tensorflow 读取图片 Dataset用法

bmp Dataset.from_tensor_slices：

Dataset简单用法

png这个测试ok：

读图片，resize，预测

构建dateset png格式可以训练：

bmp Dataset.from_tensor_slices：

    augfiles = ['test_images/532_img_.bmp']gtfiles = ['test_images/532_img_.bmp']augImages = tf.constant(augfiles)gtImages = tf.constant(gtfiles)dataset = tf.data.Dataset.from_tensor_slices((augImages, gtImages))# dataset = dataset.shuffle(len(augImages))# dataset = dataset.repeat()dataset = dataset.map(parse_function).batch(1)

Dataset简单用法

一、Dataset使用
# from_tensor_slices：表示从张量中获取数据。
# make_one_shot_iterator():表示只将数据读取一次，然后就抛弃这个数据了。
input_data = [1,2,3,5,8]
dataset = tf.data.Dataset.from_tensor_slices(input_data)
for e in dataset:print(e)

png这个测试ok：

    img_byte = tf.compat.v1.read_file(filename='test_images/532_img_.png')img_data_jpg = tf.image.decode_png(img_byte)  # 图像解码img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.uint8)  # 改变图像数据的类型

读图片，resize，预测

filename_image_string = tf.io.read_file(imgfile)
filename_image = tf.image.decode_png(filename_image_string, channels=3)
filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)
filename_image = tf.image.resize(filename_image, (256, 256))
l, w, c = filename_image.shape
filename_image = tf.reshape(filename_image, [1, l, w, c])
output = model.predict(filename_image)
output = output.reshape((l, w, c)) * 255
cv2.imwrite(out_dir+ os.path.basename(imgfile), output)

