From 9718eae4b1350fd5618c01f1e1065a6144f69747 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=B5=B5=E5=BD=A6=E6=B6=B5?= <3435533971@qq.com> Date: Fri, 19 Jul 2024 20:14:54 +0800 Subject: [PATCH] =?UTF-8?q?=E8=A7=A3=E5=86=B3issue6=EF=BC=9A=E7=94=A8Tenso?= =?UTF-8?q?rFlow=202.0=E4=B8=AD=E9=80=82=E7=94=A8=E7=9A=84=E6=96=B9?= =?UTF-8?q?=E6=B3=95=E6=94=B9=E5=86=99input=5Fdata.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../input_data.py" | 203 +++++++++--------- 1 file changed, 99 insertions(+), 104 deletions(-) diff --git "a/\347\214\253\347\213\227\350\257\206\345\210\253/input_data.py" "b/\347\214\253\347\213\227\350\257\206\345\210\253/input_data.py" index 1a2f429..35837bf 100644 --- "a/\347\214\253\347\213\227\350\257\206\345\210\253/input_data.py" +++ "b/\347\214\253\347\213\227\350\257\206\345\210\253/input_data.py" @@ -1,111 +1,106 @@ import tensorflow as tf -import os +import os import numpy as np -def get_files(file_dir): - cats = [] - label_cats = [] - dogs = [] - label_dogs = [] - for file in os.listdir(file_dir): - name = file.split(sep='.') - if 'cat' in name[0]: - cats.append(file_dir + file) - label_cats.append(0) - else: - if 'dog' in name[0]: - dogs.append(file_dir + file) - label_dogs.append(1) - image_list = np.hstack((cats,dogs)) - label_list = np.hstack((label_cats,label_dogs)) - # print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs))) - # 多个种类分别的时候需要把多个种类放在一起，打乱顺序,这里不需要 - - # 把标签和图片都放倒一个 temp 中 然后打乱顺序，然后取出来 - temp = np.array([image_list,label_list]) - temp = temp.transpose() - # 打乱顺序 - np.random.shuffle(temp) - - # 取出第一个元素作为 image 第二个元素作为 label - image_list = list(temp[:,0]) - label_list = list(temp[:,1]) - label_list = [int(i) for i in label_list] - return image_list,label_list + +def get_files(file_dir_cat, file_dir_dog): + # cats和label_cats用于存储猫的图像路径和对应的标签（0 表示猫），dogs和label_dogs用于存储狗的图像路径和对应的标签（1 表示狗） + cats = [] + label_cats = [] + dogs = [] + label_dogs = [] + valid_extensions = ['.jpg', '.jpeg', '.png'] + + for file in os.listdir(file_dir_cat): + if any(file.endswith(ext) for ext in valid_extensions): + cats.append(os.path.join(file_dir_cat, file)) + label_cats.append(0) + + for file in os.listdir(file_dir_dog): + if any(file.endswith(ext) for ext in valid_extensions): + dogs.append(os.path.join(file_dir_dog, file)) + label_dogs.append(1) + + # 将cats和dogs列表合并成一个 image_list，将label_cats和label_dogs列表合并成一个 label_list + image_list = np.hstack((cats, dogs)) + label_list = np.hstack((label_cats, label_dogs)) + print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs))) + + # 将image_list和label_list组合成一个二维数组 temp，然后对其进行转置，使得每行包含一个图像路径和一个对应的标签 + temp = np.array([image_list, label_list]) + temp = temp.transpose() + # 打乱数组顺序 + np.random.shuffle(temp) + + # 从打乱后的数组 temp 中提取图像路径和标签，分别存储在 image_list 和 label_list 中 + image_list = list(temp[:, 0]) + label_list = list(temp[:, 1]) + # 将label_list中的元素转换为整数类型 + label_list = [int(i) for i in label_list] + + return image_list, label_list + # 测试 get_files -# imgs , label = get_files('/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/testImg/') -# for i in imgs: -# print("img:",i) - -# for i in label: -# print('label:',i) -# 测试 get_files end - - -# image_W ,image_H 指定图片大小，batch_size 每批读取的个数 ，capacity队列中 最多容纳元素的个数 -def get_batch(image,label,image_W,image_H,batch_size,capacity): - # 转换数据为 ts 能识别的格式 - image = tf.cast(image,tf.string) - label = tf.cast(label, tf.int32) - - # 将image 和 label 放倒队列里 - input_queue = tf.train.slice_input_producer([image,label]) - label = input_queue[1] - # 读取图片的全部信息 - image_contents = tf.read_file(input_queue[0]) - # 把图片解码，channels ＝3 为彩色图片, r，g ，b 黑白图片为 1 ，也可以理解为图片的厚度 - image = tf.image.decode_jpeg(image_contents,channels =3) - # 将图片以图片中心进行裁剪或者扩充为 指定的image_W，image_H - image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H) - # 