from .imageUtils import cropImage
from skimage import exposure, morphology, measure, draw
import numpy as np
import matplotlib.pyplot as plt
import math
def watershed_image(img):
"""
use watershed flooding algorithm to extract the loop contour
:param img: type(numpy.ndarray) image in CHW format
:return: type(numpy.ndarray) image in HW format
"""
img_gray = img[1,:,:]
h, w = img_gray.shape
img1 = exposure.equalize_hist(img_gray)
# invert the image
img2 = np.max(img1) - img1
inner = np.zeros((h, w), np.bool)
centroid = [round(a) for a in findCentroid(img2)]
inner[centroid[0], centroid[1]] = 1
min_size = round((h + w) / 20)
kernel = morphology.disk(min_size)
inner = morphology.dilation(inner, kernel)
out = np.zeros((h,w), np.bool)
out[0, 0] = 1
out[h - 1, 0] = 1
out[0, w - 1] = 1
out[h - 1, w - 1] = 1
out = morphology.dilation(out, kernel)
out[0, :] = 1
out[h - 1, :] = 1
out[:, w - 1] = 1
out[:, 0] = 1
markers = np.zeros((h, w), np.int)
markers[inner] = 2
markers[out] = 1
labels = morphology.watershed(img2, markers)
return labels
def findCentroid(img):
"""
find the centroid position of a image by weighted method
:param img: (numpy.ndarray) image in HW format
:return: (tuple) (y,x) coordinates of the centroid
"""
h, w = img.shape
# add weighted method later
return h/2, w/2
def flood_fitting(img):
"""
Use watershed flooding algorithm and regional property analysis
to output the fitted ellipse parameters
:param img: (numpy.ndarray) image in CHW format
:return: region property, where property can be accessed through attributes
example:
area, bbox, centroid, major_axis_length, minor_axis_length, orientation
"""
labels = watershed_image(img)
results = measure.regionprops(labels - 1)
sorted(results, key=lambda k: k['area'],reverse=True)
# return the one with largest area
return results[0]
def show_fitted_ellipse(img):
"""
Show fitted ellipse on the image
:param img: img in CHW format
:return: plot ellipse on top of the image
"""
region1 = flood_fitting(img)
rr, cc = draw.ellipse_perimeter(int(region1['centroid'][0]), int(region1['centroid'][1]),
int(region1['minor_axis_length'] / 2),
int(region1['major_axis_length'] / 2), -region1['orientation'], img.shape[1:])
plt.imshow(img[1,:,:], cmap='gray')
plt.plot(cc, rr, '.')
def img_ellipse_fitting(img, bboxes):
subimages, bboxes = cropImage(img, bboxes)
y_points = np.array([])
x_points = np.array([])
for subim, bbox in zip(subimages, bboxes):
region1 = flood_fitting(subim)
result = (int(region1['centroid'][0]+bbox[0]), int(region1['centroid'][1]+bbox[1]),
int(region1['minor_axis_length'] / 2), int(region1['major_axis_length'] / 2),
-region1['orientation'])
rr,cc = draw.ellipse_perimeter(*result)
y_points = np.concatenate((y_points,rr))
x_points = np.concatenate((x_points,cc))
fig = plt.figure(figsize=(10,10))
plt.imshow(img[0,:,:], cmap='gray')
plt.scatter(x_points,y_points,s=(1*72./fig.dpi)**2,alpha=0.5)
def img_ellipse_fitting_area(img, bboxes):
subimages, bboxes = cropImage(img, bboxes)
ellipse_info_list = list()
for subim, bbox in zip(subimages, bboxes):
region1 = flood_fitting(subim)
result = (int(region1['centroid'][0]+bbox[0]), int(region1['centroid'][1]+bbox[1]),
int(region1['minor_axis_length'] / 2), int(region1['major_axis_length'] / 2),
-region1['orientation'])
ellipse_info_list.append(result)
area = list()
for item in enumerate(ellipse_info_list):
area.append(math.pi * item[1][1] * item[1][2])
plt.hist(area, bins=50)
plt.xlabel('Area of ellipse')
plt.ylabel('Frequency')
plt.title(r'$\mathrm{Distribution\ of\ Ellipse\ Area}$')
fig = plt.figure(figsize=(10, 10))
plt.show()
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