diff --git a/assets/Megamind.avi b/assets/Megamind.avi
new file mode 100644
index 0000000..86351eb
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diff --git a/assets/cat_damaged.png b/assets/cat_damaged.png
new file mode 100644
index 0000000..5af2762
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diff --git a/assets/cat_mask.png b/assets/cat_mask.png
new file mode 100644
index 0000000..3465a8a
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diff --git a/outputs/cat_inpainted.png b/outputs/cat_inpainted.png
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diff --git a/tutorial10.py b/tutorial10.py
new file mode 100644
index 0000000..1817a6f
--- /dev/null
+++ b/tutorial10.py
@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+import os
+
+# Playing video from file:
+cap = cv2.VideoCapture('assets/Megamind.avi')
+
+try:
+    if not os.path.exists('data-single'):
+        os.makedirs('data-single')
+except OSError:
+    print ('Error: Creating directory of data')
+
+currentFrame = 0
+while(True):
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+
+    # Saves image of the current frame in jpg file
+    name = './data-single/frame' + str(currentFrame) + '.jpg'
+    print ('Creating...' + name)
+    cv2.imwrite(name, frame)
+
+    # To stop duplicate images
+    currentFrame += 1
+
+# When everything done, release the capture
+cap.release()
+cv2.destroyAllWindows()
+
+
+#lucciffer
+
diff --git a/tutorial11.py b/tutorial11.py
new file mode 100644
index 0000000..eb770a5
--- /dev/null
+++ b/tutorial11.py
@@ -0,0 +1,51 @@
+import cv2
+import numpy as np
+
+
+def read_file(filename):
+  img = cv2.imread(filename)
+  cv2_imshow(img)
+  return img
+
+def color_quantization(img, k):
+# Transform the image
+  data = np.float32(img).reshape((-1, 3))
+
+# Determine criteria
+  criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 0.001)
+
+# Implementing K-Means
+  ret, label, center = cv2.kmeans(data, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+  center = np.uint8(center)
+  result = center[label.flatten()]
+  result = result.reshape(img.shape)
+  return result
+
+def edge_mask(img, line_size, blur_value):
+  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+  gray_blur = cv2.medianBlur(gray, blur_value)
+  edges = cv2.adaptiveThreshold(gray_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, line_size, blur_value)
+  return edges
+
+
+img = read_file(file_path)
+
+
+line_size = 5
+blur_value = 13
+
+edges = edge_mask(img, line_size, blur_value)
+cv2_imshow(edges)
+
+total_color = 7
+
+img = color_quantization(img, total_color)
+cv2_imshow(img)
+cv2.imwrite('./output_minus1.png', img)
+
+blurred = cv2.bilateralFilter(img, d=7, sigmaColor=200,sigmaSpace=200)
+cv2_imshow(blurred)
+
+cartoon = cv2.bitwise_and(blurred, blurred, mask=edges)
+cv2.imwrite('./output.png', cartoon)
+cv2_imshow(cartoon)
diff --git a/tutorial9.py b/tutorial9.py
new file mode 100644
index 0000000..add7a49
--- /dev/null
+++ b/tutorial9.py
@@ -0,0 +1,14 @@
+import numpy as np
+import cv2
+  
+# Open the image.
+img = cv2.imread('assets/cat_damaged.png')
+  
+# Load the mask.
+mask = cv2.imread('assets/cat_mask.png', 0)
+  
+# Inpaint.
+dst = cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)
+  
+# Write the output.
+cv2.imwrite('outputs/cat_inpainted.png', dst)