Changed plotting

kmeans_mpl.py

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+import os, shutil
+import numpy as np
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import plotly.tools as tls
+import plotly.express as px
+import cv2
+
+# READ IMAGE
+img = cv2.imread('images/minimalist_landscape1.jpg')
+resized_img = cv2.resize(img, (30, 30))
+
+def split_rgb(image):
+    b, g, r = cv2.split(image)
+    return r, g, b
+
+def flatten(arr):
+    n = arr.size
+    return arr.reshape([n,])
+
+img_r, img_g, img_b = split_rgb(resized_img)
+flatten_img_r, flatten_img_g, flatten_img_b = list(map(flatten, [img_r, img_g, img_b]))
+pixels = np.stack([flatten_img_r, flatten_img_g, flatten_img_b], axis=1)
+
+def distance(p1, p2):
+    """
+    Returns the Euclidean distance
+    """
+    return np.sqrt(np.sum((p2 - p1) * (p2 - p1), axis=1))
+
+def range_of(data):
+    return {"min" : np.array([np.min(data, axis=0)]), "max" : np.array([np.max(data, axis=0)])}
+
+def random_in_range(data):
+    data_range = range_of(data)
+    data_min, data_max = data_range["min"], data_range["max"]
+    data_diff = data_max - data_min
+    return data_min + np.array([np.random.rand(data.shape[1])]) * data_diff
+
+def initialize_random_centroids(data, k):
+    centroids = np.empty((0, data.shape[1]))
+    for _ in range(k):
+        centroids = np.concatenate([centroids, random_in_range(data)])
+    return centroids
+
+def cluster_data(data, centroids):
+    clusters = {}
+    for i in range(1, len(centroids) + 1):
+        clusters[i] = np.empty((0, data.shape[1]))
+    for point in data:
+        nearest_centroid = int(np.argmin(distance(np.array([point]), centroids))) + 1
+        clusters[nearest_centroid] = np.concatenate([clusters[nearest_centroid], np.array([point])])
+    return clusters
+
+def cost_function(data, centroids, clusters):
+    total_cost = 0
+    n = data.shape[0]
+    for centroid_num in clusters:
+        d = distance(clusters[centroid_num], centroids[centroid_num - 1])
+        total_cost += np.sum(d)
+    return total_cost / n
+
+def update_centroids(data, centroids, clusters):
+    change = False
+    for centroid_num in clusters:
+        if clusters[centroid_num].shape[0] == 0:
+            pass
+        else:
+            points_in_cluster = clusters[centroid_num]
+            new_centroid = np.sum(points_in_cluster, axis=0) / points_in_cluster.shape[0]
+            if not np.array_equal(centroids[centroid_num - 1], new_centroid):
+                change = True
+                centroids[centroid_num - 1] = new_centroid
+    return centroids, change
+
+def k_means(data, k, directory_name):
+    reset()
+
+    centroids = initialize_random_centroids(data, k)
+    clusters = cluster_data(data, centroids)
+    plot_rgb(data, centroids, "Initialize Random Centroids", directory_name + 'iteration_start' + '.png')
+
+
+    centroids_changed = True
+    i = 1
+    while centroids_changed:
+        print("Iteration:", i)
+        clusters = cluster_data(data, centroids)
+        centroids, centroids_changed = update_centroids(data, centroids, clusters)
+        plot_rgb(data, centroids, i, directory_name + 'iteration_' + str(i) + '.png')
+
+        i += 1
+    print("Cost:", cost_function(data, centroids, clusters))
+    return centroids
+
+def plot_rgb(data, centroids, iteration, file_path, end = False):
+    fig = plt.figure(figsize=(8, 6))
+    ax = fig.add_subplot(111, projection='3d')
+    ax.set_xlabel("Red")
+    ax.set_ylabel("Green")
+    ax.set_zlabel("Blue")
+    ax.set_title("Iteration: " + str(iteration))
+
+    ax.xaxis.labelpad, ax.yaxis.labelpad, ax.zaxis.labelpad = 10, 10, 10
+    ax.title.set_position([0.85, 1])
+    ax.title.set_size(10) 
+
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.set_zticks([])
+
+    r, g, b = data[:, 0], data[:, 1], data[:, 2]
+    point_opacity = 0.7
+    point_edge_color = np.hstack([data/255, np.ones([data.shape[0], 1]) * point_opacity]) # sets color of each point to the pixel value with point_opacity
+    ax.scatter3D(r, g, b, edgecolor=point_edge_color, facecolor=np.zeros([data.shape[0], 4]), zorder=1) # facecolor = transparent
+
+    for r, g, b in centroids:
+        ax.scatter3D(r, g, b, s=200, facecolor=(r/255, g/255, b/255), edgecolor="black", zorder=2)
+    fig.savefig(file_path, dpi=300)
+
+    if end:
+        pass
+        plt.show()
+    else:
+        fig.close()
+
+    return fig, ax   
+
+def reset():
+    shutil.rmtree("figs")
+    os.mkdir('figs')
+
+def run_kmeans():
+    reset()
+
+    centroids_result = k_means(pixels, 4, 'figs/')
+    final_plot, final_ax = plot_rgb(pixels, centroids_result, "Final", 'figs/iteration_final.png', True)
+

