diff --git a/Lenna.png b/Lenna.png
new file mode 100644
index 0000000..59ef68a
Binary files /dev/null and b/Lenna.png differ
diff --git a/grabcut.py b/grabcut.py
new file mode 100644
index 0000000..15036ab
--- /dev/null
+++ b/grabcut.py
@@ -0,0 +1,96 @@
+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()
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diff --git a/k-means.py b/k-means.py
new file mode 100644
index 0000000..356d276
--- /dev/null
+++ b/k-means.py
@@ -0,0 +1,25 @@
+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()
\ No newline at end of file
diff --git a/watershed.py b/watershed.py
new file mode 100644
index 0000000..eeb2616
--- /dev/null
+++ b/watershed.py
@@ -0,0 +1,71 @@
+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()
\ No newline at end of file