python使用openvc库进行图像数据增强

以下是使用Python和OpenCV库实现图像数据增强的简单示例代码，其中包括常用的数据增强操作：

import cv2
import numpy as np
import os

# 水平翻转
def horizontal_flip(image):
    return cv2.flip(image, 1)

# 垂直翻转
def vertical_flip(image):
    return cv2.flip(image, 0)

# 随机旋转
def random_rotation(image, angle_range=(-10, 10)):
    angle = np.random.randint(angle_range[0], angle_range[1])
    height, width = image.shape[:2]
    rotation_matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
    rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))
    return rotated_image

# 随机裁剪
def random_crop(image, crop_size=(224, 224)):
    height, width = image.shape[:2]
    left = np.random.randint(0, width - crop_size[0])
    top = np.random.randint(0, height - crop_size[1])
    right = left + crop_size[0]
    bottom = top + crop_size[1]
    cropped_image = image[top:bottom, left:right]
    return cropped_image

# 添加随机噪声
def random_noise(image, noise_range=(20, 50)):
    noise = np.random.randint(noise_range[0], noise_range[1], size=image.shape, dtype=np.uint8)
    noisy_image = cv2.add(image, noise)
    return np.clip(noisy_image, 0, 255)

# 设置原始图像路径和增强后图像保存路径
original_path = "original_images"
augmented_path = "augmented_images"

# 确保存储路径存在
os.makedirs(augmented_path, exist_ok=True)

# 遍历原始图像路径下的所有图像
for filename in os.listdir(original_path):
    if filename.endswith(".jpg") or filename.endswith(".png"):
        image_path = os.path.join(original_path, filename)
        image = cv2.imread(image_path)

        # 水平翻转
        h_flip = horizontal_flip(image)
        cv2.imwrite(os.path.join(augmented_path, f"flip_h_{filename}"), h_flip)

        # 垂直翻转
        v_flip = vertical_flip(image)
        cv2.imwrite(os.path.join(augmented_path, f"flip_v_{filename}"), v_flip)

        # 随机旋转
        rotated = random_rotation(image)
        cv2.imwrite(os.path.join(augmented_path, f"rotated_{filename}"), rotated)

        # 随机裁剪
        cropped = random_crop(image)
        cv2.imwrite(os.path.join(augmented_path, f"crop_{filename}"), cropped)

        # 添加随机噪声
        noisy = random_noise(image)
        cv2.imwrite(os.path.join(augmented_path, f"noisy_{filename}"), noisy)
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在这个示例代码中，我们使用OpenCV库来加载和处理图像。我们定义了几个常用的数据增强操作函数，包括水平翻转、垂直翻转、随机旋转、随机裁剪和添加随机噪声。然后，我们遍历原始图像路径下的所有图像，对每张图像进行数据增强操作，并保存到增强后图像保存路径。

请注意，为了运行此代码，您需要安装OpenCV库。可以使用pip install opencv-python命令来安装。同时，确保将原始图像放在指定的原始图像路径下，并设置好增强后图像的保存路径。

# -*- coding: utf-8 -*-

import cv2
import numpy as np
import os.path
import copy

# 椒盐噪声


def SaltAndPepper(src, percetage):
    SP_NoiseImg = src.copy()
    SP_NoiseNum = int(percetage*src.shape[0]*src.shape[1])
    for i in range(SP_NoiseNum):
        randR = np.random.randint(0, src.shape[0]-1)
        randG = np.random.randint(0, src.shape[1]-1)
        randB = np.random.randint(0, 3)
        if np.random.randint(0, 1) == 0:
            SP_NoiseImg[randR, randG, randB] = 0
        else:
            SP_NoiseImg[randR, randG, randB] = 255
    return SP_NoiseImg

# 高斯噪声


def addGaussianNoise(image, percetage):
    G_Noiseimg = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    G_NoiseNum = int(percetage*image.shape[0]*image.shape[1])
    for i in range(G_NoiseNum):
        temp_x = np.random.randint(0, h)
        temp_y = np.random.randint(0, w)
        G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[
            0]
    return G_Noiseimg

# 昏暗


def darker(image, percetage=0.9):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    # get darker
    for xi in range(0, w):
        for xj in range(0, h):
            image_copy[xj, xi, 0] = int(image[xj, xi, 0]*percetage)
            image_copy[xj, xi, 1] = int(image[xj, xi, 1]*percetage)
            image_copy[xj, xi, 2] = int(image[xj, xi, 2]*percetage)
    return image_copy

# 亮度


def brighter(image, percetage=1.5):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    # get brighter
    for xi in range(0, w):
        for xj in range(0, h):
            image_copy[xj, xi, 0] = np.clip(
                int(image[xj, xi, 0]*percetage), a_max=255, a_min=0)
            image_copy[xj, xi, 1] = np.clip(
                int(image[xj, xi, 1]*percetage), a_max=255, a_min=0)
            image_copy[xj, xi, 2] = np.clip(
                int(image[xj, xi, 2]*percetage), a_max=255, a_min=0)
    return image_copy

# 旋转


def rotate(image, angle, center=None, scale=1.0):
    (h, w) = image.shape[:2]
    # If no rotation center is specified, the center of the image is set as the rotation center
    if center is None:
        center = (w / 2, h / 2)
    m = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, m, (w, h))
    return rotated

# 翻转


def flip(image):
    flipped_image = np.fliplr(image)
    return flipped_image


# 图片文件夹路径
file_dir = r'test/img/'
for img_name in os.listdir(file_dir):
    img_path = file_dir + img_name
    img = cv2.imread(img_path)
    # cv2.imshow("1",img)
    # cv2.waitKey(5000)
    # 旋转
    rotated_90 = rotate(img, 90)
    cv2.imwrite(file_dir + img_name[0:-4] + '_r90.jpg', rotated_90)
    rotated_180 = rotate(img, 180)
    cv2.imwrite(file_dir + img_name[0:-4] + '_r180.jpg', rotated_180)

for img_name in os.listdir(file_dir):
    img_path = file_dir + img_name
    img = cv2.imread(img_path)
    # 镜像
    flipped_img = flip(img)
    cv2.imwrite(file_dir + img_name[0:-4] + '_fli.jpg', flipped_img)

    # 增加噪声
    # img_salt = SaltAndPepper(img, 0.3)
    # cv2.imwrite(file_dir + img_name[0:7] + '_salt.jpg', img_salt)
    img_gauss = addGaussianNoise(img, 0.3)
    cv2.imwrite(file_dir + img_name[0:-4] + '_noise.jpg', img_gauss)

    # 变亮、变暗
    img_darker = darker(img)
    cv2.imwrite(file_dir + img_name[0:-4] + '_darker.jpg', img_darker)
    img_brighter = brighter(img)
    cv2.imwrite(file_dir + img_name[0:-4] + '_brighter.jpg', img_brighter)

    blur = cv2.GaussianBlur(img, (7, 7), 1.5)
    #      cv2.GaussianBlur(图像，卷积核，标准差）
    cv2.imwrite(file_dir + img_name[0:-4] + '_blur.jpg', blur)
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