K-Means
```python
import cv2
import numpy as np
import matplotlib.pyplot as plt
image = cv2.imread('./Lenna.png').astype(np.float32) / 255.
image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
data = image_lab.reshape((-1, 3))
num_classes = 8
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 50, 0.1)
_, labels, centers = cv2.kmeans(data, num_classes, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
segmented_lab = centers[labels.flatten()].reshape(image.shape)
segmented = cv2.cvtColor(segmented_lab, cv2.COLOR_Lab2RGB)
plt.subplot(121)
plt.axis('off')
plt.title('original')
plt.imshow(image[:,:,[2,1,0]])
plt.subplot(122)
plt.axis('off')
plt.title('segmented')
plt.imshow(segmented)
plt.show()
```
Watershed
```python
import cv2
import numpy as np
from random import randint
img = cv2.imread('./lenna.png')
show_img = np.copy(img)
seeds = np.full(img.shape[0:2], 0, np.int32)
segmentation = np.full(img.shape, 0, np.uint8)
n_seeds = 9
colors = []
for m in range(n_seeds):
colors.append((255 * m / n_seeds, randint(0, 255), randint(0, 255)))
mouse_pressed = False
current_seed = 1
seeds_updated = False
def mouse_callback(event, x, y, flags, param):
global mouse_pressed, seeds_updated
if event == cv2.EVENT_LBUTTONDOWN:
mouse_pressed = True
cv2.circle(seeds, (x, y), 5, (current_seed), cv2.FILLED)
cv2.circle(show_img, (x, y), 5, colors[current_seed - 1], cv2.FILLED)
seeds_updated = True
elif event == cv2.EVENT_MOUSEMOVE:
if mouse_pressed:
cv2.circle(seeds, (x, y), 5, (current_seed), cv2.FILLED)
cv2.circle(show_img, (x, y), 5, colors[current_seed - 1], cv2.FILLED)
seeds_updated = True
elif event == cv2.EVENT_LBUTTONUP:
mouse_pressed = False
cv2.namedWindow('image')
cv2.setMouseCallback('image', mouse_callback)
while True:
cv2.imshow('segmentation', segmentation)
cv2.imshow('image', show_img)
k = cv2.waitKey(1)
if k == 27:
break;
elif k == ord('c'):
show_img = np.copy(img)
seeds = np.full(img.shape[0:2], 0, np.int32)
segmentation = np.full(img.shape, 0, np.uint8)
elif k > 0 and chr(k).isdigit():
n = int(chr(k))
if 1 <= n <= n_seeds and not mouse_pressed:
current_seed = n
if seeds_updated and not mouse_pressed:
seeds_copy = np.copy(seeds)
cv2.watershed(img, seeds_copy)
segmentation = np.full(img.shape, 0, np.uint8)
for m in range(n_seeds):
segmentation[seeds_copy == (m + 1)] = colors[m]
seeds_updated = False
cv2.destroyAllWindows()
```
Grabcut
```python
import cv2
import numpy as np
img = cv2.imread('./lenna.png', cv2.IMREAD_COLOR)
show_img = np.copy(img)
mouse_pressed = False
y = x = w = h = 0
def mouse_callback(event, _x, _y, flags, param):
global show_img, x, y, w, h, mouse_pressed
if event == cv2.EVENT_LBUTTONDOWN:
mouse_pressed = True
x, y, = _x, _y
show_img = np.copy(img)
elif event == cv2.EVENT_MOUSEMOVE:
if mouse_pressed:
show_img = np.copy(img)
cv2.rectangle(show_img, (x, y), (_x, _y), (0, 255, 0), 3)
elif event == cv2.EVENT_LBUTTONUP:
mouse_pressed = False
w, h = _x - x, _y - y
cv2.namedWindow('image')
cv2.setMouseCallback('image', mouse_callback)
while True:
cv2.imshow('image', show_img)
k = cv2.waitKey(1)
if k == ord('a') and not mouse_pressed:
if w * h > 0:
break
cv2.destroyAllWindows()
labels = np.zeros(img.shape[:2], np.uint8)
labels, bgdModel, fgdModel = cv2.grabCut(img, labels, (x, y, w, h), None, None, 5, cv2.GC_INIT_WITH_RECT)
show_img = np.copy(img)
show_img[(labels == cv2.GC_PR_BGD) | (labels == cv2.GC_BGD)] //= 3
cv2.imshow('image', show_img)
cv2.waitKey()
cv2.destroyAllWindows()
label = cv2.GC_BGD
lbl_clrs = {cv2.GC_BGD: (0, 0, 0), cv2.GC_FGD: (255, 255, 255)}
def mouse_callback(event, x, y, flags, param):
global mouse_pressed
if event == cv2.EVENT_LBUTTONDOWN:
mouse_pressed = True
cv2.circle(labels, (x, y), 5, label, cv2.FILLED)
cv2.circle(show_img, (x, y), 5, lbl_clrs[label], cv2.FILLED)
elif event == cv2.EVENT_MOUSEMOVE:
if mouse_pressed:
cv2.circle(labels, (x, y), 5, label, cv2.FILLED)
cv2.circle(show_img, (x, y), 5, lbl_clrs[label], cv2.FILLED)
elif event == cv2.EVENT_LBUTTONUP:
mouse_pressed = False
cv2.namedWindow('image')
cv2.setMouseCallback('image', mouse_callback)
while True:
cv2.imshow('image', show_img)
k = cv2.waitKey(1)
if k == ord('a') and not mouse_pressed:
break
elif k == ord('1'):
label = cv2.GC_FGD - label
cv2.destroyAllWindows()
labels, bgdModel, fgdModel = cv2.grabCut(img, labels, None, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_MASK)
show_img = np.copy(img)
show_img[(labels == cv2.GC_PR_BGD) | (labels == cv2.GC_BGD)] //= 3
cv2.imshow('image', show_img)
cv2.waitKey()
cv2.destroyAllWindows()
```