def on_touch_up(self, touch):
global userShape
touch.ud['line'].points
userShape = downsampleShape(userShape,
numPoints_shapeModeler,
xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(userShape)
for i in range(len(wordToLearn)):
if (shapeCentre[0] > (self.width / len(wordToLearn)) * i):
shapeIndex_demoFor = i
shapeType = wordManager.shapeAtIndexInCurrentCollection(
shapeIndex_demoFor)
print('Received demo for letter ' + shapeType)
userShape = np.reshape(userShape, (-1, 1))
#explicitly make it 2D array with only one column
shape = wordManager.respondToDemonstration(shapeIndex_demoFor,
userShape)
wordManager.save_all(shapeIndex_demoFor)
userShape = []
self.canvas.remove(touch.ud['line'])
showShape(shape, shapeIndex_demoFor)
def on_touch_up(self, touch):
global userShape
wordToLearn = wordManager.getCurrentCollection()
if len(userShape) < 5:
closeFigures(figuresToClose = range(1,len(wordToLearn)+1))
wordToLearn = raw_input("Next word:\n")
newWord(wordToLearn)
userInputCapture.display_grid(numColumns = len(wordToLearn))
else:
userShape = downsampleShape(userShape,numPoints_shapeModeler,xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(userShape)
for i in range(len(wordToLearn)):
if(shapeCentre[0] > (self.width/len(wordToLearn))*i):
shapeIndex_demoFor = i
shapeType = wordManager.shapeAtIndexInCurrentCollection(shapeIndex_demoFor)
print('Received demo for letter ' + shapeType)
userShape = numpy.reshape(userShape, (-1, 1)); #explicitly make it 2D array with only one column
userShape = ShapeModeler.normaliseShapeHeight(numpy.array(userShape))
shape = wordManager.respondToDemonstration(shapeType, userShape)
userShape = []
self.canvas.remove(touch.ud['line'])
if shape != -1:
showShape(shape, shapeIndex_demoFor)
def on_touch_up(self, touch):
global userShape
touch.ud['line'].points
userShape = downsampleShape(userShape,numPoints_shapeModeler,xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(userShape)
shapeType = letter
print('Received reference for letter ' + shapeType)
userShape = numpy.reshape(userShape, (-1, 1));
userShape = ShapeModeler.normaliseShapeHeight(numpy.array(userShape))
filename = dataSetFile
print('saving in'+filename)
# scan the dataset :
lines = []
try:
with open(filename, 'r') as f:
lines.append(f.readline())
nb_samples = int(lines[0].strip())
for i in range(nb_samples+1):
lines.append(f.readline())
except IOError:
raise RuntimeError("no reading permission for file"+filename)
nb_samples+=1
# past the dataset :
try:
with open(filename, 'w') as f:
f.write('nb_sample:\n')
f.write('%i\n'%nb_samples)
f.write('nb_pts:\n')
f.write('70\n')
f.write('ref:\n')
f.write(' '.join(map(str,userShape.T[0]))+'\n')
f.write('...\n')
for i in range(nb_samples-1):
f.write(lines[i+2])
except IOError:
raise RuntimeError("no writing permission for file"+filename)
userShape = []
self.canvas.remove(touch.ud['line'])
def onUserDrawnShapeReceived(path, shapePreprocessingMethod, positionToShapeMappingMethod):
global activeShapeForDemonstration_type
#preprocess to turn multiple strokes into one path
if(shapePreprocessingMethod == 'longestStroke'):
path = processShape_longestStroke(strokes);
else:
path = processShape_firstStroke(strokes);
#identify type of shape which demonstration was for
location = ShapeModeler.getShapeCentre(path);
if(positionToShapeMappingMethod == 'basedOnColumnOfScreen'):
shapeType_demoFor = getShapeCode_basedOnColumnOfScreen(location);
elif(positionToShapeMappingMethod == 'basedOnRowOfScreen'):
shapeType_demoFor = getShapeCode_basedOnColumnOfScreen(location);
elif(positionToShapeMappingMethod == 'basedOnShapeAtPosition'):
shapeType_demoFor = getShapeCode_basedOnShapeAtPosition(location);
else:
shapeType_demoFor = getShapeCode_basedOnClosestShapeToPosition(location);
#block the space from robot use
try:
display_shape_at_location = rospy.ServiceProxy('display_shape_at_location', displayShapeAtLocation);
request = displayShapeAtLocationRequest();
request.shape_type_code = shapeType_demoFor;
request.location.x = location[0];
request.location.y = location[1];
#todo: allow for blocking different sized shapes
response = display_shape_at_location(request);
