def __init__(self, im=None, im_path=None):
self.stack = Stack()
if im is not None:
self.im = im
elif im_path is not None:
self.imread(im_path)
else:
raise ValueError("No Input image!")
self.im_size = self.im.shape
self.im_height, self.im_width = self.im_size
self.im_label = np.full_like(self.im, 0)
#import ipdb; ipdb.set_trace()
self.max_label = 0
def __init__(self, im=None, im_path=None):
self.stack = Stack()
if im is not None:
self.im = im
elif im_path is not None:
self.imread(im_path)
else:
raise ValueError("No Input image!")
self.im_size = self.im.shape
self.im_height, self.im_width = self.im_size
self.im_label = np.full_like(self.im, 0).astype(np.int)
self.max_label = 0
self.label_area = {}
def bracket_validation_better_dict(string): stack = Stack() for ele in string: if ele in better_dict.values(): stack.push(ele) elif ele in better_dict.keys(): if not stack.is_empty(): top = stack.top() if top == better_dict[ele]: stack.pop() else: return False else: return False if not stack.is_empty(): return False return True
def bracket_validation(string): stack = Stack() for ele in string: if ele in dict.keys(): stack.push(ele) elif ele in dict.values(): if not stack.is_empty(): top = stack.top() if top == _get_key_from_value(ele): stack.pop() else: return False else: return False if not stack.is_empty(): return False return True
def length_of_longest_valid_substring(string):
#assume only contains ( and )
if len(string) == 0:
return
stack = Stack()
length = 0
for element in string:
if element == '(':
stack.push('(')
elif element == ')':
if not stack.is_empty():
stack.pop()
length +=2
return length
class GrowSeedAlgo(object):
'''
only for binary image
'''
def __init__(self, im=None, im_path=None):
self.stack = Stack()
if im is not None:
self.im = im
elif im_path is not None:
self.imread(im_path)
else:
raise ValueError("No Input image!")
self.im_height, self.im_width = self.im.shape
self.im_label = np.full_like(self.im, 0, dtype=int)
self.im_area = self.im.size
self.max_label = 0
self.label_area = {}
self.label_color = {}
def imread(self, img_path):
self.im_path = im_path
self.im = cv2.imread(im_path)
def output(self, threshold=0.05):
im_out = np.zeros_like(self.im)
im_out = np.stack((im_out,im_out,im_out),axis=2)
for label_idx in range(1,self.max_label+1):
if self.label_area[label_idx]/self.im_area > threshold: # areas smaller than threshold will be treated as background!!!
rand_color = np.random.randint(0,255),np.random.randint(0,255),np.random.randint(0,255)
im_out[np.where(self.im_label==label_idx)]=rand_color
self.label_color[label_idx] = rand_color
#cv2.imshow('output',im_out)
#cv2.waitKey(0)
def drawBbox(self, threshold=0.05):
im_out = self.im.copy()
im_out = im_out[:,:,np.newaxis].repeat(3, axis=2)
for label_idx in range(1,self.max_label+1):
if self.label_area[label_idx]/self.im_area > threshold: # areas smaller than threshold will be treated as background!!!
ys, xs = np.where(self.im_label==label_idx)
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
#print(xmin,ymin,xmax,ymax)
rand_color = self.label_color[label_idx]
cv2.rectangle(im_out, (xmin,ymin), (xmax,ymax), rand_color, 2)
cv2.imshow('bbox', im_out)
cv2.waitKey(100)
cv2.waitKey(0)
def start(self):
for x0 in range(self.im_height):
for y0 in range(self.im_width):
if self.im_label[x0,y0] == 0 and self.im[x0,y0] != 0: # ignoring the background
self.max_label += 1
self.im_label[x0,y0] = self.max_label
self.stack.push((x0,y0))
tmp_area = 1 # tmp_area is the area of growned region in this round
while not self.stack.is_empty():
x,y = self.stack.pop()
#if self.max_label == 256:
#import ipdb; ipdb.set_trace()
tmp_area += self.grow(x,y)
self.label_area[self.max_label] = tmp_area
#print(self.max_label)
#cv2.imshow('growseed',self.im_label/self.max_label)
#cv2.waitKey(1)
threshold = 0
self.output(threshold)
self.drawBbox(threshold)
def grow(self, x0,y0):
tmp = 0
current_label = self.im_label[x0,y0]
for x,y in self._get_neighbour(x0,y0):
if self.im_label[x,y] == 0 and self.im[x,y] == self.im[x0,y0]: # threshold
self.im_label[x,y] = current_label
self.stack.push((x,y))
tmp += 1
#cv2.imshow('growseed',self.im_label/self.max_label)
#cv2.waitKey(1)
return tmp
def _get_neighbour(self, x0, y0):
neighbour = []
for i in (-1,0,1):
for j in (-1,0,1):
if (i,j) == (0,0):
continue
x = x0+i
y = y0+j
if self._in_region(x,y):
neighbour.append((x,y))
return neighbour
def _in_region(self, x,y):
return True if 0<=x<self.im_height and 0<=y<self.im_width else False
class GrowSeedAlgo(object):
'''
only for binary image
'''
def __init__(self, im=None, im_path=None):
self.stack = Stack()
if im is not None:
self.im = im
elif im_path is not None:
self.imread(im_path)
else:
raise ValueError("No Input image!")
