def test_displaySeam():
image_path = "inputSeamCarvingPrague.jpg"
im = imageio.imread(image_path)
energy = energy_image(im)
# Horizontal
cum_map_h = cumulative_minimum_energy_map(energy, 'HORIZONTAL')
# view_heat_map(cum_map_h)
Lh = find_optimal_horizontal_seam(cum_map_h)
# displaySeam(image_path, Lh, 'HORIZONTAL')
# Vertical
cum_map_v = cumulative_minimum_energy_map(energy, 'VERTICAL')
# view_heat_map(cum_map_v)
Lv = find_optimal_vertical_seam(cum_map_v)
# displaySeam(image_path, Lv, 'VERTICAL')
# Display both
displayBothSeam(image_path, Lh, Lv)
def reduceWidth(im, energyImage):
cols = find_optimal_vertical_seam(cumulative_minimum_energy_map(energyImage, VER_DIR))
m, n, d = im.shape
mask = np.ones(im.shape, dtype=bool)
mask[np.arange(m), cols, :] = False
reducedColorImage = im[mask].reshape((m, n - 1, d))
reducedEnergyImage = energy_image(reducedColorImage)
return reducedColorImage, reducedEnergyImage
def test_find_optimal_vertical_seam():
image_path = "inputSeamCarvingPrague.jpg"
im = imageio.imread(image_path)
energy = energy_image(im)
cum_map = cumulative_minimum_energy_map(energy, 'VERTICAL')
# view_heat_map(cum_map)
L = find_optimal_vertical_seam(cum_map)
print (L)
def test_find_optimal_horizontal_seam():
image_path = "inputSeamCarvingPrague.jpg"
im = imageio.imread(image_path)
energy = energy_image(im)
cum_map = cumulative_minimum_energy_map(energy, 'HORIZONTAL')
view_heat_map(cum_map)
L = find_optimal_horizontal_seam(cum_map)
print(len(L))
print(L)
def reduceHeight(im, energyImage):
rows = np.array(
find_optimal_horizontal_seam(
cumulative_minimum_energy_map(energyImage, HOR_DIR)))
im = np.rot90(im, 1, (1, 0))
m, n, d = im.shape
mask = np.ones(im.shape, dtype=bool)
mask[np.arange(m), n - rows, :] = False
reducedColorImage = im[mask].reshape((m, n - 1, d))
reducedColorImage = np.rot90(reducedColorImage, 1, (0, 1))
reducedEnergyImage = energy_image(reducedColorImage)
return reducedColorImage, reducedEnergyImage
def reduceHeight(im, energyImage):
"""Reduced the heigh of an image by remove the optimal horizontal seam
Parameters
----------
im : np.array (np.uint8)
The image matrix
energyImage :np.array (np.float64/double)
Energy of the image
Returns
-------
reducedImage : np.array (np.uint8)
The reduced width image
reducedEnergyImage : np.array (np.float64/double)
The reduced width energy image
"""
im_trans = np.transpose(im, axes=[1, 0, 2])
h, w, c = im_trans.shape
# Compute the cumulative horizontal energy of the image
cumulativeEnergyMap = cumulative_minimum_energy_map(
energyImage, 'HORIZONTAL')
# Compute the optimal horizontal seam
seam = find_optimal_horizontal_seam(cumulativeEnergyMap)
# Generate Index list to delete
xs = seam
ys = [i for i in range(h)]
# Removing seam from image
mask = np.ones(im_trans.shape, dtype=bool)
mask[ys, xs, :] = False
reducedColorImage = im_trans[mask]
reducedImage = np.transpose(reducedColorImage.reshape(h, w - 1, c),
axes=[1, 0, 2])
# Computing new energy map
reducedEnergyImage = energy_image(reducedImage)
# Return the value
return [reducedImage, reducedEnergyImage]
# Uncomment lines from here for testing
# Test functions
# def test_reduceHeight():
# image_path = "inputSeamCarvingPrague.jpg"
# im = imageio.imread(image_path)
# energy = energy_image(im)
# new_im, new_energy = reduceHeight(im, energy)
# view_image(im)
# view_image(new_im)
# view_image(energy)
# if __name__=="__main__":
# test_reduceHeight();