def test_haar2d():
"""
Asserts that some basic test cases are correct.
"""
assert haar2d(np.random.random([5,3]),2,debug=True).shape == (8,4), "Transform data must be padded to compatible shape."
assert haar2d(np.random.random([8,4]),2,debug=True).shape == (8,4), "Transform data must be padded to compatible shape, only if neccersary."
image = np.random.random([5,3])
haart = haar2d(image, 3, debug=False)
haari = ihaar2d(haart, 3, debug=False)[:image.shape[0], :image.shape[1]]
assert (image - haari < 0.0001).all(), "Transform must be circular."
def test_haar2d():
"""
Asserts the forwards and inverse wavelet transformations are correct.
"""
assert haar2d.haar2d(np.random.random([5,3]),2,debug=True).shape == (8,4), \
"Transform data must be padded to compatible shape."
assert haar2d.haar2d(np.random.random([8,4]),2,debug=True).shape == (8,4), \
"Transform data must be padded to compatible shape, if required."
image = np.random.random([5,3])
haart = haar2d.haar2d(image, 3)
haari = haar2d.ihaar2d(haart, 3)[:image.shape[0], :image.shape[1]]
assert (image - haari < 0.0001).all(), "Transform must be circular."
def _detectHaarFeatures(image, options={}):
if options is None:
options = _haarDefaultOptions(image)
levels = options.get('levels')
maxpoints = options.get('maxpoints')
threshold = options.get('threshold')
locality = options.get('locality')
haarData = haar2d(image, levels)
avgRows = haarData.shape[0] / 2 ** levels
avgCols = haarData.shape[1] / 2 ** levels
SalientPoints = {}
siloH = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
siloD = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
siloV = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
# Build the saliency silos
for i in range(levels):
level = i + 1
halfRows = haarData.shape[0] / 2 ** level
halfCols = haarData.shape[1] / 2 ** level
siloH[:,:,i] = nd.zoom(haarData[:halfRows, halfCols:halfCols*2], 2**(level-1))
siloD[:,:,i] = nd.zoom(haarData[halfRows:halfRows*2, halfCols:halfCols*2], 2**(level-1))
siloV[:,:,i] = nd.zoom(haarData[halfRows:halfRows*2, :halfCols], 2**(level-1))
# Calculate saliency heat-map
saliencyMap = np.max(np.array([
np.sum(np.abs(siloH), axis=2),
np.sum(np.abs(siloD), axis=2),
np.sum(np.abs(siloV), axis=2)
]), axis=0)
# Determine global maximum and saliency threshold
maximum = np.max(saliencyMap)
sthreshold = threshold * maximum
# Extract features by finding local maxima
rows = haarData.shape[0] / 2
cols = haarData.shape[1] / 2
features = {}
id = 0
for row in range(locality,rows-locality):
for col in range(locality,cols-locality):
saliency = saliencyMap[row,col]
if saliency > sthreshold:
if saliency >= np.max(saliencyMap[row-locality:row+locality, col-locality:col+locality]):
features[id] = (row*2,col*2)
id += 1
result = {}
result['points'] = features
return result
