Add k-means 8bit picture code

content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/application/color8bit_style.py

@@ -106,4 +106,4 @@ plt.imshow(reshaped_image)
 plt.show()
 
 # Opencv store image
-cv2.imwrite('C:/Users/BME51/Desktop/color8bit_style.jpg', reshaped_image_bgr)
+cv2.imwrite('./color8bit_style.jpg', reshaped_image_bgr)

content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/k_means.md

@@ -96,7 +96,118 @@ Read image and use k-means to do clustering for pixel value. Make pic to 8bit co
 
 ![](computer_sci/deep_learning_and_machine_learning/clustering/k-means/attachments/3ed5fee41bd566be093bebd62a33d12.jpg)
 
-[color8bit_style.py](https://github.com/PinkR1ver/Jude.W-s-Knowledge-Brain/blob/master/Deep_Learning_And_Machine_Learning/clustering/k-means/application/color8bit_style.py)
+```python
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from tkinter import Tk, filedialog
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cluster import KMeans
+
+
+# Create a Tkinter root window
+root = Tk()
+root.withdraw()
+
+# Open a file explorer dialog to select an image file
+file_path = filedialog.askopenfilename()
+
+# Read the selected image using cv2
+image = cv2.imread(file_path)
+
+# Convert the image to RGB color space
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# Get the dimensions of the image
+height, width, _ = image_rgb.shape
+
+# Reshape the image to a 2D array of pixels, one is pixel number, one is pixel channel
+pixels = image_rgb.reshape((height * width, 3))
+
+# Create an empty dataset
+dataset = []
+
+# Iterate over each pixel and store the RGB values as a vector in the dataset
+for pixel in pixels:
+    dataset.append(pixel)
+
+# Convert the dataset to a NumPy array
+dataset = np.array(dataset)
+
+# Get the RGB values from the dataset
+red = dataset[:, 0]
+green = dataset[:, 1]
+blue = dataset[:, 2]
+
+
+
+# plot show
+'''
+# Plot the histograms
+plt.figure(figsize=(10, 6))
+plt.hist(red, bins=256, color='red', alpha=0.5, label='Red')
+plt.hist(green, bins=256, color='green', alpha=0.5, label='Green')
+plt.hist(blue, bins=256, color='blue', alpha=0.5, label='Blue')
+plt.title('RGB Value Histogram')
+plt.xlabel('RGB Value')
+plt.ylabel('Frequency')
+plt.legend()
+plt.show()
+
+
+# Plot the 3D scatter graph
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(red, green, blue, c='#000000', s=1)
+ax.set_xlabel('Red')
+ax.set_ylabel('Green')
+ax.set_zlabel('Blue')
+ax.set_title('RGB Scatter Plot')
+plt.show()
+'''
+
+
+# Perform k-means clustering
+num_clusters = 3  # Specify the desired number of clusters
+kmeans = KMeans(n_clusters=num_clusters, n_init='auto', random_state=42)
+labels = kmeans.fit_predict(dataset)
+
+
+# Show K-means Clustering result
+'''
+# Plot the scatter plot for each iteration of the k-means algorithm
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+
+for i in range(num_clusters):
+    cluster_points = dataset[labels == i]
+    ax.scatter(cluster_points[:, 0], cluster_points[:, 1], cluster_points[:, 2], s=1)
+
+ax.set_xlabel('Red')
+ax.set_ylabel('Green')
+ax.set_zlabel('Blue')
+ax.set_title('RGB Scatter Plot - K-Means Clustering')
+plt.show()
+'''
+
+center_values = kmeans.cluster_centers_.astype(int)
+
+for i in range(num_clusters):
+    dataset[labels == i] = center_values[i]
+
+
+# Reshape the pixels array back into an image with the original dimensions and convert it to BGR color space
+reshaped_image = dataset.reshape((height, width, 3))
+reshaped_image_bgr = cv2.cvtColor(reshaped_image.astype(np.uint8), cv2.COLOR_RGB2BGR)
+
+# Display the image using matplotlib
+plt.imshow(reshaped_image)
+plt.show()
+
+# Opencv store image
+cv2.imwrite('./color8bit_style.jpg', reshaped_image_bgr)
+
+```
 
 # Reference