def __init__(self, truth='truth', width=30, height=30, classifier=None):
super(LabelerWindow, self).__init__()
self.dataset = DataSet(truth, width, height)
""" The ground truth images
:type: labeler.dataset.DataSet
"""
self.classifier = classifier or Classifier('cars', (width, height))
""" The classifier we are building"""
self.raster = None
"""The GDAL dataset we are viewing.
:type ds: gdal.Dataset
"""
self.geo_x = None
""" The geographic x (longitude) of the center of the view """
self.geo_y = None
""" The geographic y (latitude) of the center of the view """
self.scale = 1.0
""" The number of meters per pixel in the view """
self.buffer = None
""" The raster data in the visible region (possibly padded with zeros)
:type buffer: numpy.ndarray
"""
self.random_walk = True
""" Whether to look for the next sample within the current window (True) or within the entire raster (False)
:type: Boolean
"""
self.mode = MODE_POSITIVES
def __init__(self, truth='truth', width=30, height=30, classifier = None):
super(LabelerWindow, self).__init__()
self.dataset = DataSet(truth, width, height)
""" The ground truth images
:type: labeler.dataset.DataSet
"""
self.classifier = classifier or Classifier('cars', (width, height))
""" The classifier we are building"""
self.raster = None
"""The GDAL dataset we are viewing.
:type ds: gdal.Dataset
"""
self.geo_x = None
""" The geographic x (longitude) of the center of the view """
self.geo_y = None
""" The geographic y (latitude) of the center of the view """
self.scale = 1.0
""" The number of meters per pixel in the view """
self.buffer = None
""" The raster data in the visible region (possibly padded with zeros)
:type buffer: numpy.ndarray
"""
self.random_walk = True
""" Whether to look for the next sample within the current window (True) or within the entire raster (False)
:type: Boolean
"""
self.mode = MODE_POSITIVES
class LabelerWindow(QtGui.QWidget):
def __init__(self, truth='truth', width=30, height=30, classifier = None):
super(LabelerWindow, self).__init__()
self.dataset = DataSet(truth, width, height)
""" The ground truth images
:type: labeler.dataset.DataSet
"""
self.classifier = classifier or Classifier('cars', (width, height))
""" The classifier we are building"""
self.raster = None
"""The GDAL dataset we are viewing.
:type ds: gdal.Dataset
"""
self.geo_x = None
""" The geographic x (longitude) of the center of the view """
self.geo_y = None
""" The geographic y (latitude) of the center of the view """
self.scale = 1.0
""" The number of meters per pixel in the view """
self.buffer = None
""" The raster data in the visible region (possibly padded with zeros)
:type buffer: numpy.ndarray
"""
self.random_walk = True
""" Whether to look for the next sample within the current window (True) or within the entire raster (False)
:type: Boolean
"""
self.mode = MODE_POSITIVES
def open_raster(self, datasource):
"""Load a raster dataset to display it in this window.
This can be a single GeoTIFF (or other GDAL file) or a VRT file that mosaics several rasters.
:param datasource: The GDAL datasource, or a description (filename)
:type datasource: str | gdal.DataSet
"""
if isinstance(datasource, gdal.Dataset):
self.raster = datasource
else:
self.raster = gdal.Open(datasource)
def visible_region(self, geo_x=None, geo_y=None, width=None, height=None, scale=None):
if self.raster is None: return
if geo_x is None: geo_x = self.geo_x
if geo_y is None: geo_y = self.geo_y
if width is None: width = self.width()
if height is None: height = self.height()
if scale is None: scale = self.scale
assert geo_x is not None and geo_y is not None
geo_transform = self.raster.GetGeoTransform() # col, row ---> lon, lat
ok, inverse_geo_transform = gdal.InvGeoTransform(geo_transform) # lon, lat ---> col, row
col, row = gdal.ApplyGeoTransform(inverse_geo_transform, self.geo_x, self.geo_y)
