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68099c2d5b
* Add custom detection and recognition model usage instructions in re * update * Add custom detection and recognition model usage instructions in re * add db net for benchmark * rename benckmark to PaddleOCR_benchmark * add addict to req * rename
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2019/12/7 14:46
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# @Author : zhoujun
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import numpy as np
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import cv2
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import os
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import random
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from tqdm import tqdm
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# calculate means and std
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train_txt_path = './train_val_list.txt'
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CNum = 10000 # 挑选多少图片进行计算
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img_h, img_w = 640, 640
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imgs = np.zeros([img_w, img_h, 3, 1])
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means, stdevs = [], []
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with open(train_txt_path, 'r') as f:
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lines = f.readlines()
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random.shuffle(lines) # shuffle , 随机挑选图片
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for i in tqdm(range(CNum)):
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img_path = lines[i].split('\t')[0]
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img = cv2.imread(img_path)
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img = cv2.resize(img, (img_h, img_w))
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img = img[:, :, :, np.newaxis]
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imgs = np.concatenate((imgs, img), axis=3)
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# print(i)
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imgs = imgs.astype(np.float32) / 255.
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for i in tqdm(range(3)):
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pixels = imgs[:, :, i, :].ravel() # 拉成一行
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means.append(np.mean(pixels))
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stdevs.append(np.std(pixels))
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# cv2 读取的图像格式为BGR，PIL/Skimage读取到的都是RGB不用转
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means.reverse() # BGR --> RGB
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stdevs.reverse()
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print("normMean = {}".format(means))
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print("normStd = {}".format(stdevs))
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print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs)) |