(python3.6.4+caffe+pycharm)
一、数据可视化
1、mnist数据可视化
1)、训练样本可视化
首先要打开pycharm
新建python文件1.py,代码输入:
# -*- coding: utf-8 -* import numpy as np import struct from PIL import Image import os data_file = '/caffe/data/mnist/train-images-idx3-ubyte' # 需要修改的路径,train-images-idx3-ubyte文件所在的位置 # It's 47040016B, but we should set to 47040000B data_file_size = 47040016 data_file_size = str(data_file_size - 16) + 'B' data_buf = open(data_file, 'rb').read() magic, numImages, numRows, numColumns = struct.unpack_from( '>IIII', data_buf, 0) datas = struct.unpack_from( '>' + data_file_size, data_buf, struct.calcsize('>IIII')) datas = np.array(datas).astype(np.uint8).reshape( numImages, 1, numRows, numColumns) label_file = '/caffe/data/mnist/train-labels-idx1-ubyte' # 需要修改的路径 ,train-images-idx3-ubyte文件所在位置,最好采用绝对路径 # It's 60008B, but we should set to 60000B label_file_size = 60008 label_file_size = str(label_file_size - 8) + 'B' label_buf = open(label_file, 'rb').read() magic, numLabels = struct.unpack_from('>II', label_buf, 0) labels = struct.unpack_from( '>' + label_file_size, label_buf, struct.calcsize('>II')) labels = np.array(labels).astype(np.int64) datas_root = '/home/jinhanjun/caffe/examples/mnist/mnist_train' # 需要修改的路径,你最终可视化后的图片保存在哪里 if not os.path.exists(datas_root): os.mkdir(datas_root) for i in range(10): file_name = datas_root + os.sep + str(i) if not os.path.exists(file_name): os.mkdir(file_name) for ii in range(numLabels): img = Image.fromarray(datas[ii, 0, 0:28, 0:28]) label = labels[ii] file_name = datas_root + os.sep + str(label) + os.sep + 'mnist_train_' + str(ii) + '.png' img.save(file_name)
运行上面程序,可得到训练用的50000个样本集图片。打开/home/jinhanjun/caffe/examples/mnist/mnist_train文件即可查看。
2)、测试样本可视化
在pycharm新建python程序2.py
import numpy as np import struct from PIL import Image import os data_file = '/caffe/data/mnist/t10k-images-idx3-ubyte' # 需要修改的路径,t10k-images-idx3-ubyte文件所在的位置 # It's 7840016B, but we should set to 7840000B data_file_size = 7840016 data_file_size = str(data_file_size - 16) + 'B' data_buf = open(data_file, 'rb').read() magic, numImages, numRows, numColumns = struct.unpack_from( '>IIII', data_buf, 0) datas = struct.unpack_from( '>' + data_file_size, data_buf, struct.calcsize('>IIII')) datas = np.array(datas).astype(np.uint8).reshape( numImages, 1, numRows, numColumns) label_file = '/caffe/data/mnist/t10k-labels-idx1-ubyte' # 需要修改的路径,标签t10k-labels-idx1-ubyte文件所在位置 # It's 10008B, but we should set to 10000B label_file_size = 10008 label_file_size = str(label_file_size - 8) + 'B' label_buf = open(label_file, 'rb').read() magic, numLabels = struct.unpack_from('>II', label_buf, 0) labels = struct.unpack_from( '>' + label_file_size, label_buf, struct.calcsize('>II')) labels = np.array(labels).astype(np.int64) datas_root = '/home/jinhanjun/caffe/examples/mnist/mnist_test' # 需要修改的路径(可视化后保存的位置) if not os.path.exists(datas_root): os.mkdir(datas_root) for i in range(10): file_name = datas_root + os.sep + str(i) if not os.path.exists(file_name): os.mkdir(file_name) for ii in range(numLabels): img = Image.fromarray(datas[ii, 0, 0:28, 0:28]) label = labels[ii] file_name = datas_root + os.sep + str(label) + os.sep + 'mnist_test_' + str(ii) + '.png' img.save(file_name)运行上面程序,在相应的文件/home/jinhanjun/caffe/examples/mnist/mnist_test中查看
2、cifar10数据可视化
首先下载python版cifar10数据。
先给个cifar数据下载链接:http://www.cs.toronto.edu/~kriz/cifar.html
链接上提到三个数据版本,分别是python,matlab,binary版本,分别适合python,matlab,C程序
下载cifar-10-python.tar.gz文件,下载下来复制到caffe/data/cifar10文件夹中,解压待用。
然后就是pycharm写代码来运行程序了。代码如下:
import pickle as p import numpy as np import matplotlib.pyplot as plt import matplotlib.image as plimg from PIL import Image def load_CIFAR_batch(filename): """ load single batch of cifar """ with open(filename, 'rb')as f: datadict = p.load(f,encoding='iso-8859-1') X = datadict['data'] Y = datadict['labels'] X = X.reshape(10000, 3, 32, 32) Y = np.array(Y) return X, Y def load_CIFAR_Labels(filename): with open(filename, 'rb') as f: lines = [x for x in f.readlines()] print(lines) if __name__ == "__main__": load_CIFAR_Labels("/home/jinhanjun/caffe/data/cifar-10-batches-py/batches.meta") #batches.meta路径,刚下载下来的cifar10数据文件夹中包含 imgX, imgY = load_CIFAR_batch("/home/jinhanjun/caffe/data/cifar-10-batches-py/data_batch_1") #data_batch_1路径,刚下载下来的cifar10数据文件中包含 print(imgX.shape) print("正在保存图片:") for i in range(imgX.shape[0]): imgs = imgX[i - 1] if i < 100:#只循环100张图片,这句注释掉可以便利出所有的图片,图片较多,可能要一定的时间 img0 = imgs[0] img1 = imgs[1] img2 = imgs[2] i0 = Image.fromarray(img0) i1 = Image.fromarray(img1) i2 = Image.fromarray(img2) img = Image.merge("RGB",(i0,i1,i2)) name = "img" + str(i) img.save("/home/jinhanjun/caffe/examples/images/cifar10/images/"+name,"png")#文件夹下是RGB融合后的图,保存的路径,需要特别注意的一点,此路径如果是要保存在你原本没有建立的文件夹下的情况下,需要自己手动建立,不像前面mnist程序会自己建立,而这个程序运行是不会自动建立的,如果你没有建立,程序会报错,显示路径问题。 for j in range(imgs.shape[0]): img = imgs[j - 1] name = "img" + str(i) + str(j) + ".png" print("正在保存图片" + name) plimg.imsave("/home/jinhanjun/caffe/examples/images/cifar10/image/" + name, img)#文件夹下是RGB分离的图像,保存的图像路径,同上面所说的,注意路径的建立。 print("保存完毕.")
我们可以在/home/jinhanjun/caffe/examples/images/cifar10/images/文件夹下和/home/jinhanjun/caffe/examples/images/cifar10/image/文件夹下查看保存的图片,后者图片数量是前者的三倍