【python代码】对图片进行数据增强（直方图均衡和加噪声）,私房ipad视频转换器

文件名：【python代码】对图片进行数据增强（直方图均衡和加噪声）,私房ipad视频转换器 【python代码】对图片进行数据增强（直方图均衡和加噪声）

文章目录 1. 代码

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1. 代码 import osimport cv2import albumentations as Afrom tqdm import tqdmfrom glob import globimport numpy as np# 方法1：加入噪声trans2 = A.Compose([A.RandomBrightnessContrast(p=0.5),A.HueSaturationValue(p=0.5),A.OneOf([ A.AdvancedBlur(p=0.5),A.Blur(p=0.5),A.Defocus(p=0.5),A.GaussianBlur(p=0.5),A.GlassBlur(p=0.5),A.MedianBlur(p=0.5),A.MotionBlur(p=0.5),],p=1.0),A.GaussNoise(p=0.5),])# 方法2：直方图均衡化：对比度受限自适应直方图均衡trans3 = A.Compose([A.CLAHE(p=1), # 对比度受限自适应直方图均衡A.RandomGamma(p=0.5),])def create_dir(path):if not os.path.exists(path):os.makedirs(path)def load_data_aug(path):# todo:train imgstrain_x = sorted(glob(os.path.join(path, "train/images", "*.png")))train_y = sorted(glob(os.path.join(path, "train/masks/0", "*.png")))# todo:val imgsval_x = sorted(glob(os.path.join(path, "val/images", "*.png")))val_y = sorted(glob(os.path.join(path, "val/masks/0", "*.png")))# test_x = sorted(glob(os.path.join(path, "test/images", "*.png")))# test_y = sorted(glob(os.path.join(path, "test/masks/0", "*.png")))return (train_x, train_y), (val_x, val_y)def augment_data(images, masks, save_path, augment=False):for idx, (x, y) in tqdm(enumerate(zip(images, masks)), total=len(images)):name = x.split("/")[-1].split(".")[0]img = cv2.imread(x)# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# img = img[:, 520:3368, :] # 对图片进行裁剪# mask = cv2.imread(y, 0) # todo:将图像读取为单通道的灰度图像# mask = mask[:, 520:3368] # 对图片进行裁剪mask = cv2.imread(y)# for i, m in zip(img, mask):# print(i.shape)# print(m.shape)transformed = trans3(image=img, mask=mask) # todo:select methodsimg= transformed['image']mask = transformed['mask']# img = trans3(image=img)['image']# i1 = cv2.resize(img, (512, 512))# m1 = cv2.resize(mask, (512, 512))tmp_image_name = f"{name}_trans3.png" # todo:renametmp_mask_name = f"{name}_trans3.png" # todo:renameimage_path = os.path.join(save_path, "images", tmp_image_name)mask_path = os.path.join(save_path, "masks", "0", tmp_mask_name)cv2.imwrite(image_path, img, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])cv2.imwrite(mask_path, mask, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])if __name__ == '__main__':root_path = 'path/to/数据集根目录'out_path ='path/to/保存路径（数据集根目录）'(train_x, train_y), (val_x, val_y) = load_data_aug(root_path)# print(train_x)# print(train_y)# print(test_x)# print(test_y)# create_dir(out_path + "/train/images/")# create_dir(out_path + "/train/masks/0")# create_dir(out_path + "/val/images/")# create_dir(out_path + "/val/masks/0")# create_dir(out_path + "/test/images/")# create_dir(out_path + "/test/masks/0")augment_data(train_x, train_y, out_path + "/train/", augment=False)augment_data(val_x, val_y, out_path + "/val/", augment=False)# augment_data(test_x, test_y, out_path + "/test/", augment=False)