对数据进行标准化,标准化，就是减去它的均值，除以他的方差 - image = tf.image.per_image_standardization(image) - - # 生成批次 num_threads 有多少个线程根据电脑配置设置 capacity 队列中 最多容纳图片的个数 tf.train.shuffle_batch 打乱顺序， - image_batch, label_batch = tf.train.batch([image, label],batch_size = batch_size, num_threads = 64, capacity = capacity) - - # 重新定义下 label_batch 的形状 - label_batch = tf.reshape(label_batch , [batch_size]) - # 转化图片 - image_batch = tf.cast(image_batch,tf.float32) - return image_batch, label_batch - - -# test get_batch -# import matplotlib.pyplot as plt -# BATCH_SIZE = 2 -# CAPACITY = 256 -# IMG_W = 208 -# IMG_H = 208 - -# train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/testImg/' - -# image_list, label_list = get_files(train_dir) -# image_batch, label_batch = get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY) - -# with tf.Session() as sess: -# i = 0 -# # Coordinator 和 start_queue_runners 监控 queue 的状态，不停的入队出队 -# coord = tf.train.Coordinator() -# threads = tf.train.start_queue_runners(coord=coord) -# # coord.should_stop() 返回 true 时也就是 数据读完了应该调用 coord.request_stop() -# try: -# while not coord.should_stop() and i<1: -# # 测试一个步 -# img, label = sess.run([image_batch, label_batch]) - -# for j in np.arange(BATCH_SIZE): -# print('label: %d' %label[j]) -# # 因为是个4D 的数据所以第一个为 索引 其他的为冒号就行了 -# plt.imshow(img[j,:,:,:]) -# plt.show() -# i+=1 -# # 队列中没有数据 -# except tf.errors.OutOfRangeError: -# print('done!') -# finally: -# coord.request_stop() -# coord.join(threads) - # sess.close() +file_dir_cat = "C:/Cat-or-Dog/test/cat" +file_dir_dog = "C:/Cat-or-Dog/test/dog" +imgs, label = get_files(file_dir_cat, file_dir_dog) +for i in imgs: + print("img:", i) +for i in label: + print('label:', i) + +# 读取图像数据，进行预处理（包括解码、调整大小和标准化），并生成图像和标签的批次 +# image_W ,image_H 指定图片大小，batch_size 每批读取的个数 ，capacity队列中最多容纳元素的个数 +def get_batch(image_paths, labels, image_W, image_H, batch_size, capacity): + dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels)) + + def load_and_preprocess_image(image_path, label): + image_contents = tf.io.read_file(image_path) + try: + image = tf.image.decode_image(image_contents, channels=3, expand_animations=False) + + if image.shape[-1] != 3: + tf.print(f"Skipping image {image_path}, as it has {image.shape[-1]} channels.") + return tf.constant(0.0, shape=(image_W, image_H, 3)), tf.constant(-1, dtype=tf.int32) + + image = tf.image.resize_with_crop_or_pad(image, image_W, image_H) + image = tf.image.per_image_standardization(image) + return image, label + except tf.errors.InvalidArgumentError as e: + tf.print(f"Skipping image {image_path}, due to error: {e}") + return tf.constant(0.0, shape=(image_W, image_H, 3)), tf.constant(-1, dtype=tf.int32) + except Exception as e: + tf.print(f"Skipping image {image_path}, due to an unexpected error: {e}") + return tf.constant(0.0, shape=(image_W, image_H, 3)), tf.constant(-1, dtype=tf.int32) + + def filter_invalid_images(image, label): + return tf.not_equal(label, -1) + + dataset = dataset.map(load_and_preprocess_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) + dataset = dataset.filter(filter_invalid_images) + dataset = dataset.shuffle(buffer_size=capacity).batch(batch_size).prefetch(buffer_size=tf.data.experimental.AUTOTUNE) + return dataset + +# # test get_batch +# import matplotlib.pyplot as plt +# BATCH_SIZE = 2 +# CAPACITY = 256 +# IMG_W = 350 +# IMG_H = 350 +# +# dataset = get_batch(imgs, label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY) +# +# # 创建一个迭代器来遍历数据集 +# iterator = iter(dataset) +# +# # 获取一个批次的图像和标签 +# images_batch, labels_batch = next(iterator) +# +# # 遍历一个批次的图像和标签，并显示每个图像及其对应的标签 +# for j in range(BATCH_SIZE): +# print('label: %d' % labels_batch[j].numpy()) +# plt.imshow(images_batch[j].numpy()) +# plt.show() \ No newline at end of file