经过长久的准备工作，今天终于要尝试训练程序的编写
今天先编写训练图片预处理和数据增强（即随机翻转、裁剪、改变色调、各种滤镜）
代码如下：
import os import random from multiprocessing import cpu_count import numpy as np import paddle from PIL import Image # 训练图片的预处理 def train_mapper(sample): img_path, label, crop_size, resize_size = sample # noinspection PyBroadException try: img = Image.open(img_path) # 统一图片大小 img = img.resize((resize_size, resize_size), Image.ANTIALIAS) # 随机水平翻转 r1 = random.random() if r1 > 0.5: img = img.transpose(Image.FLIP_LEFT_RIGHT) # 随机垂直翻转 r2 = random.random() if r2 > 0.5: img =img.transpose(Image.FLIP_TOP_BOTTOM) # 随机角度翻转 r3 = random.randint(-3, 3) img = img.rotate(r3, expand=False) # 随机裁剪 r4 = random.randint(0, int(resize_size - crop_size)) r5 = random.randint(0, int(resize_size - crop_size)) box = (r4, r5, r4 + crop_size, r5 + crop_size) img = img.crop(box) # 把图片转换成numpy值 img = np.array(img).astype(np.float32) # 转换成CHW img = img.transpose((2, 0, 1)) # 转换成BGR img = img[(2, 1, 0), :, :] / 255.0 return img, int(label) except Exception as e: print("%s 该图片错误 %s" % (img_path, e)) # 获取训练的reader def train_reader(train_list_path, crop_size, resize_size): father_path = os.path.dirname(train_list_path) def reader(): with open(train_list_path, 'r') as f: lines = f.readlines() # 打乱图像列表 np.random.shuffle(lines) # 开始获取每张图像和标签 for line in lines: img, label = line.split(' ') img = os.path.join(father_path, img) yield img, label, crop_size, resize_size return paddle.reader.xmap_readers(train_mapper, reader, cpu_count(), 102400) # 测试图片的预处理 def test_mapper(sample): img, label, crop_size = sample img = Image.open(img) # 统一图像大小 img = img.resize((crop_size, crop_size), Image.ANTIALIAS) # 转换成numpy值 img = np.array(img).astype(np.float32) # 转换成CHW img = img.transpose((2, 0, 1)) # 转换成BGR img = img[(2, 1, 0), :, :] / 255.0 return img, int(label) # 测试的图片reader def test_reader(test_list_path, crop_size): father_path = os.path.dirname(test_list_path) def reader(): with open(test_list_path, 'r') as f: lines = f.readlines() for line in lines: img, label = line.split(' ') img = os.path.join(father_path, img) yield img, label, crop_size return paddle.reader.xmap_readers(test_mapper, reader, cpu_count(), 1024)