def __init__(self, image, label, strength, parent=None):
super(GrowCutWidget, self).__init__(parent)
self.image = image
self.strength = strength
self.label = label
self.coordinates = automata.formSamples(
self.image.shape,
neighbours=automata.CONNECT_4
)
# Itterating?
self.paused = True
self.editLabel = 1
self.resize(640, 480)
self.layout = QtGui.QHBoxLayout(self)
self.setMouseTracking(True)
# Place a visualization of the image.
self.imageScene = QtGui.QGraphicsScene(self)
self.imageView = GrowCutImageView(self.imageScene)
self.imageView.fitInView(QtCore.QRectF(0, 0, image.shape[1], image.shape[0]), QtCore.Qt.KeepAspectRatio)
self.layout.addWidget(self.imageView)
self.displayedImage = QtGui.QGraphicsPixmapItem()
self.imageScene.addItem(self.displayedImage)
self.imageView.centerOn(self.displayedImage)
self.displayedImage.setPixmap(
QtGui.QPixmap.fromImage(ndarrayToQtImage(self.image))
)
# Place a visualization of the label.
self.labelScene = QtGui.QGraphicsScene(self)
self.labelView = GrowCutImageView(self.labelScene)
self.labelView.fitInView(QtCore.QRectF(0, 0, image.shape[1], image.shape[0]), QtCore.Qt.KeepAspectRatio)
self.layout.addWidget(self.labelView)
self.displayedLabel = QtGui.QGraphicsPixmapItem()
self.labelScene.addItem(self.displayedLabel)
self.labelView.centerOn(self.displayedLabel)
self.displayedLabel.setPixmap(
QtGui.QPixmap.fromImage(ndarrayToQtImage(self.label))
)
self.labelView.mouseDragEvent.connect(self.labelUpdate)
self.imageView.mouseDragEvent.connect(self.labelUpdate)
# self.labelView.wheelEvent.connect(self.wheelEvent)
# Form a timer for growcut automations
self.timer = QtCore.QTimer()
self.timer.timeout.connect(self.timedUpdate)
self.redPen = QtGui.QPen(QtGui.QColor(*RED))
self.greenPen = QtGui.QPen(QtGui.QColor(*GREEN))
def test_numpy_automate():
shape = (100, 100)
cx, cy = shape[1] / 2.0, shape[0] / 2.0
r = 20
y, x = np.ogrid[0:shape[0], 0:shape[1]]
mask = (np.power((y - cy), 2) + np.power((x - cx), 2)) < np.power(r, 2)
field = np.zeros(mask.shape)
field[mask] = 1.0
label = np.zeros_like(field)
theta = np.zeros_like(field)
label[:] = 0
label[0:(r / 4), 0:(r / 4)] = -1
theta[0:(r / 4), 0:(r / 4)] = 1.0
label[cy - (r / 4):cy + (r / 4), cx - (r / 4):cx + (r / 4)] = 1
theta[cy - (r / 4):cy + (r / 4), cx - (r / 4):cx + (r / 4)] = 1.0
# Plot the image and the label map.
# import matplotlib.pyplot as plt
# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
# ax1.imshow(field, interpolation='nearest', cmap='gray')
# ax1.axis('off')
# ax2.imshow(label, interpolation='nearest', cmap='jet')
# ax2.axis('off')
coordinates = automata.formSamples(field.shape, neighbours=automata.CONNECT_8)
# Automate to update the labels
for itteration in range(100):
theta, label = growcut.numpyAutomate(coordinates, field, theta, label)
# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
# ax1.imshow(field, interpolation='nearest', cmap='gray')
# ax1.axis('off')
# ax2.imshow(label, interpolation='nearest', cmap='jet')
# ax2.axis('off')
# plt.show()
# Assert that the label map looks like the mask
assert np.allclose(label[mask], 1.0) & np.allclose(label[~mask], -1), \
"Segmentation did not converge after {} iterations".format(itteration)
def test_formSamples():
"""
Assert that the correct pattern of samples is generated using both
the fast and slow sampling methods.
"""
grid, _ = np.mgrid[0:100, 0:100]
gridIterator = automata.iterGrid(grid, automata.CONNECT_4)
# Slow approach
iteratorValues = []
for _, values in gridIterator:
iteratorValues.append(values)
iteratorValues = np.array(iteratorValues)
# Fast approach
coordinates = automata.formSamples(grid.shape, automata.CONNECT_4)
sampledValues = grid[coordinates]
# Assert equivalence of approaches
assert np.allclose(sampledValues, iteratorValues)
def imageUpdate(self, fname):
lum = plt.imread(fname)
self.image = np.average(lum, 2)
self.label = np.zeros_like(self.image, dtype=np.int)
self.strength = np.zeros_like(self.image, dtype=np.float64)
self.displayedImage.setPixmap(
QtGui.QPixmap.fromImage(ndarrayToQtImage(self.image))
)
self.imageView.centerOn(self.displayedImage)
self.displayedLabel.setPixmap(
QtGui.QPixmap.fromImage(ndarrayToQtImage(self.label))
)
self.labelView.centerOn(self.displayedLabel)
self.coordinates = automata.formSamples(
self.image.shape,
neighbours=automata.CONNECT_4
)
import numpy as np
from growcut import automata
from matplotlib import pyplot as plt
from matplotlib import animation
state = np.random.randint(0, 2, (250, 250)).astype(np.bool)
coordinates = automata.formSamples(state.shape, neighbours=automata.CONNECT_8)
fig = plt.figure(figsize=(10, 10))
img = plt.imshow(state, interpolation='nearest', origin='lower', cmap='binary')
plt.axis('off')
def init():
img.set_data(state)
return img,
def animate(i):
state[:] = automata.numpyGameOfLife(state, coordinates)[:]
img.set_data(state)
return img,
# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(
fig,
animate,
init_func=init,
# Form a label grid (0: no label, 1: foreground, 2: background) label = np.zeros_like(lum, dtype=np.int) label[:] = -1 label[75:90, 100:110] = 1 label[110:120, 150:160] = 1 label[50:55, 160:165] = 1 label[50:55, 180:185] = 0 label[0:10, 0:10] = 0 label[75:90, 0:10] = 0 label[0:10, 200:210] = 0 label[75:90, 200:210] = 0 # Form a strength grid. strength = np.zeros_like(lum, dtype=np.float64) strength[label != -1] = 1.0 t0 = time.time() coordinates = automata.formSamples(lum.shape, neighbours=automata.CONNECT_4) strength, label = growcut.numpyAutomate(coordinates, lum, strength, label) print "Numpy vectorized: " + str(1000 * (time.time() - t0)) + " ms" t0 = time.time() strength, label = automate_cy(lum, strength, label, connectivity=4) print "Cython: " + str(1000 * (time.time() - t0)) + " ms" t0 = time.time() strength, label = growcut.automate(lum, strength, label) print "Pure Python: " + str(1000 * (time.time() - t0)) + " ms"