self.im_size = self.im.shape
self.im_height, self.im_width = self.im_size
self.im_label = np.full_like(self.im, 0).astype(np.int)
self.max_label = 0
self.label_area = {}
def imread(self, img_path):
self.im_path = im_path
self.im = cv2.imread(im_path)
def output(self, threshold=20):
im_out = np.zeros_like(self.im)
im_out = np.stack((im_out,im_out,im_out),axis=2)
for label_idx in range(1,self.max_label+1):
if self.label_area[label_idx] > threshold: # areas smaller than threshold will be treated as background!!!
rand_color = np.random.randint(0,255),np.random.randint(0,255),np.random.randint(0,255)
im_out[np.where(self.im_label==label_idx)]=rand_color
cv2.imshow('output',im_out)
cv2.waitKey(0)
def start(self):
for x0 in range(self.im_height):
for y0 in range(self.im_width):
if self.im_label[x0,y0] == 0 and self.im[x0,y0] != 0: # ignoring the background
self.max_label += 1
self.im_label[x0,y0] = self.max_label
self.stack.push((x0,y0))
tmp_area = 1 # tmp_area is the area of growned region in this round
while not self.stack.is_empty():
x,y = self.stack.pop()
tmp_area += self.grow(x,y)
self.label_area[self.max_label] = tmp_area
#cv2.imshow('growseed',self.im_label/self.max_label)
#cv2.waitKey(0)
self.output(threshold=20)
def grow(self, x0,y0):
tmp = 0
current_label = self.im_label[x0,y0]
for x,y in self._get_neighbour(x0,y0):
if self.im_label[x,y] == 0 and self.im[x,y] == self.im[x0,y0]: # threshold
self.im_label[x,y] = current_label
self.stack.push((x,y))
tmp += 1
#cv2.imshow('growseed',self.im_label/self.max_label)
#cv2.waitKey(1)
return tmp
def _get_neighbour(self, x0, y0):
neighbour = []
for i in (-1,0,1):
for j in (-1,0,1):
if (i,j) == (0,0):
continue
x = x0+i
y = y0+j
if self._in_region(x,y):
neighbour.append((x,y))
return neighbour
def _in_region(self, x,y):
return True if 0<=x<self.im_height and 0<=y<self.im_width else False
class GrowSeedAlgo(object):
'''
only for binary image
'''
def __init__(self, im=None, im_path=None):
self.stack = Stack()
if im is not None:
self.im = im
elif im_path is not None:
self.imread(im_path)
else:
raise ValueError("No Input image!")
self.im_size = self.im.shape
self.im_height, self.im_width = self.im_size
self.im_label = np.full_like(self.im, 0)
#import ipdb; ipdb.set_trace()
self.max_label = 0
def imread(self, img_path):
self.im_path = im_path
self.im = cv2.imread(im_path)
def start(self):
for x0 in range(self.im_height):
for y0 in range(self.im_width):
if self.im_label[x0,y0] == 0 and self.im[x0,y0] != 0: # self.im[x0,y0]!=0 get rid of background
self.max_label += 1
self.im_label[x0,y0] = self.max_label
self.stack.push((x0,y0))
while not self.stack.is_empty():
x,y = self.stack.pop()
self.grow(x,y)
cv2.imshow('growseed',self.im_label/self.max_label)
cv2.waitKey(0)
def grow(self, x0,y0):
current_label = self.im_label[x0,y0]
for x,y in self._get_neighbour(x0,y0):
if self.im_label[x,y] == 0 and self.im[x,y] == self.im[x0,y0]: # threshold
self.im_label[x,y] = current_label
self.stack.push((x,y))
#print((self.im_label/self.max_label).shape)
#cv2.imshow('growseed',self.im_label/self.max_label)
#cv2.waitKey(1)
def _get_neighbour(self, x0, y0):
neighbour = []
for i in (-1,0,1):
for j in (-1,0,1):
if (i,j) == (0,0):
continue
x = x0+i
y = y0+j
if self._in_region(x,y):
neighbour.append((x,y))
return neighbour
def _in_region(self, x,y):
return True if 0<=x<self.im_height and 0<=y<self.im_width else False
