From bef0ba447079753a862774e607fa789bdabb97ca Mon Sep 17 00:00:00 2001
From: PinkR1ver <3180102330@zju.edu.cn>
Date: Tue, 21 May 2024 09:51:09 +0800
Subject: [PATCH] delete this code

---
 .../k-means/application/color8bit_style.py    | 109 ------------------
 1 file changed, 109 deletions(-)
 delete mode 100644 content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/application/color8bit_style.py

diff --git a/content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/application/color8bit_style.py b/content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/application/color8bit_style.py
deleted file mode 100644
index ab59bf891..000000000
--- a/content/computer_sci/deep_learning_and_machine_learning/clustering/k-means/application/color8bit_style.py
+++ /dev/null
@@ -1,109 +0,0 @@
-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)