# col, row are now where we are centered in the image
# I want scale=1 to be viewing the image at 1m per pixel.
# The image may be sampled at a different resolution, fetch the meters-per-pixel from the geo-transform
# Also, the pixels generally are not square.
meters_x = geo_transform[1]
meters_y = geo_transform[5]
# Determine the size and offset within the raster
x_size = abs(int(width * meters_x / scale))
y_size = abs(int(height * meters_y / scale))
x_off = int(col - x_size/2)
y_off = int(row - y_size/2)
return x_off, y_off, x_size, y_size
def load_buffer(self, geo_x=None, geo_y=None, width=None, height=None, scale=None):
if self.raster is None: return
x_off, y_off, x_size, y_size = self.visible_region(geo_x, geo_y, width, height, scale)
# Load the data (gdal may keep it cached / memory mapped for us)
clipped_x = max(0, -x_off)
clipped_y = max(0, -y_off)
clipped_xmax = min(self.raster.RasterXSize, x_off + x_size)
clipped_ymax = min(self.raster.RasterYSize, y_off + y_size)
clipped_width = clipped_xmax - clipped_x - x_off
clipped_height = clipped_ymax - clipped_y - y_off
if clipped_width < 0 or clipped_height < 0 or clipped_x >= x_size or clipped_y >= y_size:
return numpy.zeros((y_size, x_size, self.raster.RasterCount), dtype=numpy.uint8)
data = self.raster.ReadAsArray(x_off + clipped_x, y_off + clipped_y, clipped_width, clipped_height) \
# Convert data into visible representation
data = data.transpose(1, 2, 0)
if not data.dtype == numpy.uint8:
data += data.min()
data *= 255/data.max()
data = data.astype(numpy.uint8)
if not data.shape[:2] == (y_size, x_size):
padding = numpy.zeros((y_size, x_size, self.raster.RasterCount), dtype=numpy.uint8)
padding[clipped_y:clipped_y+clipped_height, clipped_x:clipped_x+clipped_width, :] = data
data = padding
return data
def center_of_raster(self):
geo_transform = self.raster.GetGeoTransform() # col, row ---> lon, lat
x, y = gdal.ApplyGeoTransform(geo_transform, self.raster.RasterXSize / 2, self.raster.RasterYSize / 2)
return x, y
def look_at(self, geo_x, geo_y, scale=None):
self.geo_x = geo_x
self.geo_y = geo_y
if scale is not None: self.scale = scale
self.update_raster()
def window_to_raster(self, x, y):
rx, ry, rw, rh = self.visible_region()
x = rx + rw*x/self.width()
y = ry + rh*y/self.height()
return x, y
def raster_to_window(self, x, y):
rx, ry, rw, rh = self.visible_region()
x = -rx + x*self.width()/rw
y = -ry + y*self.height()/rh
return x, y
def raster_to_geo(self, x, y):
geo_transform = self.raster.GetGeoTransform() # col, row ---> lon, lat
x, y = gdal.ApplyGeoTransform(geo_transform, x, y)
return x, y
def geo_to_raster(self, x, y):
geo_transform = self.raster.GetGeoTransform() # col, row ---> lon, lat
inv_transform = gdal.InvGeoTransform(geo_transform)
x, y = gdal.ApplyGeoTransform(inv_transform, x, y)
return x, y
def geo_to_window(self, x, y):
return self.raster_to_window(self.geo_to_raster(x, y))
def window_to_geo(self, x, y):
return self.raster_to_geo(*self.window_to_raster(x,y))
def update_raster(self):
self.buffer = self.load_buffer()
self.update()
def load_classifier(self):
try:
if os.path.exists('training.pkl'):
with open('training.pkl', 'rb') as f:
self.classifier.load(f)
except:
self.classifier.rebuild(self.dataset)
def paintEvent(self, QPaintEvent):
super(LabelerWindow, self).paintEvent(QPaintEvent)
p = QtGui.QPainter(self)
image = ImageQt(Image.fromarray(self.buffer))
image = image.scaled(self.width(), self.height())
p.drawImage(0, 0, image)
# Now for the HUD
# -> Draw green cross-hairs
old_pen = p.pen()
new_pen = QPen()
new_pen.setColor(Qt.green)
new_pen.setStyle(Qt.DotLine)
new_pen.setWidth(1)
p.setPen(new_pen)
p.drawLine(0, self.height()/2, self.width(), self.height()/2)