kmeans_plotly.py

@@ -0,0 +1,152 @@
+import os
+import numpy as np
+import pandas as pd
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import plotly.tools as tls
+import plotly.graph_objects as go
+import cv2
+
+# READ IMAGE
+img = cv2.imread('images/minimalist_landscape1.jpg')
+resized_img = cv2.resize(img, (30, 30))
+
+def split_rgb(image):
+    b, g, r = cv2.split(image)
+    return r, g, b
+
+def flatten(arr):
+    n = arr.size
+    return arr.reshape([n,])
+
+img_r, img_g, img_b = split_rgb(resized_img)
+flatten_img_r, flatten_img_g, flatten_img_b = list(map(flatten, [img_r, img_g, img_b]))
+pixels = np.stack([flatten_img_r, flatten_img_g, flatten_img_b], axis=1)
+
+def distance(p1, p2):
+    """
+    Returns the Euclidean distance
+    """
+    return np.sqrt(np.sum((p2 - p1) * (p2 - p1), axis=1))
+
+def range_of(image_data):
+    return {"min" : np.array([np.min(image_data, axis=0)]), "max" : np.array([np.max(image_data, axis=0)])}
+
+def random_in_range(image_data):
+    data_range = range_of(image_data)
+    data_min, data_max = data_range["min"], data_range["max"]
+    data_diff = data_max - data_min
+    return data_min + np.array([np.random.rand(image_data.shape[1])]) * data_diff
+
+def initialize_random_centroids(image_data, k):
+    centroids = np.empty((0, image_data.shape[1]))
+    for _ in range(k):
+        centroids = np.concatenate([centroids, random_in_range(image_data)])
+    return centroids
+
+def cluster_data(image_data, centroids):
+    clusters = {}
+    for i in range(1, len(centroids) + 1):
+        clusters[i] = np.empty((0, image_data.shape[1]))
+    for point in image_data:
+        nearest_centroid = int(np.argmin(distance(np.array([point]), centroids))) + 1
+        clusters[nearest_centroid] = np.concatenate([clusters[nearest_centroid], np.array([point])])
+    return clusters
+
+def cost_function(image_data, centroids, clusters):
+    total_cost = 0
+    n = image_data.shape[0]
+    for centroid_num in clusters:
+        d = distance(clusters[centroid_num], centroids[centroid_num - 1])
+        total_cost += np.sum(d)
+    return total_cost / n
+
+def update_centroids(image_data, centroids, clusters):
+    change = False
+    for centroid_num in clusters:
+        if clusters[centroid_num].shape[0] == 0:
+            pass
+        else:
+            points_in_cluster = clusters[centroid_num]
+            new_centroid = np.sum(points_in_cluster, axis=0) / points_in_cluster.shape[0]
+            if not np.array_equal(centroids[centroid_num - 1], new_centroid):
+                change = True
+                centroids[centroid_num - 1] = new_centroid
+    return centroids, change
+
+def k_means(image_data, k, directory_name):
+    reset()
+
+    centroids = initialize_random_centroids(image_data, k)
+    clusters = cluster_data(image_data, centroids)
+
+    centroids_changed = True
+    i = 1
+    while centroids_changed:
+        print("Iteration:", i)
+        plot_rgb(image_data, centroids, i, directory_name + 'iteration_' + str(i) + '.html')
+        clusters = cluster_data(image_data, centroids)
+        centroids, centroids_changed = update_centroids(image_data, centroids, clusters)
+        i += 1
+    print("Cost:", cost_function(image_data, centroids, clusters))
+    return centroids
+
+def to_rgb(values):
+    color_dict = {}
+    for i in range(len(values)):
+        color_dict[i] = "rgb(" + str(values[i, 0]) + ", " + str(values[i, 1]) + ", " + str(values[i, 2]), ")"
+    return color_dict
+
+def plot_rgb(image_data, centroids, iteration, file_path, show = False):
+    data_df = pd.DataFrame(data=image_data, columns=["Red", "Green", "Blue"])
+    fig = go.Figure(data=[go.Scatter3d(
+        x = image_data[:, 0],
+        y = image_data[:, 1],
+        z = image_data[:, 2],
+        mode = 'markers'
+        marker = dict(
+            color = 
+        )
+    )])
+    fig.write_html(file_path)
+    # fig = plt.figure(figsize=(8, 6))
+    # ax = plt.axes(projection='3d')
+    # ax.set_xlabel("Red")
+    # ax.set_ylabel("Green")
+    # ax.set_zlabel("Blue")
+    # ax.set_title("Iteration: " + str(iteration))
+
+    # ax.xaxis.labelpad, ax.yaxis.labelpad, ax.zaxis.labelpad = 10, 10, 10
+    # ax.title.set_position([0.85, 1])
+    # ax.title.set_size(10) 
+
+    # ax.set_xticks([])
+    # ax.set_yticks([])
+    # ax.set_zticks([])
+
+    # r, g, b = data[:, 0], data[:, 1], data[:, 2]
+    # point_opacity = 0.7
+    # point_edge_color = np.hstack([data/255, np.ones([data.shape[0], 1]) * point_opacity]) # sets color of each point to the pixel value with point_opacity
+    # ax.scatter3D(r, g, b, edgecolor=point_edge_color, facecolor=np.zeros([data.shape[0], 4]), zorder=1) # facecolor = transparent
+
+    # for r, g, b in centroids:
+    #     ax.scatter3D(r, g, b, s=200, facecolor=(r/255, g/255, b/255), edgecolor="black", zorder=2)
+    # fig.savefig(file_path, dpi=300)
+
+    # if show:
+    #     plt.show()
+
+def reset():
+    directory = 'figs/'
+    for filename in os.listdir(directory):
+        file_path = os.path.join(directory, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+        except:
+            print("Failed to delete")
+
+centroids_result = k_means(pixels, 4, 'figs/')
+# plot_rgb(pixels, centroids_result, "Final", 'figs/iteration_final.png', True)