added kmeans image segmentation tutorial

Author: x4nth055

Diff for: README.md

@@ -31,6 +31,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Detect Shapes in Images in Python](https://www.thepythoncode.com/article/detect-shapes-hough-transform-opencv-python). ([code](machine-learning/shape-detection))
         - [How to Detect Contours in Images using OpenCV in Python](https://www.thepythoncode.com/article/contour-detection-opencv-python). ([code](machine-learning/contour-detection))
         - [How to Recognize Optical Characters in Images in Python](https://www.thepythoncode.com/article/optical-character-recognition-pytesseract-python). ([code](machine-learning/optical-character-recognition))
+        - [How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python). ([code](machine-learning/kmeans-image-segmentation))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
     - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

Diff for: machine-learning/kmeans-image-segmentation/README.md

@@ -0,0 +1,8 @@
+# [How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- If you want to perform image segmentation on the image `image.jpg`:
+    ```
+    python kmeans_segmentation.py image.jpg
+    ```
+- For live camera, consider using `live_kmeans_segmentation.py`.

Diff for: machine-learning/kmeans-image-segmentation/image.jpg

@@ -0,0 +1,47 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+# read the image
+image = cv2.imread(sys.argv[1])
+
+# convert to RGB
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# reshape the image to a 2D array of pixels and 3 color values (RGB)
+pixel_values = image.reshape((-1, 3))
+# convert to float
+pixel_values = np.float32(pixel_values)
+
+print(pixel_values.shape)
+# define stopping criteria
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
+
+# number of clusters (K)
+k = 3
+compactness, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+
+print(labels)
+print(centers.shape)
+
+# convert back to 8 bit values
+centers = np.uint8(centers)
+
+# convert all pixels to the color of the centroids
+segmented_image = centers[labels.flatten()]
+
+# reshape back to the original image dimension
+segmented_image = segmented_image.reshape(image.shape)
+
+# show the image
+plt.imshow(segmented_image)
+plt.show()
+
+# disable only the cluster number 2
+masked_image = np.copy(image)
+masked_image[labels == 2] = [0, 0, 0]
+
+# show the image
+plt.imshow(masked_image)
+plt.show()

Diff for: machine-learning/kmeans-image-segmentation/kmeans_segmentation.py

@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+
+cap = cv2.VideoCapture(0)
+k = 5
+
+# define stopping criteria
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
+
+while True:
+    # read the image
+    _, image = cap.read()
+
+    # reshape the image to a 2D array of pixels and 3 color values (RGB)
+    pixel_values = image.reshape((-1, 3))
+    # convert to float
+    pixel_values = np.float32(pixel_values)
+
+    # number of clusters (K)
+    _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+
+    # convert back to 8 bit values
+    centers = np.uint8(centers)
+
+    # convert all pixels to the color of the centroids
+    segmented_image = centers[labels.flatten()]
+
+    # reshape back to the original image dimension
+    segmented_image = segmented_image.reshape(image.shape)
+
+    # reshape labels too
+    labels = labels.reshape(image.shape[0], image.shape[1])
+
+    cv2.imshow("segmented_image", segmented_image)
+    # visualize each segment
+
+    if cv2.waitKey(1) == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Diff for: machine-learning/kmeans-image-segmentation/live_kmeans_segmentation.py

@@ -0,0 +1,3 @@
+opencv-python
+numpy
+matplotlib