p.drawLine(self.width()/2, 0, self.width()/2, self.height())
p.setPen(old_pen)
# -> Show help for keystrokes
help = "[X] Pos. [C] Neg. [UP] Zoom in [DN] Zoom Out [LT] Undo Last [RT] Ignore [LMB] Move "
p.fillRect(0, 0, self.width(), p.fontMetrics().height()*1.5, Qt.gray)
p.drawText(0, p.fontMetrics().height(), help)
def resizeEvent(self, QResizeEvent):
self.update_raster()
def get_sample(self):
s = self.load_buffer(width=self.dataset.width, height=self.dataset.height, scale=0.25).astype(float)/255.0
return s
def geo_xy(self):
return self.geo_x, self.geo_y
def goto_next_sample(self):
if self.mode == MODE_POSITIVES:
self.goto_next_positive()
elif self.mode == MODE_NEGATIVES:
self.goto_next_negative()
else:
self.goto_next_uncertain()
def get_random_xys(self, n):
if self.random_walk:
return self.get_random_in_view(n)
else:
return self.get_random_in_raster(n)
def get_random_in_raster(self, n):
width, height = self.classifier.shape
x = numpy.random.random_integers(int(ceil(width/2)), int(floor(self.raster.RasterXSize - width / 2)), n)
y = numpy.random.random_integers(int(ceil(height/2)), int(floor(self.raster.RasterYSize - height / 2)), n)
return zip(x, y)
def get_random_in_view(self, n):
x0, y0, w, h = self.visible_region()
x1, y1 = x0+w, y0+h
width, height = self.classifier.shape
rx, ry = width / 2, height/2
x0 = int(ceil(max(x0, rx)))
y0 = int(ceil(max(y0, ry)))
x1 = int(floor(min(self.raster.RasterXSize-rx, x1)))
y1 = int(floor(min(self.raster.RasterYSize-ry, y1)))
x = numpy.random.random_integers(x0, x1, n)
y = numpy.random.random_integers(y0, y1, n)
return zip(x, y)
def goto_next_uncertain(self):
x, y = zip(*self.get_random_xys(100))
samples = []
for i in range(len(x)):
self.look_at(*self.raster_to_geo(x[i], y[i]))
s = self.get_sample()
samples.append(s)
i, w = self.classifier.find_least_certain(samples)
print "Least confident:", w
self.look_at(*self.raster_to_geo(x[i], y[i]))
def goto_next_positive(self, thresh=-1):
best = -1
best_x, best_y = self.geo_x, self.geo_y
for i, (x, y) in enumerate(self.get_random_xys(100)):
self.look_at(*self.raster_to_geo(x, y))
s = self.get_sample()
w = self.classifier.classify(s)
if w > thresh:
best = i
best_x, best_y = x, y
thresh = w
self.look_at(*self.raster_to_geo(best_x, best_y))
def goto_next_negative(self, thresh=0.5):
for i, (x, y) in enumerate(self.get_random_xys(100)):
self.look_at(*self.raster_to_geo(x, y))
s = self.get_sample()
w = self.classifier.classify(s)
if w < thresh:
print "Negative sample", w
break
def keyPressEvent(self, QKeyEvent):
key = QKeyEvent.key()
x, y = self.geo_x, self.geo_y
def undo_positive():
self.look_at(x, y)
self.dataset.delete(self.get_sample(), label='positive')
self.classifier.positives.pop()
self.classifier.save()
def undo_negative():
self.look_at(x, y)
self.dataset.delete(self.get_sample(), label='negative')
self.classifier.negatives.pop()
self.classifier.save()
if key == Qt.Key_X:
print "Mark Pos. ", self.raster.GetDescription(), self.geo_xy()
self.classifier.add_positive(self.get_sample())
self.dataset.create(self.get_sample(), label='positive', meta=dict(pos=self.geo_xy())).save()
self.classifier.save()
undos.append(undo_positive)
self.goto_next_sample()
elif key == Qt.Key_C:
print "Mark Neg. ", self.raster.GetDescription(), self.geo_xy()
self.classifier.add_negative(self.get_sample())
self.dataset.create(self.get_sample(), label='negative', meta=dict(pos=self.geo_xy())).save()
self.classifier.save()
undos.append(undo_negative)
self.goto_next_sample()
elif key == Qt.Key_Left:
print len(undos)
if len(undos) > 0:
undos[-1]()
undos.pop()
elif key == Qt.Key_Right:
print "Next Sample (do not save current)"
self.goto_next_sample()
elif key == Qt.Key_Up:
self.scale *= sqrt(2.)
