def extract(img_copy):
img=img_copy.copy()
junctions,end_points=special_points(img)
img=global_vars.add_borders(img)
visited=np.zeros(img.shape)
comps=[]
minima=[]
maxima=[]
for y in range(img.shape[1]):
for x in range(img.shape[0]-1,-1,-1):
if img[x,y]:
#print (visited)
if not(visited[x,y]):
queue=deque()
strokes=[]
queue.append([x,y])
while len(queue):
[current_x,current_y]=queue.popleft()
#stroke,nbr,visited,maxima,minima=build_stroke(current_x,current_y,img,visited,maxima,minima)
stroke,visited,maxima,minima,queue=build_stroke2(current_x,current_y,img,visited,maxima,minima,junctions,queue)
if len(stroke.stroke_points):
strokes.append(stroke)
comps.append(Component(strokes=strokes))
img=global_vars.delete_borders(img)
return comps,maxima,minima,junctions,end_points
def skeletonizer(image):
image2 = image.copy()
image2 = global_vars.add_borders(image2)
iterations = 0
while True:
iterations += 1
image4 = image2.copy()
#while True:
#image3=image2.copy()
for x in range(1, image2.shape[0] - 1):
y = 1
while y < image2.shape[1] - 1:
if image2[x, y]:
y_start = y
while y < image2.shape[1] - 1 and image2[x, y]:
y += 1
y_end = y - 1
if y_end != y_start:
p = neighbours(x, y_end, image2)
if not (
(p[2] and not (p[1])) or
(p[4] and not (p[5])) or sum(p) == 1
): #p[0]+p[1]+p[2]+p[3]+p[4]+p[5]!=0 and p[1]+p[2]+p[3]+p[4]+p[5]+p[6]!=0:
image2[x, y_end] = 0
if image2[x, y_start + 1]:
p = neighbours(x, y_start, image2)
if not ((p[0] and not (p[1])) or
(p[6] and not (p[5])) or sum(p) == 1):
image2[x, y_start] = 0
y += 1
#if np.array_equal(image3,image2): break
#while True:
#image3=image2.copy()
for y in range(1, image2.shape[1] - 1):
x = 1
while x < image2.shape[0] - 1:
if image2[x, y]:
x_start = x
while x < image2.shape[0] - 1 and image2[x, y]:
x += 1
x_end = x - 1
if x_end != x_start:
p = neighbours(x_end, y, image2)
if not (
(p[6] and not (p[7])) or
(p[4] and not (p[3])) or sum(p) == 1
): #p[2]+p[3]+p[4]+p[5]+p[6]+p[7]!=0 and p[3]+p[4]+p[5]+p[6]+p[7]!=0:
image2[x_end, y] = 0
if image2[x_start + 1, y]:
p = neighbours(x_start, y, image2)
if not (
(p[0] and not (p[7])) or
(p[2] and not (p[3])) or sum(p) == 1
): #p[6]+p[7]+[0]+[1]+[2]+[3]!=0 and p[7]+[0]+[1]+[2]+[3]+p[4]!=0:
image2[x_start, y] = 0
x += 1
#if np.array_equal(image3,image2): break
if numpy.array_equal(image4, image2): break
image2 = global_vars.delete_borders(image2)
return image2
def smooth_contours(img_copy):
img = img_copy.copy()
#print (type(img[0,0]))
#img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = global_vars.add_borders(img)
kernel1 = np.array([[1, 1, 0], [1, -1, 0], [1, 1, 0]], dtype=np.int8)
kernel2 = np.array([[1, 1, 1], [1, -1, 1], [0, 0, 0]], dtype=np.int8)
kernel3 = np.array([[0, 1, 1], [0, -1, 1], [0, 1, 1]], dtype=np.int8)
kernel4 = np.array([[0, 0, 0], [1, -1, 1], [1, 1, 1]], dtype=np.int8)
while True:
img2 = img.copy()
img = np.array(img * 255, dtype=np.uint8)
hit_miss1 = cv2.morphologyEx(img, cv2.MORPH_HITMISS, kernel1)
hit_miss2 = cv2.morphologyEx(img, cv2.MORPH_HITMISS, kernel2)
hit_miss3 = cv2.morphologyEx(img, cv2.MORPH_HITMISS, kernel3)
hit_miss4 = cv2.morphologyEx(img, cv2.MORPH_HITMISS, kernel4)
img = np.logical_or.reduce(
(hit_miss1, hit_miss2, hit_miss3, hit_miss4, img))
