paddle dataset

paddle实现图像旋转 import numpy as np from PIL import Image from matplotlib import pyplot as plt from paddle.vision.transforms import functional as F import cv2 image=cv2.imread('./1.jpg') image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB) # 图像旋转 opencv # imgR90 = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) # # imgR90 = np.rot90(image, 1) # numpy 矩阵旋转 90*1=90 度 # cv2.imshow('image',imgR90) # cv2.waitKey() # # 图像旋转 使用PIL # img=Image.fromarray(image) # img=img.rotate(180) # img.show() # img=np.array(img) # plt.imshow(img) # plt.show() # cv2.imshow('img',img) # opencv显示的格式是bgr 其他的都是rgb # cv2.waitKey() # 图像旋转 使用paddle image=Image.fromarray(image) rotate_img=F.rotate(image,90) # rotate_img.show() image=np.array(rotate_img) plt.imshow(image) plt.show() paddle实现数据增强 import cv2 import numpy as np from matplotlib import pyplot as plt from paddle.vision import Compose,RandomHorizontalFlip,RandomCrop,Normalize,RandomRotation,RandomVerticalFlip,ColorJitter transfrom=Compose([ # Normalize(mean=[125],std=[125],data_format="HWC"), RandomHorizontalFlip(1), RandomRotation(180), RandomCrop(200), RandomVerticalFlip(0.5), ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0) #图像的亮度、对比度、饱和度和色调 ]) while(True): image = cv2.imread('1.jpg') # image=np.expand_dims(image,axis=2) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = transfrom(image) # image=np.clip(image,0,1) plt.imshow(image) plt.show() dataset import os import cv2 import numpy as np from paddle.io import Dataset from paddle.vision.transforms import Normalize,Compose,RandomHorizontalFlip,ColorJitter,CenterCrop,RandomVerticalFlip,RandomRotation from matplotlib import pyplot as plt class MyDataset(Dataset): def __init__(self,data_dir,data_txt,transform=None): super().__init__() self.data_list=[] self.data_dir=data_dir with open(data_txt,encoding='utf-8') as f: for line in f.readlines(): image_path=line.strip() self.data_list.append(image_path) self.transform=transform def __getitem__(self, index): index=index%len(self.data_list) image_path=self.data_list[index] image_path=os.path.join(self.data_dir,image_path) image=cv2.imread(image_path) image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB) # 飞桨训练格式默认为float32 image=image.astype('float32') if self.transform is not None: # print('h') image=self.transform(image) return image def __len__(self): return len(self.data_list) # 定义数据预处理方法 transform=Compose( [ RandomHorizontalFlip(1), RandomVerticalFlip(1), RandomRotation(360) # Normalize(mean=[125],std=[125],data_format='CHW'), # ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5), # CenterCrop(100) ] ) dataset=MyDataset(data_dir='./fruit-detection/JPEGImages',data_txt='./fruit-detection/ImageSets/Main/train.txt',transform=transform) image=dataset[1] # image=np.clip(image,0,1) print(type(image)) plt.imshow(image.astype(np.uint8)) plt.show()