数据增强（随机旋转+噪点）

# !/usr/bin python3                                 
# encoding    : utf-8 -*-                                                          
# @software   : PyCharm      
# @file       :   Augment.py
import cv2
import os
import numpy as np
import random
from tqdm import tqdm
list = os.listdir('Order')
for i in range(len(list)):
    list[i] = "Order/" + list[i]


def rotate_bound(image, angle):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)

    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (i.e., the rotation components of the matrix)
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])

    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))

    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY

    # perform the actual rotation and return the image
    return cv2.warpAffine(image, M, (nW, nH))

def add_noise(img, num):
    imgInfo = img.shape
    height = imgInfo[0] - 1  # 防止越界
    width = imgInfo[1] - 1

    temp = num  # 噪声点的个数
    for i in range(0, temp):
        if random.randint(1, temp) % 2 == 0:
            img[random.randint(0, height), random.randint(0, width)] = (255, 255, 255)
        if random.randint(1, temp) % 2 != 0:
            img[random.randint(0, height), random.randint(0, width)] = (0, 0, 0)
    return img


for i in tqdm(range(len(list))):
    if i % 2 == 0:
        img = cv2.imread(list[i])
        img_dst = rotate_bound(img, random.randint(1, 9)*20)
        cv2.imwrite('Order/{}_rotate.jpg'.format(i), img_dst)

标签：噪点,img,randint,image,random,list,旋转,随机,import
来源： https://blog.csdn.net/weilaicxy22/article/details/121258391