img_negative = np.array(255 - img, dtype=np.uint8)
hit_miss1 = cv2.morphologyEx(img_negative, cv2.MORPH_HITMISS, kernel1)
hit_miss2 = cv2.morphologyEx(img_negative, cv2.MORPH_HITMISS, kernel2)
hit_miss3 = cv2.morphologyEx(img_negative, cv2.MORPH_HITMISS, kernel3)
hit_miss4 = cv2.morphologyEx(img_negative, cv2.MORPH_HITMISS, kernel4)
img = np.logical_or.reduce(
(hit_miss1, hit_miss2, hit_miss3, hit_miss4, img_negative))
img = 255 - img_negative
if np.array_equal(img, img2): break
img = global_vars.delete_borders(img)
return img
def special_points(skeleton_copy):
img=skeleton_copy.copy()
img=global_vars.add_borders(img)
#minima=[]
#maxima=[]
junctions=[]
end_points=[]
for x,y in np.transpose(np.nonzero(img)):
nbrs=skeletonize.neighbours(x,y,img)
if sum(nbrs)<2:
end_points.append([x-1,y-1])
"""
if (nbrs[0])[0]<x:
minima.append([x-1,y-1])
elif (nbrs[0])[0]>x:
maxima.append([x-1,y-1])
"""
elif sum(nbrs)>2:
nbrs.append(nbrs[0])
if not(contains([1,1],nbrs)):
junctions.append([x,y])
img=global_vars.delete_borders(img)
return junctions,end_points
def smooth_skeleton(skeleton_copy):
img = skeleton_copy.copy()
img = global_vars.add_borders(img)
deletion = []
addition = []
for x in range(1, img.shape[0] - 1):
y = 1
while y < img.shape[1] - 1:
if img[x, y]:
y_start = y
y += 1
#while img[x,y] :
while img[x, y]:
y += 1
y -= 1
y_end = y
start_nbrs = neighbors_x((x, y_start), img)
end_nbrs = neighbors_x((x, y_end), img)
if y_start == y_end:
if len(start_nbrs) == 2:
if (start_nbrs[0])[0] == (start_nbrs[1])[0]:
for temp_y in range(y_start, y_end + 1):
deletion.append([x, temp_y])
addition.append([(start_nbrs[0])[0], temp_y])
else:
y_arr = list(range(y_start + 1, y_end))
for item in y_arr:
if len(neighbors_x((x, item), img)):
y += 1
continue
if len(start_nbrs) == 1 and len(end_nbrs) == 1:
if (start_nbrs[0])[0] == (end_nbrs[0])[0]:
for temp_y in range(y_start, y_end + 1):
deletion.append([x, temp_y])
#img[x,temp_y]=0
addition.append([(start_nbrs[0])[0], temp_y])
#img[(start_nbrs[0])[0],temp_y]=1
#img[x,temp_y]=0
#img[(start_nbrs[0])[0],temp_y]=1
y += 1
for item in deletion:
img[item[0], item[1]] = 0
for item in addition:
img[item[0], item[1]] = 1
deletion = []
addition = []
for y in range(1, img.shape[1] - 1):
x = 1
while x < img.shape[0] - 1:
if img[x, y]:
x_start = x
x += 1
#while img[x,y]:
while img[x, y]:
x += 1
x -= 1
x_end = x
start_nbrs = neighbors_y((x_start, y), img)
end_nbrs = neighbors_y((x_end, y), img)
if x_start == x_end:
if len(start_nbrs) == 2:
if (start_nbrs[0])[1] == (start_nbrs[1])[1]:
for temp_x in range(x_start, x_end + 1):
deletion.append([temp_x, y])
addition.append([temp_x, (start_nbrs[0])[1]])
else:
x_arr = list(range(x_start + 1, x_end))
for item in x_arr:
if len(neighbors_y((item, y), img)):
x += 1
continue
if len(start_nbrs) == 1 and len(end_nbrs) == 1:
if (start_nbrs[0])[1] == (end_nbrs[0])[1]:
for temp_x in range(x_start, x_end + 1):
deletion.append([temp_x, y])
addition.append([temp_x, (start_nbrs[0])[1]])
#img[temp_x,y]=0
#img[temp_x,(start_nbrs[0])[1]]=1
#img[temp_x,y]=0
#img[temp_x,(start_nbrs[0])[1]]=1
x += 1
for item in deletion:
img[item[0], item[1]] = 0
for item in addition:
img[item[0], item[1]] = 1
img = global_vars.delete_borders(img)
return img