def __init__(self, image_file):
self.image = Image.from_file(image_file)
self.debug_animation = AnimationMaker()
def __init__(self, image_file):
self.image = Image.from_file(image_file)
self.debug_animation = AnimationMaker()
class SeamCarver:
def __init__(self, image_file):
self.image = Image.from_file(image_file)
self.debug_animation = AnimationMaker()
def seams(self, n):
"""
Finds the n minimum seams in the image
:param n: the number of paths to be found
:return a list of Image objects, each containing the a path, sorted ascending
"""
image = self.image.min_energy
seams_found = []
for iteration in range(n):
seam_image = np.zeros((self.image.height, 1))
for i in reversed(range(0, self.image.height)):
if i == self.image.height - 1:
value = min(image[i, :])
j = np.where(image[i][:] == value)[0][0]
else:
if seam_image[i + 1, 0] == 0:
tmp = [
float("Inf"), image[i, seam_image[i + 1, 0]],
image[i, seam_image[i + 1, 0] + 1]
]
elif seam_image[i + 1, 0] == self.image.width - 1:
tmp = [
image[i, seam_image[i + 1, 0] - 1],
image[i, seam_image[i + 1, 0]],
float("Inf")
]
else:
tmp = [
image[i, seam_image[i + 1, 0] - 1],
image[i, seam_image[i + 1, 0]],
image[i, seam_image[i + 1, 0] + 1]
]
j = seam_image[i + 1, 0] + np.argmin(tmp) - 1
seam_image[i, 0] = j
image[i, j] = float("Inf")
seams_found.append(
Image.from_image_array(array=seam_image,
transposed=self.image.transposed))
return seams_found
def cut_seam(self):
seam = self.seams(1)[0]
result = np.zeros(
(self.image.height, self.image.width - 1, self.image.dim))
for i in range(0, self.image.dim):
for j in range(0, self.image.height):
if seam.transposed:
result[j, :, i] = np.append(
self.image.array[j, 0:seam.array[0, j], i],
self.image.array[j,
seam.array[0, j] + 1:self.image.width,
i])
else:
result[j, :, i] = np.append(
self.image.array[j, 0:seam.array[j, 0], i],
self.image.array[j,
seam.array[j, 0] + 1:self.image.width,
i])
debug_image = self.image.debug(seam)
self.debug_animation.add(
Image.from_image_array(debug_image,
transposed=self.image.transposed))
if self.image.transposed:
result = result.transpose(1, 0, 2)
return Image.from_image_array(array=result,
transposed=self.image.transposed)
def add_seam(self, seam):
result = np.zeros(
(self.image.height, self.image.width + 1, self.image.dim))
for i in range(0, self.image.dim):
for j in range(0, self.image.height):
x = seam.array[0, j] if seam.transposed else seam.array[j, 0]
if x < self.image.width - 2:
vector_average = np.array([
(self.image.array[j, x, i] +
self.image.array[j, x + 1, i]) / 2.0
])
else:
vector_average = np.array([
(self.image.array[j, x, i] +
self.image.array[j, x - 1, i]) / 2.0
])
tmp = np.append(self.image.array[j, 0:x + 1, i],
vector_average)
result[j, :, i] = np.append(
tmp, self.image.array[j, x + 1:self.image.width, i])
debug_image = self.image.debug(seam)
self.debug_animation.add(
Image.from_image_array(debug_image,
transposed=self.image.transposed))
if self.image.transposed:
result = result.transpose(1, 0, 2)
return Image.from_image_array(array=result, transposed=seam.transposed)
def cut_seams(self, desired_width):
iterations = self.image.width - desired_width
current_iteration = 0
while current_iteration < iterations:
print("Resizing from %s to %s" %
(self.image.width, self.image.width - 1))
self.image = self.cut_seam()
current_iteration += 1
def add_seams(self, desired_width):
iterations = desired_width - self.image.width
seams = self.seams(iterations)
current_iteration = 0
while current_iteration < iterations:
print("Resizing from %s to %s" %
(self.image.width, self.image.width + 1))
self.image = self.add_seam(seams[current_iteration])
current_iteration += 1
def resize(self, desired_width, desired_height):
if desired_width < self.image.width:
self.cut_seams(desired_width)
else:
self.add_seams(desired_width)
if desired_height < self.image.height:
self.image.transposed = True
self.cut_seams(desired_height)
self.image.transposed = False
