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トップに関する質問matplotlibによる画像のプロット時のエラーpython

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質問編集履歴

2

文法修正

2020/12/13 14:51

投稿

kusegasugoi0221
kusegasugoi0221

スコア11

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- 画像のプロットのエラーpython
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+ matplotlibによる画像のプロット時のエラーpython
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1

コード改善

2020/12/13 14:51

投稿

kusegasugoi0221
kusegasugoi0221

スコア11

test CHANGED
File without changes
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@@ -10,15 +10,15 @@
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- File "C:\Users\tSNE_CNN.py", line 247, in <module>
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+ File "C:\Users\tSNE_CNN.py", line 248, in <module>
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  main()
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- File "C:\Users\tSNE_CNN.py", line 208, in main
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+ File "C:\Users\tSNE_CNN.py", line 209, in main
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-
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+
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- plt.scatter(X_embeddedsc[label==index,1], X_embeddedsc[label==index,2], X_embeddedsc[label==index,3],X_embeddedsc[label==index,4],
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+ plt.scatter(X_embeddedsc[label==index,0], X_embeddedsc[label==index,1], X_embeddedsc[label==index,2],X_embeddedsc[label==index,3],
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@@ -28,7 +28,7 @@
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- 以下にそれらを8種類分類用にして作ったコードを掲載します。一応二種類分類で実行できるコードは作っていてそれを参考にして作ったのですがなぜエラーが起きたかわかりません。こちらどういったエラーなのでしょうか。
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+ こちらはどういったエラーなのでしょうか。以下にそれらを8種類分類用にして作ったコードを掲載します。
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  よろしくお願いします。
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@@ -38,12 +38,358 @@
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  ```ここに言語を入力
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+ # -*- coding: utf-8 -*
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+
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+ import glob
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+
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+ import cv2
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+
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+ import numpy as np
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+
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+ import time
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+
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+ from tqdm import tqdm
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+
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+ from skimage import feature,exposure
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+
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+ from keras.models import model_from_json
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+
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+ from keras.utils.vis_utils import plot_model
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+
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+ from keras.models import Model
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+
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+ import pandas as pd
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+
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+ from matplotlib import pylab as plt
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+
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+ from sklearn.manifold import TSNE
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+
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+ from sklearn.preprocessing import StandardScaler
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+
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+ from sklearn.preprocessing import MinMaxScaler
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+
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+
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+
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+
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+
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+
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+
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+ # 特徴量抽出
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+
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+ def create_images_array(load_img_paths,model,layer_name):
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+
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+ imgs=[]
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+
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+ middle_layer_model = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)
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+
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+ for load_img_path in tqdm(load_img_paths):
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+
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+ img = cv2.imread(load_img_path)
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+
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+ #型を合わせる。255は正規化のため。
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+
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+ target = np.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2])).astype('float') / 255.0
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+
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+ middle_output = middle_layer_model.predict(target)
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+
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+ imgs.append(middle_output)
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+
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+ #imgsは384枚の特徴量配列(384,1,7,7,2048)をもつ。それを(384,100352)に変更する
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+
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+ imgs = np.reshape(imgs, [np.shape(imgs)[0], -1])
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+
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+ print('\nimgs.shape')
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+
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+ print(imgs.shape)
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+
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+ return imgs
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+
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+
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+
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+
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+
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+ def tSNE(X,seikika):
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+
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+ print('正規化start')
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+
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+ #データの正規化
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+
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+ #X_pre_emb = seikika.fit_transform(X)
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+
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+ #tSNEの適用
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+
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+ print('t-SNE適用start')
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+
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+ tsne = TSNE(n_components=2, random_state=0)
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+
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+ X_embedded = tsne.fit_transform(X)
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+
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+ #tSNEで次元削減したデータの正規化
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+
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+ X_2d= seikika.fit_transform(X_embedded)
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+
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+ print(X_2d.shape)
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+
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+ #print(X_2d)
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+
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+
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+
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+ return X_2d
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+ def main():
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+
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+ t1 = time.time()
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+
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+
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+
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+ json_string = open('./preprocess_images_keras_2class/case3/model.json').read()
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+
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+ model = model_from_json(json_string)
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+
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+
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+
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+ model.load_weights('./preprocess_images_keras_2class/case3/weight.hdf5')
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+
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+
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+
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+ layer_name = 'conv5_block3_out'
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+
