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snake_countor.py
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snake_countor.py
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import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageOps
from scipy import ndimage as filt
def Matrix_A(a, b, N):
"""
a: float
alpha parameter
b: float
beta parameter
N: int
N is the number of points sampled on the snake curve: (x(p_i), y(p_i)), i=0,...,N-1
"""
row = np.r_[
-2*a - 6*b,
a + 4*b,
-b,
np.zeros(N-5),
-b,
a + 4*b
]
A = np.zeros((N, N))
for i in range(N):
A[i] = np.roll(row, i)
return A
def external_edge_force(img, sigma=30.):
"""
Given an image, returns 2 functions, fx & fy, that compute
the gradient of the external edge force in the x and y directions.
img: ndarray
The image.
"""
# Gaussian smoothing.
smoothed = filt.gaussian_filter(
(img-img.min()) / (img.max()-img.min()), sigma)
# Gradient of the image in x and y directions.
giy, gix = np.gradient(smoothed)
# Gradient magnitude of the image.
gmi = (gix**2 + giy**2)**(0.5)
# Normalize. This is crucial (empirical observation).
gmi = (gmi - gmi.min()) / (gmi.max() - gmi.min())
# Gradient of gradient magnitude of the image in x and y directions.
ggmiy, ggmix = np.gradient(gmi)
def fx(x, y):
"""
Return external edge force in the x direction.
x: ndarray
numpy array of floats.
y: ndarray:
numpy array of floats.
"""
# Check bounds.
x[x < 0] = 0.
y[y < 0] = 0.
x[x > img.shape[1]-1] = img.shape[1]-1
y[y > img.shape[0]-1] = img.shape[0]-1
return ggmix[(y.round().astype(int), x.round().astype(int))]
def fy(x, y):
"""
Return external edge force in the y direction.
x: ndarray
numpy array of floats.
y: ndarray:
numpy array of floats.
"""
# Check bounds.
x[x < 0] = 0.
y[y < 0] = 0.
x[x > img.shape[1]-1] = img.shape[1]-1
y[y > img.shape[0]-1] = img.shape[0]-1
return ggmiy[(y.round().astype(int), x.round().astype(int))]
return fx, fy
def iterate_snake(x, y, a, b, fx, fy, gamma, n_iters, return_all=True):
"""
x: ndarray
intial x coordinates of the snake
y: ndarray
initial y coordinates of the snake
a: float
alpha parameter
b: float
beta parameter
fx: callable
partial derivative of first coordinate of external energy function. This is the first element of the gradient of the external energy.
fy: callable
see fx.
gamma: float
step size of the iteration
n_iters: int
number of times to iterate the snake
return_all: bool
if True, a list of (x,y) coords are returned corresponding to each iteration.
if False, the (x,y) coords of the last iteration are returned.
"""
A = Matrix_A(a, b, x.shape[0])
B = np.linalg.inv(np.eye(x.shape[0]) - gamma*A)
if return_all:
snakes = []
for i in range(n_iters):
x_ = np.dot(B, x + gamma*fx(x, y))
y_ = np.dot(B, y + gamma*fy(x, y))
x, y = x_.copy(), y_.copy()
if return_all:
snakes.append((x_.copy(), y_.copy()))
if return_all:
return snakes
else:
return (x, y)
def activeContour(img,alpha,beta,gamma,iterations,sigma):
t = np.arange(0, 2*np.pi, 0.1)
x = 153+105*np.cos(t)
y = 156+120*np.sin(t)
# fx and fy are callable functions
fx, fy = external_edge_force(img, sigma)
snakes = iterate_snake(
x=x,
y=y,
a=alpha,
b=beta,
fx=fx,
fy=fy,
gamma=gamma,
n_iters=iterations,
return_all=True
)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(img, cmap=plt.cm.gray)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim(0, img.shape[1])
ax.set_ylim(img.shape[0], 0)
ax.plot(np.r_[x, x[0]], np.r_[y, y[0]], c=(0, 1, 0), lw=2)
for i, snake in enumerate(snakes):
if i % 10 == 0:
ax.plot(np.r_[snake[0], snake[0][0]], np.r_[
snake[1], snake[1][0]], c=(0, 0, 1), lw=2)
x=snake[0]
y=snake[1]
perimeter = 0
area=0.5*np.sum(y[:-1]*np.diff(x) - x[:-1]*np.diff(y))
for i in np.arange(len(x)-1):
distance = np.sqrt(np.square(x[i+1]-x[i])+np.square(y[i+1]-y[i]))
perimeter += distance
# cnt = contours[0]
# area1 = cv2.contourArea(x) # Area of first contour
# perimeter = cv2.arcLength(x, True) # Perimeter of first contour
# area1 = cv2.contourArea(snake)
# x = snake[:, 0]
# y = snake[:, 1]
# for i in np.arange(len(x)-1):
# # calculate the distance between the current point and the next point
# distance = np.sqrt(np.square(x[i+1]-x[i])+np.square(y[i+1]-y[i]))
# perimeter += distance
# print("Detected Contour with Area: ",abs(area) )
# Plot the last one a different color.
ax.plot(np.r_[snakes[-1][0], snakes[-1][0][0]],
np.r_[snakes[-1][1], snakes[-1][1][0]], c=(1, 0, 0), lw=2)
# plt.title("Active Contour")
# plt.savefig('./output/active_contour.png', bbox_inches='tight')
# print('active_contour.png saved successfully in output directory.')
plt.savefig('images/output/snake.jpeg')
# plt.show()
return abs(area),perimeter
def resize_img(img: np.ndarray, basewidth: int = 300):
w_percent = (basewidth/float(img.size[0]))
h_size = int((float(img.size[1])*float(w_percent)))
resized_img = img.resize((basewidth, h_size), Image.ANTIALIAS)
return resized_img