self.update_raster()
elif key == Qt.Key_Down:
self.scale /= sqrt(2.)
self.update_raster()
elif key == Qt.Key_S:
self.save_classifier()
elif key == Qt.Key_R:
self.classifier.rebuild(self.dataset)
self.save_classifier()
self.goto_next_sample()
elif key == Qt.Key_L:
self.load_classifier()
elif key == Qt.Key_P:
self.mode = MODE_POSITIVES
self.goto_next_sample()
elif key == Qt.Key_N:
self.mode = MODE_NEGATIVES
self.goto_next_sample()
elif key == Qt.Key_U:
self.mode = MODE_UNCERTAIN
self.goto_next_sample()
elif key == Qt.Key_V:
s = self.get_sample()
self.visualize_classification(s)
def save_classifier(self):
self.classifier.save(file('training.pkl', 'wb'))
def visualize_classification(self, s):
import pylab
i_p, w_p = self.classifier.find_similar_positive(s)
i_n, w_n = self.classifier.find_similar_negative(s)
pylab.figure()
pylab.subplot(231)
if i_n >= 0:
pylab.imshow(self.classifier.negatives[i_n])
pylab.title('negative')
pylab.subplot(232)
pylab.imshow(s)
pylab.title('query')
pylab.subplot(233)
if i_p >= 0:
pylab.imshow(self.classifier.positives[i_p])
pylab.title('positive')
pylab.subplot(234)
if i_n >= 0:
pylab.imshow((abs(self.classifier.negatives[i_n].astype(float) - s) ** 2).mean(2), cmap=pylab.cm.gray,
vmax=255 ** 2)
pylab.subplot(236)
if i_p >= 0:
pylab.imshow((abs(self.classifier.positives[i_p].astype(float) - s) ** 2).mean(2), cmap=pylab.cm.gray,
vmax=255 ** 2)
pylab.show()
def mousePressEvent(self, QMouseEvent):
x, y = QMouseEvent.x(), QMouseEvent.y()
button = QMouseEvent.button()
if button == Qt.LeftButton:
rx, ry = self.window_to_raster(x, y)
gx, gy = self.raster_to_geo(rx, ry)
self.look_at(gx, gy)
QtGui.QCursor.setPos(self.mapToGlobal(self.rect().center()))
class LabelerWindow(QtGui.QWidget):
def __init__(self, truth='truth', width=30, height=30, classifier=None):
super(LabelerWindow, self).__init__()
self.dataset = DataSet(truth, width, height)
""" The ground truth images
:type: labeler.dataset.DataSet
"""
self.classifier = classifier or Classifier('cars', (width, height))
""" The classifier we are building"""
self.raster = None
"""The GDAL dataset we are viewing.
:type ds: gdal.Dataset
"""
self.geo_x = None
""" The geographic x (longitude) of the center of the view """
self.geo_y = None
""" The geographic y (latitude) of the center of the view """
self.scale = 1.0
""" The number of meters per pixel in the view """
self.buffer = None
""" The raster data in the visible region (possibly padded with zeros)
:type buffer: numpy.ndarray
"""
self.random_walk = True
""" Whether to look for the next sample within the current window (True) or within the entire raster (False)
:type: Boolean
"""
self.mode = MODE_POSITIVES
def open_raster(self, datasource):
"""Load a raster dataset to display it in this window.
This can be a single GeoTIFF (or other GDAL file) or a VRT file that mosaics several rasters.
