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Optimized function(s) using numpy.lib.stride_tricks.as_strided #166

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Optimized function(s) using numpy.lib.stride_tricks.as_strided #166

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100 changes: 19 additions & 81 deletions miscnn/utils/patch_operations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,41 +77,15 @@ def concat_matrices(patches, image_size, window, overlap, three_dim):
#-----------------------------------------------------#
# Slice a 2D matrix
def slice_2Dmatrix(array, window, overlap):
# Calculate steps
steps_x = int(math.ceil((len(array) - overlap[0]) /
float(window[0] - overlap[0])))
steps_y = int(math.ceil((len(array[0]) - overlap[1]) /
float(window[1] - overlap[1])))
# Exception Handling: patch overlap smaller than patches
if steps_x < 1 : steps_x = 1
if steps_y < 1 : steps_y = 1

# Iterate over it x,y
patches = []
for x in range(0, steps_x):
for y in range(0, steps_y):
# Define window edges
x_start = x*window[0] - x*overlap[0]
x_end = x_start + window[0]
y_start = y*window[1] - y*overlap[1]
y_end = y_start + window[1]
# Adjust ends
if(x_end > len(array)):
# Create an overlapping patch for the last images / edges
# to ensure the fixed patch/window sizes
x_start = len(array) - window[0]
x_end = len(array)
# Fix for MRIs which are smaller than patch size
if x_start < 0 : x_start = 0
if(y_end > len(array[0])):
y_start = len(array[0]) - window[1]
y_end = len(array[0])
# Fix for MRIs which are smaller than patch size
if y_start < 0 : y_start = 0
# Cut window
window_cut = array[x_start:x_end,y_start:y_end]
# Add to result list
patches.append(window_cut)
# Calculate strides
strides = array.strides
shape = ((array.shape[0] - window[0]) // (window[0] - overlap[0])) + 1, \
((array.shape[1] - window[1]) // (window[1] - overlap[1])) + 1, \
window[0], window[1]
strides = strides[0] * (window[0] - overlap[0]), strides[1] * (window[1] - overlap[1]), \
strides[0], strides[1]
# Slice array
patches = np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides)
return patches

# Concatenate a list of patches together to a numpy matrix
Expand Down Expand Up @@ -165,53 +139,17 @@ def concat_2Dmatrices(patches, image_size, window, overlap):
#-----------------------------------------------------#
# Slice a 3D matrix
def slice_3Dmatrix(array, window, overlap):
# Calculate steps
steps_x = int(math.ceil((len(array) - overlap[0]) /
float(window[0] - overlap[0])))
steps_y = int(math.ceil((len(array[0]) - overlap[1]) /
float(window[1] - overlap[1])))
steps_z = int(math.ceil((len(array[0][0]) - overlap[2]) /
float(window[2] - overlap[2])))
# Exception Handling: patch overlap smaller than patches
if steps_x < 1 : steps_x = 1
if steps_y < 1 : steps_y = 1
if steps_z < 1 : steps_z = 1
strides = array.itemsize * np.array([array.shape[1]*array.shape[2], array.shape[2], 1])
shape = np.array([(array.shape[0] - window[0]) // (window[0] - overlap[0]) + 1,
(array.shape[1] - window[1]) // (window[1] - overlap[1]) + 1,
(array.shape[2] - window[2]) // (window[2] - overlap[2]) + 1,
window[0], window[1], window[2]])
strides = np.concatenate((strides, strides[-2:] * np.array([window[1], 1])))
strides[-3:] = array.itemsize

# Iterate over it x,y,z
patches = []
for x in range(0, steps_x):
for y in range(0, steps_y):
for z in range(0, steps_z):
# Define window edges
x_start = x*window[0] - x*overlap[0]
x_end = x_start + window[0]
y_start = y*window[1] - y*overlap[1]
y_end = y_start + window[1]
z_start = z*window[2] - z*overlap[2]
z_end = z_start + window[2]
# Adjust ends
if(x_end > len(array)):
# Create an overlapping patch for the last images / edges
# to ensure the fixed patch/window sizes
x_start = len(array) - window[0]
x_end = len(array)
# Fix for MRIs which are smaller than patch size
if x_start < 0 : x_start = 0
if(y_end > len(array[0])):
y_start = len(array[0]) - window[1]
y_end = len(array[0])
# Fix for MRIs which are smaller than patch size
if y_start < 0 : y_start = 0
if(z_end > len(array[0][0])):
z_start = len(array[0][0]) - window[2]
z_end = len(array[0][0])
# Fix for MRIs which are smaller than patch size
if z_start < 0 : z_start = 0
# Cut window
window_cut = array[x_start:x_end,y_start:y_end,z_start:z_end]
# Add to result list
patches.append(window_cut)
return patches
patches = np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides)

return patches.reshape([-1, window[0], window[1], window[2]])

# Concatenate a list of patches together to a numpy matrix
def concat_3Dmatrices(patches, image_size, window, overlap):
Expand Down