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+
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+
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+
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+
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+ # 学習用の画像ファイルの格納先
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+
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+ LOAD_TRAIN_IMG1S_PATH = './preprocess_images_keras/case3/train/T1-FE/*'
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+
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+ LOAD_TRAIN_IMG2S_PATH = './preprocess_images_keras/case3/train/T2-FE/*'
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+
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+ LOAD_TRAIN_IMG3S_PATH = './preprocess_images_keras/case3/train/T3-FE/*'
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+
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+ LOAD_TRAIN_IMG4S_PATH = './preprocess_images_keras/case3/train/T4-FE/*'
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+
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+ LOAD_TRAIN_IMG5S_PATH = './preprocess_images_keras/case3/train/T5-FE/*'
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+
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+ LOAD_TRAIN_IMG6S_PATH = './preprocess_images_keras/case3/train/T6-FE/*'
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+
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+ LOAD_TRAIN_IMG7S_PATH = './preprocess_images_keras/case3/train/T7-FE/*'
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+
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+ LOAD_TRAIN_IMG8S_PATH = './preprocess_images_keras/case3/train/T8-FE/*'
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+
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+
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+
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+ LOAD_TRAIN_IMG9S_PATH = './preprocess_images_keras/case3/train/T1-W/*'
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+
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+ LOAD_TRAIN_IMG10S_PATH = './preprocess_images_keras/case3/train/T2-W/*'
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+
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+ LOAD_TRAIN_IMG11S_PATH = './preprocess_images_keras/case3/train/T3-W/*'
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+
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+ LOAD_TRAIN_IMG12S_PATH = './preprocess_images_keras/case3/train/T4-W/*'
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+
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+ LOAD_TRAIN_IMG13S_PATH = './preprocess_images_keras/case3/train/T5-W/*'
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+
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+ LOAD_TRAIN_IMG14S_PATH = './preprocess_images_keras/case3/train/T6-W/*'
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+
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+ LOAD_TRAIN_IMG15S_PATH = './preprocess_images_keras/case3/train/T7-W/*'
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+
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+ LOAD_TRAIN_IMG16S_PATH = './preprocess_images_keras/case3/train/T8-W/*'
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+
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+
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+
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+
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+
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+
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+
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+ # 学習用の画像ファイルのパスを取得
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+
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+ load_img1_paths = glob.glob(LOAD_TRAIN_IMG1S_PATH)
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+
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+ load_img2_paths = glob.glob(LOAD_TRAIN_IMG2S_PATH)
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+
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+ load_img3_paths = glob.glob(LOAD_TRAIN_IMG3S_PATH)
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+
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+ load_img4_paths = glob.glob(LOAD_TRAIN_IMG4S_PATH)
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+
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+ load_img5_paths = glob.glob(LOAD_TRAIN_IMG5S_PATH)
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+
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+ load_img6_paths = glob.glob(LOAD_TRAIN_IMG6S_PATH)
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+
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+ load_img7_paths = glob.glob(LOAD_TRAIN_IMG7S_PATH)
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+
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+ load_img8_paths = glob.glob(LOAD_TRAIN_IMG8S_PATH)
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+
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+
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+
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+ load_img9_paths = glob.glob(LOAD_TRAIN_IMG9S_PATH)
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+
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+ load_img10_paths = glob.glob(LOAD_TRAIN_IMG10S_PATH)
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+
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+ load_img11_paths = glob.glob(LOAD_TRAIN_IMG11S_PATH)
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+
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+ load_img12_paths = glob.glob(LOAD_TRAIN_IMG12S_PATH)
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+
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+ load_img13_paths = glob.glob(LOAD_TRAIN_IMG13S_PATH)
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+
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+ load_img14_paths = glob.glob(LOAD_TRAIN_IMG14S_PATH)
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+
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+ load_img15_paths = glob.glob(LOAD_TRAIN_IMG15S_PATH)
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+
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+ load_img16_paths = glob.glob(LOAD_TRAIN_IMG16S_PATH)
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+ # 学習用の画像ファイルをロードし特徴量抽出
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+
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+ imgs1 = create_images_array(load_img1_paths,model,layer_name)
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+
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+ imgs2 = create_images_array(load_img2_paths,model,layer_name)
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+
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+ imgs3 = create_images_array(load_img3_paths,model,layer_name)
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+
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+ imgs4 = create_images_array(load_img4_paths,model,layer_name)
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+
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+ imgs5 = create_images_array(load_img5_paths,model,layer_name)
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+
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+ imgs6 = create_images_array(load_img6_paths,model,layer_name)
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+
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+ imgs7 = create_images_array(load_img7_paths,model,layer_name)
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+
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+ imgs8 = create_images_array(load_img8_paths,model,layer_name)
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+
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+
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+
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+ imgs9 = create_images_array(load_img9_paths,model,layer_name)
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+
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+ imgs10 = create_images_array(load_img10_paths,model,layer_name)
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+
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+ imgs11 = create_images_array(load_img11_paths,model,layer_name)
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+
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+ imgs12 = create_images_array(load_img12_paths,model,layer_name)
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+
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+ imgs13 = create_images_array(load_img13_paths,model,layer_name)
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+
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+ imgs14 = create_images_array(load_img14_paths,model,layer_name)
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+
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+ imgs15 = create_images_array(load_img15_paths,model,layer_name)
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+
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+ imgs16 = create_images_array(load_img16_paths,model,layer_name)
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+
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+
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+
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+
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+
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+ X = np.r_[imgs1, imgs2, imgs3, imgs4, imgs5, imgs6, imgs7, imgs8, imgs9, imgs10, imgs11, imgs12, imgs13, imgs14, imgs15, imgs16]
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+
