diff --git a/4/1.py b/4/1.py
new file mode 100644
index 0000000..42c1feb
--- /dev/null
+++ b/4/1.py
@@ -0,0 +1,23 @@
+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+
+image = cv.imread("images/lena.png")
+gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+gray = np.float32(gray)
+
+block_size = 2
+sobel_size = 3
+k = 0.04
+
+corners_img = cv.cornerHarris(gray, block_size, sobel_size, k)  # type: ignore
+
+indices = np.argsort(corners_img.flatten())[::-1][:50]
+coords = np.column_stack(np.unravel_index(indices, corners_img.shape))
+for coord in coords:
+    cv.circle(image, (coord[1], coord[0]), 3, (255, 0, 0), 1)
+
+plt.imshow(image)
+plt.axis("off")
+plt.tight_layout()
+plt.show()
diff --git a/4/2.py b/4/2.py
new file mode 100644
index 0000000..ce9614a
--- /dev/null
+++ b/4/2.py
@@ -0,0 +1,23 @@
+import cv2 as cv
+import numpy as np
+
+img = cv.imread("images/lena.png")
+cv.imshow("raw_img", img)
+
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+gray = np.float32(gray)
+
+J = (0.05, 0.01, 0.005)
+
+for j in J:
+    dst = cv.cornerHarris(gray, 2, 3, 0.04)  # type: ignore
+    a = dst > j * dst.max()
+    for r in range(a.shape[0]):
+        for c in range(a.shape[1]):
+            if a[r, c] != 0:
+                cv.circle(img, (c, r), 1, (0, 0, 255), 1)
+    cv.imshow("corners_" + str(j), img)
+    cv.waitKey(0)
+
+cv.waitKey(0)
+cv.destroyAllWindows()
diff --git a/4/3.py b/4/3.py
new file mode 100644
index 0000000..18a51fc
--- /dev/null
+++ b/4/3.py
@@ -0,0 +1,43 @@
+import cv2 as cv
+
+
+def build_gaussian_pyramid(image, octaves, scales):
+    gaussian_pyramid = []
+    for o in range(octaves):
+        octave_images = []
+        for s in range(scales):
+            if s == 0:
+                if o == 0:
+                    octave_images.append(image)
+                else:
+                    octave_images.append(cv.pyrDown(gaussian_pyramid[o - 1][-3]))
+            else:
+                sigma = 1.6 * (2 ** (s / scales))
+                blurred = cv.GaussianBlur(octave_images[0], (5, 5), sigma)
+                octave_images.append(blurred)
+
+        gaussian_pyramid.append(octave_images)
+    return gaussian_pyramid
+
+
+def build_dog_pyramid(gaussian_pyramid):
+    dog_pyramid = []
+    for octave in gaussian_pyramid:
+        dog_images = []
+        for s in range(len(octave) - 1):
+            dog_images.append(octave[s + 1] - octave[s])
+        dog_pyramid.append(dog_images)
+    return dog_pyramid
+
+
+image = cv.imread("images/lena.jpg", cv.IMREAD_GRAYSCALE)
+guassian_pyramid = build_gaussian_pyramid(image, octaves=4, scales=5)
+dog_pyramid = build_dog_pyramid(guassian_pyramid)
+
+for i in range(len(dog_pyramid)):
+    for j in range(len(dog_pyramid[i])):
+        cv.namedWindow("DoG pyramid image %d %d" % (i, j))
+        cv.imshow("DoG pyramid image %d %d" % (i, j), dog_pyramid[i][j])
+
+cv.waitKey(0)
+cv.destroyAllWindows()