-
Notifications
You must be signed in to change notification settings - Fork 2
/
seg5.py
32 lines (24 loc) · 962 Bytes
/
seg5.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import cv2
image = cv2.imread('a1.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
D = ndimage.distance_transform_edt(thresh)
localMax = peak_local_max(D, indices=False, min_distance=0,labels=thresh)
markers = ndimage.label(localMax,structure=np.ones((3,3)))[0]
labels = watershed(-D, markers, mask = thresh)
for label in np.unique(labels):
if label == 0:
continue
mask = np.zeros(gray.shape, dtype="uint8")
mask[labels == label] = 255
contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
c = max(contours, key=cv2.contourArea)
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),1)
cv2.imshow("Output", image)
cv2.waitKey(0)