result = response.success;
#todo: do something if unsuccessful
except rospy.ServiceException, e:
rospy.logerr("Service call failed: %s",e);
def on_touch_up(self, touch):
global userShape
touch.ud['line'].points
userShape = downsampleShape(userShape,numPoints_shapeModeler,xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(userShape)
for i in range(len(wordToLearn)):
if(shapeCentre[0] > (self.width/len(wordToLearn))*i):
shapeIndex_demoFor = i
shapeType = wordManager.shapeAtIndexInCurrentCollection(shapeIndex_demoFor)
print('Received demo for letter ' + shapeType)
userShape = np.reshape(userShape, (-1, 1)); #explicitly make it 2D array with only one column
shape = wordManager.respondToDemonstration(shapeIndex_demoFor, userShape)
wordManager.save_all(shapeIndex_demoFor)
userShape = []
self.canvas.remove(touch.ud['line'])
showShape(shape, shapeIndex_demoFor)
def find_letter(self, path):
x,y = ShapeModeler.getShapeCentre(path)
return self.closest_letter(x, y)
def on_touch_up(self, touch):
global userShape
word = []
total_score = 0
rest = userShape
scan = downsampleShape(userShape,
numPoints_shapeModeler,
xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(scan)
scan = np.reshape(scan, (-1, 1))
#explicitly make it 2D array with only one column
scan = ShapeModeler.normaliseShapeHeight(np.array(scan))
scores = np.array(separator(scan))
scores = scores / np.max(scores)
scores = scores * np.abs(scores)
scores[scores < 0.1] = 0
indices = np.array(range(len(scores)))
sep = indices[scores > 0]
#print zip(sep[:-1],sep[1:])
for i in sep:
x = scan[i]
if len(scan[scan[i:numPoints_shapeModeler] < x]) > 0:
scores[i] = 0
if len(scan[scan[:i] > x]) > 0:
scores[i] = 0
u = True
while u:
u = False
sep = indices[scores > 0]
for i, j in zip(sep[:-1], sep[1:]):
if j - i < 5:
u = True
if scores[i] > scores[j]:
scores[j] = 0
else:
scores[i] = 0
plt.clf()
ShapeModeler.showShape_score(scan, scores)
word = []
sep = indices[scores > 0]
for i, j in zip(sep[:-1], sep[1:]):
shape = scan[i:j +
1].T.tolist()[0] + scan[numPoints_shapeModeler +
i:numPoints_shapeModeler + j +
1].T.tolist()[0]
shape = downsampleShape(shape, numPoints_shapeModeler)
shape = np.reshape(shape, (-1, 1))
shape = ShapeModeler.normaliseShapeHeight(np.array(shape))
best_letter = '?'
errors = {}
values = []
for letter in abc:
space = spaces[letter]
error = space.getMinDist(shape)
errors[letter] = error
values.append(error)
best_letter = min(errors, key=errors.get)
word.append(best_letter)
print 'found ' + str(word)
print '######################'
print ''
userShape = []
self.canvas.remove(touch.ud['line'])
def on_touch_up(self, touch):
global userShape
word = []
total_score =0
rest = userShape
scan = downsampleShape(userShape, numPoints_shapeModeler,xyxyFormat=True)
shapeCentre = ShapeModeler.getShapeCentre(scan)
scan = np.reshape(scan, (-1, 1)); #explicitly make it 2D array with only one column
scan = ShapeModeler.normaliseShapeHeight(np.array(scan))
scores = np.array(separator(scan))
scores = scores/np.max(scores)
scores = scores*np.abs(scores)
scores[scores<0.1]=0
indices = np.array(range(len(scores)))
sep = indices[scores>0]
#print zip(sep[:-1],sep[1:])
for i in sep:
x = scan[i]
if len(scan[scan[i:numPoints_shapeModeler]<x])>0:
scores[i]=0
if len(scan[scan[:i]>x])>0:
scores[i]=0
u = True
while u:
u = False
sep = indices[scores>0]
for i,j in zip(sep[:-1],sep[1:]):
if j-i<5:
u = True
if scores[i]>scores[j]:
scores[j] = 0
else:
scores[i] = 0
plt.clf()
ShapeModeler.showShape_score(scan,scores)
word = []
sep = indices[scores>0]
for i,j in zip(sep[:-1],sep[1:]):
shape = scan[i:j+1].T.tolist()[0] + scan[numPoints_shapeModeler+i:numPoints_shapeModeler+j+1].T.tolist()[0]
shape = downsampleShape(shape, numPoints_shapeModeler)
shape = np.reshape(shape,(-1,1))
shape = ShapeModeler.normaliseShapeHeight(np.array(shape))
best_letter = '?'
errors = {}
values = []
for letter in abc:
space = spaces[letter]
error = space.getMinDist(shape)
errors[letter] = error
values.append(error)
best_letter = min(errors, key = errors.get)
word.append(best_letter)
print 'found ' + str(word)
print '######################'
print ''
userShape = []
self.canvas.remove(touch.ud['line'])