:param datasource: The GDAL datasource, or a description (filename)
:type datasource: str | gdal.DataSet
"""
if isinstance(datasource, gdal.Dataset):
self.raster = datasource
else:
self.raster = gdal.Open(datasource)
def visible_region(self,
geo_x=None,
geo_y=None,
width=None,
height=None,
scale=None):
if self.raster is None: return
if geo_x is None: geo_x = self.geo_x
if geo_y is None: geo_y = self.geo_y
if width is None: width = self.width()
if height is None: height = self.height()
if scale is None: scale = self.scale
assert geo_x is not None and geo_y is not None
geo_transform = self.raster.GetGeoTransform(
) # col, row ---> lon, lat
ok, inverse_geo_transform = gdal.InvGeoTransform(
geo_transform) # lon, lat ---> col, row
col, row = gdal.ApplyGeoTransform(inverse_geo_transform, self.geo_x,
self.geo_y)
# col, row are now where we are centered in the image
# I want scale=1 to be viewing the image at 1m per pixel.
# The image may be sampled at a different resolution, fetch the meters-per-pixel from the geo-transform
# Also, the pixels generally are not square.
meters_x = geo_transform[1]
meters_y = geo_transform[5]
# Determine the size and offset within the raster
x_size = abs(int(width * meters_x / scale))
y_size = abs(int(height * meters_y / scale))
x_off = int(col - x_size / 2)
y_off = int(row - y_size / 2)
return x_off, y_off, x_size, y_size
def load_buffer(self,
geo_x=None,
geo_y=None,
width=None,
height=None,
scale=None):
if self.raster is None: return
x_off, y_off, x_size, y_size = self.visible_region(
geo_x, geo_y, width, height, scale)
# Load the data (gdal may keep it cached / memory mapped for us)
clipped_x = max(0, -x_off)
clipped_y = max(0, -y_off)
clipped_xmax = min(self.raster.RasterXSize, x_off + x_size)
clipped_ymax = min(self.raster.RasterYSize, y_off + y_size)
clipped_width = clipped_xmax - clipped_x - x_off
clipped_height = clipped_ymax - clipped_y - y_off
if clipped_width < 0 or clipped_height < 0 or clipped_x >= x_size or clipped_y >= y_size:
return numpy.zeros((y_size, x_size, self.raster.RasterCount),
dtype=numpy.uint8)
data = self.raster.ReadAsArray(x_off + clipped_x, y_off + clipped_y, clipped_width, clipped_height) \
# Convert data into visible representation
data = data.transpose(1, 2, 0)
if not data.dtype == numpy.uint8:
data += data.min()
data *= 255 / data.max()
data = data.astype(numpy.uint8)
if not data.shape[:2] == (y_size, x_size):
padding = numpy.zeros((y_size, x_size, self.raster.RasterCount),
dtype=numpy.uint8)
padding[clipped_y:clipped_y + clipped_height,
clipped_x:clipped_x + clipped_width, :] = data
data = padding
return data
def center_of_raster(self):
geo_transform = self.raster.GetGeoTransform(
) # col, row ---> lon, lat
x, y = gdal.ApplyGeoTransform(geo_transform,
self.raster.RasterXSize / 2,
self.raster.RasterYSize / 2)
return x, y
def look_at(self, geo_x, geo_y, scale=None):
self.geo_x = geo_x
self.geo_y = geo_y
if scale is not None: self.scale = scale
self.update_raster()
def window_to_raster(self, x, y):
rx, ry, rw, rh = self.visible_region()
x = rx + rw * x / self.width()
y = ry + rh * y / self.height()
return x, y
def raster_to_window(self, x, y):
rx, ry, rw, rh = self.visible_region()
x = -rx + x * self.width() / rw
y = -ry + y * self.height() / rh
return x, y
def raster_to_geo(self, x, y):
