def ground(self, imagefile, segments, _=None):
"""asks the user to label each segment as either a character or "<" for unknown"""
print(
"For each shown segment, please write the character that it represents, or spacebar if it's not a "
"character. To undo a classification, press backspace. Press ESC when completed, arrow keys to move"
)
i = 0
if imagefile.is_grounded:
classes = classes_from_numpy(imagefile.ground.classes)
segments = imagefile.ground.segments
else:
classes = [BLANK_CLASS] * len(segments)
done = False
allowed_chars = list(
map(ord,
string.digits + string.ascii_letters + string.punctuation))
while not done:
image = imagefile.image.copy()
draw_segments(image, [segments[i]])
draw_classes(image, segments, classes)
key = show_image_and_wait_for_key(image, "segment " + str(i))
if key == 27: # ESC
break
elif key == 8: # backspace
classes[i] = BLANK_CLASS
i += 1
elif key == 32: # space
classes[i] = NOT_A_SEGMENT
i += 1
elif key == 65361: # <-
i -= 1
elif key == 65363: # ->
i += 1
elif key in allowed_chars:
classes[i] = unichr(key)
i += 1
if i >= len(classes):
i = 0
if i < 0:
i = len(classes) - 1
classes = numpy.array(classes)
is_segment = classes != NOT_A_SEGMENT
classes = classes[is_segment]
segments = segments[is_segment]
classes = list(classes)
classes = classes_to_numpy(classes)
print("classified ",
numpy.count_nonzero(classes != classes_to_numpy(BLANK_CLASS)),
"characters out of", max(classes.shape))
imagefile.set_ground(segments, classes)
def ground(self, imagefile, segments, _=None):
'''asks the user to label each segment as either a character or "<" for unknown'''
print '''For each shown segment, please write the character that it represents, or spacebar if it's not a character. To undo a classification, press backspace. Press ESC when completed, arrow keys to move'''
i = 0
if imagefile.isGrounded():
classes = classes_from_numpy(imagefile.ground.classes)
segments = imagefile.ground.segments
else:
classes = [BLANK_CLASS] * len(
segments
) #char(10) is newline. it represents a non-assigned label, and will b filtered
done = False
allowed_chars = map(
ord, string.digits + string.letters + string.punctuation)
while not done:
image = imagefile.image.copy()
draw_segments(image, [segments[i]])
draw_classes(image, segments, classes)
key = show_image_and_wait_for_key(
image, "segment " + str(i), return_arrow_keys=True)
if key == 27: #ESC
break
elif key == 8: #backspace
classes[i] = BLANK_CLASS
i += 1
elif key == 32: #space
classes[i] = NOT_A_SEGMENT
i += 1
elif key == 65361: #<-
i -= 1
elif key == 65363: #->
i += 1
elif key in allowed_chars:
classes[i] = unichr(key)
i += 1
if i >= len(classes):
i = 0
if i < 0:
i = len(classes) - 1
classes = numpy.array(classes)
is_segment = classes != NOT_A_SEGMENT
classes = classes[is_segment]
segments = segments[is_segment]
classes = list(classes)
classes = classes_to_numpy(classes)
print "classified ", numpy.count_nonzero(
classes != classes_to_numpy(BLANK_CLASS)
), "characters out of", max(classes.shape)
imagefile.set_ground(segments, classes)
def ground( self, imagefile, segments, text ):
'''tries to grounds from a simple string'''
text= unicode( text )
text= filter( lambda c: c in string.ascii_letters+string.digits, list(text))
if len(segments)!=len(text):
raise Exception( "segments/text length mismatch")
classes= classes_to_numpy( text )
imagefile.set_ground( segments, classes)
def ground(self, imagefile, segments, text):
"""tries to grounds from a simple string"""
text = unicode(text)
text = filter(lambda c: c in string.ascii_letters + string.digits, list(text))
if len(segments) != len(text):
raise ValueError("segments/text length mismatch")
classes = classes_to_numpy(text)
imagefile.set_ground(segments, classes)
def ground(self, imagefile, segments, text):
"""tries to grounds from a simple string"""
text = text_type(text)
text = [c for c in text if c in string.ascii_letters + string.digits]
if len(segments) != len(text):
raise ValueError("segments/text length mismatch")
classes = classes_to_numpy(text)
imagefile.set_ground(segments, classes)
def read_boxfile(path):
classes = []
segments = []
with open(path) as f:
for line in f:
s = line.split(" ")
assert len(s) == 6
assert s[5] == '0\n'
classes.append(s[0].decode('utf-8'))
segments.append(map(int, s[1:5]))
return classes_to_numpy(classes), segments_to_numpy(segments)
def read_boxfile( path ):
classes= []
segments= []
with open(path) as f:
for line in f:
s= line.split(" ")
assert len(s)==6
assert s[5]=='0\n'
classes.append( s[0].decode('utf-8') )
segments.append( map(int, s[1:5]))
return classes_to_numpy(classes), segments_to_numpy(segments)
def read_boxfile(path):
classes = []
segments = []
with io.open(path, encoding="utf-8") as f:
for line in f:
s = line.split(" ")
assert len(s) == 6
assert s[5] == '0\n'
classes.append(s[0])
segments.append(list(map(int, s[1:5])))
return classes_to_numpy(classes), segments_to_numpy(segments)
def ground( self, imagefile, segments, text ):
'''tries to grounds from a simple string'''
text= unicode( text )
text= filter( lambda c: c in string.ascii_letters+string.digits, list(text))
if len(segments)!=len(text):
# if its an exact multiple, then repeat it as needed
if len(segments) % len(text) == 0:
utext = []
while len(segments) > len(utext):
utext = utext + text
text= utext
else:
raise Exception( "segments/text length mismatch in file " + imagefile.image_path)
classes= classes_to_numpy( text )
imagefile.set_ground( segments, classes, write_file=True)
def ground(self, imagefile, segments, _=None):
classes = []
character = ""
print "Found %s segments to ground." % len(segments)
print "Type 'exit' to stop grounding the file."
print "Type ' ' for anything that is not a character."
print "Grounding will exit automatically after all segments."
print "Going back to a previous segment is not possible at this time."
for num in range(len(segments)):
while len(character) != 1:
character = raw_input(
"Please enter the value for segment #%s: " % (num + 1))
if character == "exit":
break
if len(character) != 1:
print "That is not a single character. Please try again."
if character == " ":
classes.append(NOT_A_SEGMENT)
else:
classes.append(character)
character = ""
classes = classes_to_numpy(classes)
imagefile.set_ground(segments, classes)
def _filter_unclassified( features, classes ):
classified= (classes != classes_to_numpy(BLANK_CLASS)).reshape(-1)
return features[classified], classes[classified]
def _filter_unclassified(features, classes):
classified = (classes != classes_to_numpy(BLANK_CLASS)).reshape(-1)
return features[classified], classes[classified]