构建dateset png格式可以训练：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, SpatialDropout2D, ReLU, Input, Concatenate, Add
from tensorflow.keras.losses import MeanAbsoluteError, MeanSquaredError
from tensorflow.keras.optimizers import Adam
import os
import pandas as pd
import cv2class UWCNN(tf.keras.Model):def __init__(self):super(UWCNN, self).__init__()self.conv1 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze1")self.relu1 = ReLU()self.conv2 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze2")self.relu2 = ReLU()self.conv3 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze3")self.relu3 = ReLU()self.concat1 = Concatenate(axis=3)self.conv4 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze4")self.relu4 = ReLU()self.conv5 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze5")self.relu5 = ReLU()self.conv6 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze6")self.relu6 = ReLU()self.concat2 = Concatenate(axis=3)self.conv7 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze7")self.relu7 = ReLU()self.conv8 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze8")self.relu8 = ReLU()self.conv9 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze9")self.relu9 = ReLU()self.concat3 = Concatenate(axis=3)self.conv10 = Conv2D(3, 3, (1, 1), 'same', name="conv2d_dehaze10")self.add1 = Add()def call(self, inputs):image_conv1 = self.relu1(self.conv1(inputs))image_conv2 = self.relu2(self.conv2(image_conv1))image_conv3 = self.relu3(self.conv3(image_conv2))dehaze_concat1 = self.concat1([image_conv1, image_conv2, image_conv3, inputs])image_conv4 = self.relu4(self.conv4(dehaze_concat1))image_conv5 = self.relu5(self.conv5(image_conv4))image_conv6 = self.relu6(self.conv6(image_conv5))dehaze_concat2 = self.concat2([dehaze_concat1, image_conv4, image_conv5, image_conv6])image_conv7 = self.relu7(self.conv7(dehaze_concat2))image_conv8 = self.relu8(self.conv8(image_conv7))image_conv9 = self.relu9(self.conv9(image_conv8))dehaze_concat3 = self.concat3([dehaze_concat2, image_conv7, image_conv8, image_conv9])image_conv10 = self.conv10(dehaze_concat3)out = self.add1([inputs, image_conv10])return outdef parse_function(filename, label):filename_image_string = tf.io.read_file(filename)label_image_string = tf.io.read_file(label)# Decode the filename_image_stringfilename_image = tf.image.decode_png(filename_image_string, channels=3)filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)# Decode the label_image_stringlabel_image = tf.image.decode_png(label_image_string, channels=3)label_image = tf.image.convert_image_dtype(label_image, tf.float32)return filename_image, label_imagedef combloss(y_actual, y_predicted):'''This is the custom loss function for keras model:param y_actual::param y_predicted::return:'''# this is just l2 + lssimlssim = tf.constant(1, dtype=tf.float32) - tf.reduce_mean(tf.image.ssim(y_actual, y_predicted, max_val=1, filter_size=13))  # remove max_val=1.0# lmse = MeanSquaredError(reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)(y_actual, y_predicted)lmse = MeanSquaredError(reduction='sum_over_batch_size')(y_actual, y_predicted)lmse = tf.math.multiply(lmse, 4)return tf.math.add(lmse, lssim)def train(ckptpath="./train_type1/cp.ckpt", type='type1'):# df = pd.read_csv(datafile)augfiles = ['test_images/532_img_.png']gtfiles = ['test_images/532_label_.png']augImages = tf.constant(augfiles)gtImages = tf.constant(gtfiles)dataset = tf.data.Dataset.from_tensor_slices((augImages, gtImages))# dataset = dataset.shuffle(len(augImages))# dataset = dataset.repeat()dataset = dataset.map(parse_function).batch(1)# Call backs# checkpoint_path = "./train_type1/cp.ckpt"checkpoint_path = ckptpathcheckpoint_dir = os.path.dirname(checkpoint_path)# Create a callback that saves the model's weightscp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)model = UWCNN()model.compile(optimizer=Adam(), loss=combloss)model.fit(dataset, epochs=1, callbacks=[cp_callback])# os.listdir(checkpoint_dir)# model.save('saved_model/my_model')model.save('save_model/' + type)# model.sample_weights('model_weight.h5')def model_test(imgdir="./test_images/", imgfile="12433.png", ckdir="./train_type1/cp.ckpt", outdir="./results/",type='type1'):model = UWCNN()# model.load_weights('model_weight.h5')# model = tf.keras.models.load_model('save_model/' + type, custom_objects={'loss': combloss}, compile=False)augfiles = ['test_images/532_img_.bmp']gtfiles = ['test_images/532_img_.bmp']augImages = tf.constant(augfiles)gtImages = tf.constant(gtfiles)dataset = tf.data.Dataset.from_tensor_slices((augImages, gtImages))dataset = dataset.map(parse_function).batch(1)cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=ckdir, save_weights_only=True, verbose=1)model.compile(optimizer=Adam(), loss=combloss)model.fit(dataset, epochs=1, callbacks=[cp_callback])model.summary()model.load_weights(ckdir)filename_image_string = tf.io.read_file(imgdir + imgfile)filename_image = tf.image.decode_png(filename_image_string, channels=3)filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)filename_image = tf.image.resize(filename_image, (460, 620))l, w, c = filename_image.shapefilename_image = tf.reshape(filename_image, [1, l, w, c])output = model.predict(filename_image)output = output.reshape((l, w, c)) * 255cv2.imwrite(outdir + type + "_" + imgfile, output)if __name__ == "__main__":train(ckptpath="./train_type1/cp.ckpt", type='type1')exit(0)type = "type1"ckdir = "./train_type1/cp.ckpt"model_test(imgdir="./test_images/", imgfile="532_img_.png", ckdir=ckdir, outdir="./results/", type=type)# model_test(imgdir="./test_images/", imgfile="602_img_.png", ckdir=ckdir, outdir="./results/", type=type)# model_test(imgdir="./test_images/", imgfile="617_img_.png", ckdir=ckdir, outdir="./results/", type=type)# model_test(imgdir="./test_images/", imgfile="12422.png", ckdir=ckdir, outdir="./results/", type=type)# model_test(imgdir="./test_images/", imgfile="12433.png", ckdir=ckdir, outdir="./results/", type=type)

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tensorflow 读取图片 Dataset用法

bmp Dataset.from_tensor_slices：

Dataset简单用法

png这个测试ok：

读图片，resize，预测

构建dateset png格式可以训练：

tensorflow 读取图片 Dataset用法相关推荐

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