geo_transform = self.raster.GetGeoTransform(
) # col, row ---> lon, lat
x, y = gdal.ApplyGeoTransform(geo_transform, x, y)
return x, y
def geo_to_raster(self, x, y):
geo_transform = self.raster.GetGeoTransform(
) # col, row ---> lon, lat
inv_transform = gdal.InvGeoTransform(geo_transform)
x, y = gdal.ApplyGeoTransform(inv_transform, x, y)
return x, y
def geo_to_window(self, x, y):
return self.raster_to_window(self.geo_to_raster(x, y))
def window_to_geo(self, x, y):
return self.raster_to_geo(*self.window_to_raster(x, y))
def update_raster(self):
self.buffer = self.load_buffer()
self.update()
def load_classifier(self):
try:
if os.path.exists('training.pkl'):
with open('training.pkl', 'rb') as f:
self.classifier.load(f)
except:
self.classifier.rebuild(self.dataset)
def paintEvent(self, QPaintEvent):
super(LabelerWindow, self).paintEvent(QPaintEvent)
p = QtGui.QPainter(self)
image = ImageQt(Image.fromarray(self.buffer))
image = image.scaled(self.width(), self.height())
p.drawImage(0, 0, image)
# Now for the HUD
# -> Draw green cross-hairs
old_pen = p.pen()
new_pen = QPen()
new_pen.setColor(Qt.green)
new_pen.setStyle(Qt.DotLine)
new_pen.setWidth(1)
p.setPen(new_pen)
p.drawLine(0, self.height() / 2, self.width(), self.height() / 2)
p.drawLine(self.width() / 2, 0, self.width() / 2, self.height())
p.setPen(old_pen)
# -> Show help for keystrokes
help = "[X] Pos. [C] Neg. [UP] Zoom in [DN] Zoom Out [LT] Undo Last [RT] Ignore [LMB] Move "
p.fillRect(0, 0, self.width(), p.fontMetrics().height() * 1.5, Qt.gray)
p.drawText(0, p.fontMetrics().height(), help)
def resizeEvent(self, QResizeEvent):
self.update_raster()
def get_sample(self):
s = self.load_buffer(width=self.dataset.width,
height=self.dataset.height,
scale=0.25).astype(float) / 255.0
return s
def geo_xy(self):
return self.geo_x, self.geo_y
def goto_next_sample(self):
if self.mode == MODE_POSITIVES:
self.goto_next_positive()
elif self.mode == MODE_NEGATIVES:
self.goto_next_negative()
else:
self.goto_next_uncertain()
def get_random_xys(self, n):
if self.random_walk:
return self.get_random_in_view(n)
else:
return self.get_random_in_raster(n)
def get_random_in_raster(self, n):
width, height = self.classifier.shape
x = numpy.random.random_integers(
int(ceil(width / 2)),
int(floor(self.raster.RasterXSize - width / 2)), n)
y = numpy.random.random_integers(
int(ceil(height / 2)),
int(floor(self.raster.RasterYSize - height / 2)), n)
return zip(x, y)
def get_random_in_view(self, n):
x0, y0, w, h = self.visible_region()
x1, y1 = x0 + w, y0 + h
width, height = self.classifier.shape
rx, ry = width / 2, height / 2
x0 = int(ceil(max(x0, rx)))
y0 = int(ceil(max(y0, ry)))
x1 = int(floor(min(self.raster.RasterXSize - rx, x1)))
y1 = int(floor(min(self.raster.RasterYSize - ry, y1)))
x = numpy.random.random_integers(x0, x1, n)
y = numpy.random.random_integers(y0, y1, n)
return zip(x, y)
def goto_next_uncertain(self):
x, y = zip(*self.get_random_xys(100))
samples = []
for i in range(len(x)):
self.look_at(*self.raster_to_geo(x[i], y[i]))
s = self.get_sample()
samples.append(s)
i, w = self.classifier.find_least_certain(samples)
print "Least confident:", w
self.look_at(*self.raster_to_geo(x[i], y[i]))
def goto_next_positive(self, thresh=-1):
best = -1
best_x, best_y = self.geo_x, self.geo_y
for i, (x, y) in enumerate(self.get_random_xys(100)):
