Python进行图片t-SNE降维可视化

文章目录

• 问题描述
• 解决方案
• IPython代码
• 参考文献

问题描述

Python进行图片t-SNE降维可视化

解决方案

下载数据集 plant-seedlings-classification 后解压，把 train.zip 放在根目录下解压

IPython代码

%matplotlib inline

import os
import cv2
import matplotlib
import numpy as np
from glob import glob
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
           

BASE_DATA_FOLDER = "./"
TRAIN_DATA_FOLDER = os.path.join(BASE_DATA_FOLDER, "train")
           

def create_mask_for_plant(image):
    image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    sensitivity = 35
    lower_hsv = np.array([60 - sensitivity, 100, 50])
    upper_hsv = np.array([60 + sensitivity, 255, 255])

    mask = cv2.inRange(image_hsv, lower_hsv, upper_hsv)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
    
    return mask

def segment_plant(image):
    mask = create_mask_for_plant(image)
    output = cv2.bitwise_and(image, image, mask = mask)
    return output

def visualize_scatter(data_2d, label_ids, figsize=(20,20)):
    plt.figure(figsize=figsize)
    plt.grid()
    
    nb_classes = len(np.unique(label_ids))
    
    for label_id in np.unique(label_ids):
        plt.scatter(data_2d[np.where(label_ids == label_id), 0],
                    data_2d[np.where(label_ids == label_id), 1],
                    marker='o',
                    color= plt.cm.Set1(label_id / float(nb_classes)),
                    linewidth='1',
                    alpha=0.8,
                    label=id_to_label_dict[label_id])
    plt.legend(loc='best')
           

images = []
labels = []

for class_folder_name in os.listdir(TRAIN_DATA_FOLDER):
    class_folder_path = os.path.join(TRAIN_DATA_FOLDER, class_folder_name)
    for image_path in glob(os.path.join(class_folder_path, "*.png")):
        image = cv2.imread(image_path, cv2.IMREAD_COLOR)
        
        image = cv2.resize(image, (150, 150))
        image = segment_plant(image)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.resize(image, (45,45))
        
        image = image.flatten()
        
        images.append(image)
        labels.append(class_folder_name)
        
images = np.array(images)
labels = np.array(labels)
           

指定图片格式为.png

等待运行完毕

label_to_id_dict = {v:i for i,v in enumerate(np.unique(labels))}
id_to_label_dict = {v: k for k, v in label_to_id_dict.items()}

label_ids = np.array([label_to_id_dict[x] for x in labels])
           

id_to_label_dict
           

{0: 'Black-grass',
 1: 'Charlock',
 2: 'Cleavers',
 3: 'Common Chickweed',
 4: 'Common wheat',
 5: 'Fat Hen',
 6: 'Loose Silky-bent',
 7: 'Maize',
 8: 'Scentless Mayweed',
 9: 'Shepherds Purse',
 10: 'Small-flowered Cranesbill',
 11: 'Sugar beet'}
           

images_scaled.shape
           

(4750, 2025)
           

label_ids.shape
           

(2435,)
           

pca = PCA(n_components=180)
pca_result = pca.fit_transform(images_scaled)
           

pca_result.shape
           

(4750, 180)
           

tsne = TSNE(n_components=2, perplexity=40.0)
tsne_result = tsne.fit_transform(pca_result)
tsne_result_scaled = StandardScaler().fit_transform(tsne_result)
visualize_scatter(tsne_result_scaled, label_ids)
           

等待运行完毕

其他图请自行查阅参考文献：

3D图，gif动图看原文献

参考文献

1. Plants PCA & t-SNE | Kaggle
2. 从SNE到t-SNE再到LargeVis