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+ print('X')
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+
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+ print(X)
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+
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+ # 正解ラベルを生成imgs.番号でラベルを決める
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+
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+
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+
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+ labels1 = np.full(len(load_img1_paths), 0, np.int32)
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+
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+ labels2 = np.full(len(load_img2_paths), 1, np.int32)
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+
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+ labels3 = np.full(len(load_img3_paths), 2, np.int32)
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+
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+ labels4 = np.full(len(load_img4_paths), 3, np.int32)
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+
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+ labels5 = np.full(len(load_img5_paths), 4, np.int32)
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+
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+ labels6 = np.full(len(load_img6_paths), 5, np.int32)
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+
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+ labels7 = np.full(len(load_img7_paths), 6, np.int32)
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+
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+ labels8 = np.full(len(load_img8_paths), 7, np.int32)
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+
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+
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+
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+ labels9 = np.full(len(load_img9_paths), 0, np.int32)
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+
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+ labels10 = np.full(len(load_img10_paths), 1, np.int32)
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+
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+ labels11 = np.full(len(load_img11_paths), 2, np.int32)
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+
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+ labels12 = np.full(len(load_img12_paths), 3, np.int32)
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+
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+ labels13 = np.full(len(load_img13_paths), 4, np.int32)
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+
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+ labels14 = np.full(len(load_img14_paths), 5, np.int32)
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+
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+ labels15 = np.full(len(load_img15_paths), 6, np.int32)
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+
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+ labels16 = np.full(len(load_img16_paths), 7, np.int32)
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+
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+
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+
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+
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+
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+ label = np.r_[labels1, labels2, labels3, labels4, labels5, labels6, labels7, labels8, labels9, labels10, labels11, labels12, labels13, labels14, labels15, labels16]
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+
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+ print('label')
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+
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+ print(label.shape)
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+
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+ #正規化
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+
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+ seikika = StandardScaler()
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+
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+ #seikika = MinMaxScaler()
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+
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+ X_embeddedsc = tSNE(X,seikika)
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+
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+ print(X_embeddedsc.shape)
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+
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+
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+
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+
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+
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+
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+
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+ # グラフを横長にする
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+
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+ from matplotlib.pylab import rcParams
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+
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+ rcParams['figure.figsize'] = 15, 6
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+
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+
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+
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+ import matplotlib as mpl
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+
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+ mpl.rcParams['font.family'] = ['serif']
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+
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+
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+
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+
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+
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+
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+
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- #8種類分類のプロット
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+ #8種類分類のプロット
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  print("-----------------------------------------------------------")
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  # 可視化の用意
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+ #listは1番目は0から読み取る。matlabとの大きな違い
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+
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  color_list1 = ["blue","green", "red","cyan","magenta","yellow","black","white"]
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  color_list2 = ["blue","green", "red","cyan","magenta","yellow","black","black"]
@@ -64,9 +410,9 @@
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  for index in range(len(np.unique(label))):
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- plt.scatter(X_embeddedsc[label==index,1], X_embeddedsc[label==index,2], X_embeddedsc[label==index,3],X_embeddedsc[label==index,4],
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+ plt.scatter(X_embeddedsc[label==index,0], X_embeddedsc[label==index,1], X_embeddedsc[label==index,2],X_embeddedsc[label==index,3],
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-
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+
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- X_embeddedsc[label==index,5],X_embeddedsc[label==index,6],X_embeddedsc[label==index,7],X_embeddedsc[label==index,8],
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+ X_embeddedsc[label==index,4],X_embeddedsc[label==index,5],X_embeddedsc[label==index,6],X_embeddedsc[label==index,7],
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  edgecolor=color_list2[index],color=color_list1[index], marker=marker_list[index], label=label_list[index])
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@@ -78,6 +424,34 @@
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79
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  plt.show()
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81
- print("-----------------------------------------------------------\n\n")
427
+ print("-----------------------------------------------------------\n\n")
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+
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+
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+
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+
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+
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+
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+
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+ t2 = time.time()
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+
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+ elapsed_time = (t2-t1)/3600
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+
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+ print(f"経過時間:{elapsed_time}")
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+
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+
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+
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+
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+
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+
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+
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+ if __name__ == '__main__':
448
+
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+ main()
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+
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+
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+
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+
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+
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+
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  ```