
if len(cont) == 0:
print "No Top Contours"
return
else:
hull = cv2.convexHull(cont)
x, y, w1, h1 = cv2.boundingRect(hull)
# Determine the bouding rectangle of the largest contour in the bottom area
thresh21 = 200
thresh22 = 400
edges2 = cv2.Canny(shape2blur, thresh21, thresh22)
#cv2.imshow('edges',edges2)
#cv2.waitKey(0)
contours2, hierarchy2 = cv2.findContours(edges2, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours2:
cv2.drawContours(cup2, [cnt], 0, colourList[j], 2)
#cv2.imshow('cnt',cup2)
#cv2.waitKey(0)
cont2 = np.vstack(contours2)
if len(cont2) == 0:
print "No Bottom Contours"
return
else:
hull2 = cv2.convexHull(cont2)
x2, y2, w21, h21 = cv2.boundingRect(hull2)
#cv2.drawContours(cup2,[hull2],0,colourList[j+1],2)
#cv2.imshow('cntMax',cup2)
#cv2.waitKey(0)
# Use brounding rectangle size to determine cup type and orientation
s1 = [(centx - w) + x, (centy - h) + y, centdepth]
s2 = [(centx - w) + x + w1, (centy - h) + y, centdepth]
s3 = [(centx - w) + x2, (centy) + y2 + h21, centdepth]
s4 = [(centx - w) + x2 + w21, (centy) + y2 + h21, centdepth]
top = [s1, s2]
bottom = [s3, s4]
topWorld = convertToWorldCoords(top)
bottomWorld = convertToWorldCoords(bottom)
CupTopWidth = topWorld[1][0] - topWorld[0][0]
CupBottomWidth = bottomWorld[1][0] - bottomWorld[0][0]
if CupBottomWidth > CupTopWidth:
cupOrientation = "Upsidedown"
cupFill = "Empty"
CupTopWidth = CupBottomWidth
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 84.5:
cupType = "Large"
elif CupTopWidth > 71:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
else:
cupOrientation = "Upright"
cupFill = "Unsure"
print CupTopWidth
if CupTopWidth > 100:
cupType = "Not a Cup"
elif CupTopWidth > 79:
cupType = "Large"
elif CupTopWidth > 60:
cupType = "Medium"
elif CupTopWidth > 50:
cupType = "Small"
else:
cupType = "Not a Cup"
#Draw the top of the bounding rectangle
if cupType <> "Not a Cup":
new_cnt = hull + [
int(round((centx - w), 0)),
int(round((centy - h), 0))
]
cv2.line(img, (int(round(s1[0], 0)), int(round(s1[1], 0))),
(int(round(s2[0], 0)), int(round(s2[1], 0))),
colourList[j])
cv2.drawContours(img, [new_cnt], 0, colourList[j], 2)
new_cnt2 = hull2 + [
int(round((FC[j][0] - w), 0)),
int(round((FC[j][1]), 0))
]
cv2.line(img, (int(round(s3[0], 0)), int(round(s3[1], 0))),
(int(round(s4[0], 0)), int(round(s4[1], 0))),
colourList[j])
cv2.line(img, (int(round(s3[0], 0)), int(round(s3[1], 0))),
(int(round(s1[0], 0)), int(round(s1[1], 0))),
colourList[j])
cv2.line(img, (int(round(s4[0], 0)), int(round(s4[1], 0))),
(int(round(s2[0], 0)), int(round(s2[1], 0))),
colourList[j])
cv2.drawContours(img, [new_cnt2], 0, colourList[j], 2)
FinalCentersWC[j].append(cupType)
FinalCentersWC[j].append(cupOrientation)
FinalCentersWC[j].append(cupFill)
# Draw the groups
deleteList = []
for j in xrange(groups):
if len(FinalCentersWC[j]) > 3:
centerst = tuple(np.array(centers[j]) + np.array([0, 50]))
cv2.putText(img, str(FinalCentersWC[j]), centerst,
cv2.FONT_HERSHEY_SIMPLEX, 0.3, colourList[j])
cv2.circle(img, centers[j], 10, colourList[j], -1)
cv2.circle(img, centers[j], 2, (0, 0, 0), -1)
for i in range(len(segregated[j])):
pt_a = (int(segregated[j][i, 0]), int(segregated[j][i, 1]))
cv2.circle(img, pt_a, 3, colourList[j])
else:
deleteList.append(j)
FinalFinalCentersWC = [
i for j, i in enumerate(FinalCentersWC) if j not in deleteList
]
if save == 1:
cv2.imwrite('ProcessedImages/ProcessedCluster' + str(ImageNo) + '.jpg',
img)
if len(FinalFinalCentersWC) <> 0:
print FinalFinalCentersWC
cv2.imshow("Cups Stream", img)
