2022年度第7回卒研セミナー(2022/06/02)
関連サイトと資料
学習用データの画像サイズを必要なサイズに縮小する
画像の縮小
import cv2
import glob
import os
# データがあるディレクトリ
DATA_DIR = 'forest-path-movie-dataset-main/forest-path-movie-dataset-main/'
INPUT_DIR = DATA_DIR + 'scenes/scene-001'
OUTPUT_DIR = DATA_DIR + 'scenes2/scene-001'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
def createSavePath(input_path, output_dir):
fn = os.path.basename(input_path)
fn2 = os.path.splitext(fn)[0] + '.png'
return output_dir + '/' + fn2
input_path = INPUT_DIR + '/000.jpg'
output_path = createSavePath(input_path, OUTPUT_DIR)
print(f'input_path = {input_path}')
print(f'output_path = {output_path}')
img = cv2.imread(input_path, cv2.IMREAD_COLOR)
print(img.shape)
img2 = cv2.resize(img, dsize=(224, 224))
print(img2.shape)
cv2.imwrite(output_path, img2)
scenes/scene-001/000.jpg
scenes2/scene-001/000.png
os.remove(output_path)
for input_path in glob.glob(INPUT_DIR + '/*.jpg'):
print(input_path)
for input_path in glob.glob(INPUT_DIR + '/*.jpg'):
output_path = createSavePath(input_path, OUTPUT_DIR)
img = cv2.imread(input_path, cv2.IMREAD_COLOR)
img2 = cv2.resize(img, dsize=(224, 224))
cv2.imwrite(output_path, img2)
for i in range(2, 100+1):
INPUT_DIR = DATA_DIR + f'scenes/scene-{i:03}'
OUTPUT_DIR = DATA_DIR + f'scenes2/scene-{i:03}'
print(INPUT_DIR)
print(OUTPUT_DIR)
for i in range(2, 100+1):
print(i)
INPUT_DIR = DATA_DIR + f'scenes/scene-{i:03}'
OUTPUT_DIR = DATA_DIR + f'scenes2/scene-{i:03}'
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
for input_path in glob.glob(INPUT_DIR + '/*.jpg'):
output_path = createSavePath(input_path, OUTPUT_DIR)
img = cv2.imread(input_path, cv2.IMREAD_COLOR)
img2 = cv2.resize(img, dsize=(224, 224))
cv2.imwrite(output_path, img2)
csvファイルの書き換え
import pandas as pd
df = pd.read_csv('sample.csv')
print(df['file'])
v = df['file'][0]
print(v)
v = v.replace('scenes', 'scenes2')
v = v.replace('.jpg', '.png')
print(v)
for i in range(len(df['file'])):
v = df['file'][i]
v = v.replace('scenes', 'scenes2')
v = v.replace('.jpg', '.png')
df['file'][i] = v
print(df['file'])
df.to_csv('sample2.csv', index=False)
INPUT_DIR = 'forest-path-movie-dataset-main/forest-path-movie-dataset-main/'
df = pd.read_csv(INPUT_DIR+'all_file.csv')
for i in range(len(df['file'])):
v = df['file'][i]
v = v.replace('scenes', 'scenes2')
v = v.replace('.jpg', '.png')
df['file'][i] = v
df.to_csv(INPUT_DIR+'all_file2.csv', index=False)