self.look_at(*self.raster_to_geo(x, y))
s = self.get_sample()
w = self.classifier.classify(s)
if w > thresh:
best = i
best_x, best_y = x, y
thresh = w
self.look_at(*self.raster_to_geo(best_x, best_y))
def goto_next_negative(self, thresh=0.5):
for i, (x, y) in enumerate(self.get_random_xys(100)):
self.look_at(*self.raster_to_geo(x, y))
s = self.get_sample()
w = self.classifier.classify(s)
if w < thresh:
print "Negative sample", w
break
def keyPressEvent(self, QKeyEvent):
key = QKeyEvent.key()
x, y = self.geo_x, self.geo_y
def undo_positive():
self.look_at(x, y)
self.dataset.delete(self.get_sample(), label='positive')
self.classifier.positives.pop()
self.classifier.save()
def undo_negative():
self.look_at(x, y)
self.dataset.delete(self.get_sample(), label='negative')
self.classifier.negatives.pop()
self.classifier.save()
if key == Qt.Key_X:
print "Mark Pos. ", self.raster.GetDescription(), self.geo_xy()
self.classifier.add_positive(self.get_sample())
self.dataset.create(self.get_sample(),
label='positive',
meta=dict(pos=self.geo_xy())).save()
self.classifier.save()
undos.append(undo_positive)
self.goto_next_sample()
elif key == Qt.Key_C:
print "Mark Neg. ", self.raster.GetDescription(), self.geo_xy()
self.classifier.add_negative(self.get_sample())
self.dataset.create(self.get_sample(),
label='negative',
meta=dict(pos=self.geo_xy())).save()
self.classifier.save()
undos.append(undo_negative)
self.goto_next_sample()
elif key == Qt.Key_Left:
print len(undos)
if len(undos) > 0:
undos[-1]()
undos.pop()
elif key == Qt.Key_Right:
print "Next Sample (do not save current)"
self.goto_next_sample()
elif key == Qt.Key_Up:
self.scale *= sqrt(2.)
self.update_raster()
elif key == Qt.Key_Down:
self.scale /= sqrt(2.)
self.update_raster()
elif key == Qt.Key_S:
self.save_classifier()
elif key == Qt.Key_R:
self.classifier.rebuild(self.dataset)
self.save_classifier()
self.goto_next_sample()
elif key == Qt.Key_L:
self.load_classifier()
elif key == Qt.Key_P:
self.mode = MODE_POSITIVES
self.goto_next_sample()
elif key == Qt.Key_N:
self.mode = MODE_NEGATIVES
self.goto_next_sample()
elif key == Qt.Key_U:
self.mode = MODE_UNCERTAIN
self.goto_next_sample()
elif key == Qt.Key_V:
s = self.get_sample()
self.visualize_classification(s)
def save_classifier(self):
self.classifier.save(file('training.pkl', 'wb'))
def visualize_classification(self, s):
import pylab
i_p, w_p = self.classifier.find_similar_positive(s)
i_n, w_n = self.classifier.find_similar_negative(s)
pylab.figure()
pylab.subplot(231)
if i_n >= 0:
pylab.imshow(self.classifier.negatives[i_n])
pylab.title('negative')
pylab.subplot(232)
pylab.imshow(s)
pylab.title('query')
pylab.subplot(233)
if i_p >= 0:
pylab.imshow(self.classifier.positives[i_p])
pylab.title('positive')
pylab.subplot(234)
if i_n >= 0:
pylab.imshow((abs(self.classifier.negatives[i_n].astype(float) -
s)**2).mean(2),
cmap=pylab.cm.gray,
vmax=255**2)
pylab.subplot(236)
if i_p >= 0:
pylab.imshow((abs(self.classifier.positives[i_p].astype(float) -
s)**2).mean(2),
cmap=pylab.cm.gray,
vmax=255**2)
pylab.show()
def mousePressEvent(self, QMouseEvent):
x, y = QMouseEvent.x(), QMouseEvent.y()
button = QMouseEvent.button()
if button == Qt.LeftButton:
rx, ry = self.window_to_raster(x, y)
gx, gy = self.raster_to_geo(rx, ry)
self.look_at(gx, gy)
QtGui.QCursor.setPos(self.mapToGlobal(self.rect().center()))