zhoujun 68099c2d5b
add db for benchmark (#8959)
* 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
2023-02-08 15:52:30 +08:00

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