else:
self.image.transposed = True
self.add_seams(desired_height)
self.image.transposed = False
return self.image
class SeamCarver:
def __init__(self, image_file):
self.image = Image.from_file(image_file)
self.debug_animation = AnimationMaker()
def seams(self, n):
"""
Finds the n minimum seams in the image
:param n: the number of paths to be found
:return a list of Image objects, each containing the a path, sorted ascending
"""
image = self.image.min_energy
seams_found = []
for iteration in range(n):
seam_image = np.zeros((self.image.height, 1))
for i in reversed(range(0, self.image.height)):
if i == self.image.height - 1:
value = min(image[i, :])
j = np.where(image[i][:] == value)[0][0]
else:
if seam_image[i + 1, 0] == 0:
tmp = [
float("Inf"),
image[i, seam_image[i + 1, 0]],
image[i, seam_image[i + 1, 0] + 1]
]
elif seam_image[i + 1, 0] == self.image.width - 1:
tmp = [
image[i, seam_image[i + 1, 0] - 1],
image[i, seam_image[i + 1, 0]],
float("Inf")
]
else:
tmp = [
image[i, seam_image[i + 1, 0] - 1],
image[i, seam_image[i + 1, 0]],
image[i, seam_image[i + 1, 0] + 1]
]
j = seam_image[i+1, 0] + np.argmin(tmp) - 1
seam_image[i, 0] = j
image[i, j] = float("Inf")
seams_found.append(Image.from_image_array(array=seam_image, transposed=self.image.transposed))
return seams_found
def cut_seam(self):
seam = self.seams(1)[0]
result = np.zeros((self.image.height, self.image.width - 1, self.image.dim))
for i in range(0, self.image.dim):
for j in range(0, self.image.height):
if seam.transposed:
result[j, :, i] = np.append(self.image.array[j, 0: seam.array[0, j], i],
self.image.array[j, seam.array[0, j] + 1: self.image.width, i]
)
else:
result[j, :, i] = np.append(self.image.array[j, 0: seam.array[j, 0], i],
self.image.array[j, seam.array[j, 0] + 1: self.image.width, i]
)
debug_image = self.image.debug(seam)
self.debug_animation.add(Image.from_image_array(debug_image, transposed=self.image.transposed))
if self.image.transposed:
result = result.transpose(1, 0, 2)
return Image.from_image_array(array=result, transposed=self.image.transposed)
def add_seam(self, seam):
result = np.zeros((self.image.height, self.image.width + 1, self.image.dim))
for i in range(0, self.image.dim):
for j in range(0, self.image.height):
x = seam.array[0, j] if seam.transposed else seam.array[j, 0]
if x < self.image.width - 2:
vector_average = np.array([(self.image.array[j, x, i] + self.image.array[j, x + 1, i])/2.0])
else:
vector_average = np.array([(self.image.array[j, x, i] + self.image.array[j, x - 1, i])/2.0])
tmp = np.append(self.image.array[j, 0: x + 1, i], vector_average)
result[j, :, i] = np.append(tmp, self.image.array[j, x + 1: self.image.width, i])
debug_image = self.image.debug(seam)
self.debug_animation.add(Image.from_image_array(debug_image, transposed=self.image.transposed))
if self.image.transposed:
result = result.transpose(1, 0, 2)
return Image.from_image_array(array=result, transposed=seam.transposed)
def cut_seams(self, desired_width):
iterations = self.image.width - desired_width
current_iteration = 0
while current_iteration < iterations:
print("Resizing from %s to %s" % (self.image.width, self.image.width-1))
self.image = self.cut_seam()
current_iteration += 1
def add_seams(self, desired_width):
iterations = desired_width - self.image.width
seams = self.seams(iterations)
current_iteration = 0
while current_iteration < iterations:
print("Resizing from %s to %s" % (self.image.width, self.image.width+1))
self.image = self.add_seam(seams[current_iteration])
current_iteration += 1
def resize(self, desired_width, desired_height):
if desired_width < self.image.width:
self.cut_seams(desired_width)
else:
self.add_seams(desired_width)
if desired_height < self.image.height:
self.image.transposed = True
self.cut_seams(desired_height)
self.image.transposed = False
else:
self.image.transposed = True
self.add_seams(desired_height)
self.image.transposed = False
return self.image
