三維的讀取圖片（w, h, c）:
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import tensorflow as tf import globimport os def _parse_function(filename): # print(filename) image_string = tf.read_file(filename) image_decoded = tf.image.decode_image(image_string) # (375, 500, 3) image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200) return image_resized with tf.Session() as sess: print( sess.run( img ).shape ) |
讀取批量圖片的讀取圖片（b, w, h, c）:
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import tensorflow as tf import globimport os ''' Dataset 批量讀取圖片''' def _parse_function(filename): # print(filename) image_string = tf.read_file(filename) image_decoded = tf.image.decode_image(image_string) # (375, 500, 3) image_decoded = tf.expand_dims(image_decoded, axis=0) image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200) return image_resized img = _parse_function('../pascal/VOCdevkit/VOC2012/JPEGImages/2007_000068.jpg') # image_resized = tf.image.resize_image_with_crop_or_pad( tf.truncated_normal((1,220,300,3))*10, 200, 200) 這種四維 形式是可以的 with tf.Session() as sess: print( sess.run( img ).shape ) #直接初始化就可以 ，轉(zhuǎn)換成四維報(bào)錯誤，不知道為什么，若誰想明白，請留言 報(bào)錯誤 #InvalidArgumentError (see above for traceback): Input shape axis 0 must equal 4, got shape [5] |
Databae的操作：
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import tensorflow as tf import globimport os ''' Dataset 批量讀取圖片： 原因： 1. 先定義圖片名的list,存放在Dataset中 from_tensor_slices（） 2. 映射函數(shù)， 在函數(shù)中，對list中的圖片進(jìn)行讀取，和resize,細(xì)節(jié) tf.read_file(filename) 返回的是三維的，因?yàn)檫@個(gè)每次取出一張圖片，放進(jìn)隊(duì)列中的，不需要轉(zhuǎn)化為四維 然后對圖片進(jìn)行resize, 然后每個(gè)batch進(jìn)行訪問這個(gè)函數(shù) ，所以get_next() 返回的是 [batch, w, h, c ] 3. 進(jìn)行shuffle , batch repeat的設(shè)置 4. iterator = dataset.make_one_shot_iterator() 設(shè)置迭代器 5. iterator.get_next() 獲取每個(gè)batch的圖片''' def _parse_function(filename): # print(filename) image_string = tf.read_file(filename) image_decoded = tf.image.decode_image(image_string) #(375, 500, 3) ''' Tensor` with type `uint8` with shape `[height, width, num_channels]` for BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for GIF images. ''' # image_resized = tf.image.resize_images(label, [200, 200]) ''' images 三維，四維的都可以 images: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor of shape `[height, width, channels]`. size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. ''' image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200) # return tf.squeeze(mage_resized,axis=0) return image_resized filenames = glob.glob( os.path.join('../pascal/VOCdevkit/VOC2012/JPEGImages', "*." + 'jpg') ) dataset = tf.data.Dataset.from_tensor_slices((filenames)) dataset = dataset.map(_parse_function) dataset = dataset.shuffle(10).batch(2).repeat(10)iterator = dataset.make_one_shot_iterator() img = iterator.get_next() with tf.Session() as sess: # print( sess.run(img).shape ) #(4, 200, 200, 3) for _ in range (10): print( sess.run(img).shape ) |
以上這篇淺談tensorflow中Dataset圖片的批量讀取及維度的操作詳解就是小編分享給大家的全部內(nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/qq_30638831/article/details/83450136
