first commit

benchmark/PaddleOCR_DBNet/utils/compute_mean_std.py

@@ -0,0 +1,47 @@
+# -*- 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) / 255.0
+
+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BGR，PIL/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))