準備一張手寫數字圖片

注意在depoy.prototxt檔案中指定正確的該圖片的通道數。

準備一個均值檔案

因為classify.py中的測試介面caffe.Classifier需要訓練圖片的均值檔案作為輸入引數，而實際lenet-5訓練時並未計算均值檔案，所以這裡建立一個全0的均值檔案輸入。編寫一個zeronp.py檔案如下

執行

python zeronp.py

修改classify.py儲存為classifymnist.py檔案

#!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.

By default it configures and runs the Caffe reference ImageNet model.
"""
 

import numpy as np
import os
import sys
import argparse
import glob
import time
import pandas as pd #插入資料分析包

import caffe

def main(argv):
    pycaffe_dir = os.path.dirname(__file__)

    parser = argparse.ArgumentParser()
    # Required arguments: input and output files.
    parser.add_argument(
        "input_file"
 
,
        help="Input image, directory, or npy."
    )
    parser.add_argument(
        "output_file",
        help="Output npy filename."
    )
    # Optional arguments.
    parser.add_argument(
        "--model_def",
        default=os.path.join(pycaffe_dir,
                "../examples/mnist/deploy.prototxt"
 
), #指定lenet-5的deploy.prototxt模型位置
        help="Model definition file."
    )
    parser.add_argument(
        "--pretrained_model",
        default=os.path.join(pycaffe_dir,
                "../examples/mnist/lenet_iter_10000.caffemodel"), #指定lenet-5的caffemodel模型位置
        help="Trained model weights file."
    )
#######新增^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    parser.add_argument(
        "--labels_file",
        default=os.path.join(pycaffe_dir,
                "../examples/mnist/synset_words.txt"), #指定輸出結果對應的類別名檔案
        help="mnist result words file"
    )
    parser.add_argument(
        "--force_grayscale",
        action='store_true',   #增加一個變數將輸入影象強制轉化為灰度圖，因為lenet-5訓練用的就是灰度圖
        help="Converts RGB images down to single-channel grayscale versions," +
                   "useful for single-channel networks like MNIST."
    )
    parser.add_argument(
        "--print_results",
        action='store_true', #輸入引數要求列印輸出結果
        help="Write output text to stdout rather than serializing to a file."
    )
#######新增vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
    parser.add_argument(
        "--gpu",
        action='store_true',
        help="Switch for gpu computation."
    )
    parser.add_argument(
        "--center_only",
        action='store_true',
        help="Switch for prediction from center crop alone instead of " +
             "averaging predictions across crops (default)."
    )
    parser.add_argument(
        "--images_dim",
        default='28,28',     #指定影象高與寬
        help="Canonical 'height,width' dimensions of input images."
    )
    parser.add_argument(
        "--mean_file",
        default=os.path.join(pycaffe_dir,
                             '../examples/mnist/meanfile.npy'), #指定均值檔案
        help="Data set image mean of [Channels x Height x Width] dimensions " +
             "(numpy array). Set to '' for no mean subtraction."
    )
    parser.add_argument(
        "--input_scale",
        type=float,
        help="Multiply input features by this scale to finish preprocessing."
    )
    parser.add_argument(
        "--raw_scale",
        type=float,
        default=255.0,
        help="Multiply raw input by this scale before preprocessing."
    )
    parser.add_argument(
        "--channel_swap",
        default='2,1,0',
        help="Order to permute input channels. The default converts " +
             "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
    )
    parser.add_argument(
        "--ext",
        default='jpg',
        help="Image file extension to take as input when a directory " +
             "is given as the input file."
    )
    args = parser.parse_args()

    image_dims = [int(s) for s in args.images_dim.split(',')]

    mean, channel_swap = None, None
    if not args.force_grayscale:
        if args.mean_file:
            mean = np.load(args.mean_file).mean(1).mean(1)
        if args.channel_swap:
            channel_swap = [int(s) for s in args.channel_swap.split(',')]

    if args.gpu:
        caffe.set_mode_gpu()
        print("GPU mode")
    else:
        caffe.set_mode_cpu()
        print("CPU mode")

    # Make classifier.
    classifier = caffe.Classifier(args.model_def, args.pretrained_model,
            image_dims=image_dims, mean=mean,
            input_scale=args.input_scale, raw_scale=args.raw_scale,
            channel_swap=channel_swap)

    # Load numpy array (.npy), directory glob (*.jpg), or image file.
    args.input_file = os.path.expanduser(args.input_file)
    if args.input_file.endswith('npy'):
        print("Loading file: %s" % args.input_file)
        inputs = np.load(args.input_file)
    elif os.path.isdir(args.input_file):
        print("Loading folder: %s" % args.input_file)
        inputs =[caffe.io.load_image(im_f)
                 for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
    else:
        print("Loading image file: %s" % args.input_file)
        inputs = [caffe.io.load_image(args.input_file, not args.force_grayscale)] #強制圖片為灰度圖

    print("Classifying %d inputs." % len(inputs))

    # Classify.
    start = time.time()
    scores = classifier.predict(inputs, not args.center_only).flatten()
    print("Done in %.2f s." % (time.time() - start))

   #增加輸出結果列印到終端^^^^^^^^
    # print
    if args.print_results:
        with open(args.labels_file) as f:
            labels_df = pd.DataFrame([{'synset_id':l.strip().split(' ')[0], 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]} for l in f.readlines()])
            labels = labels_df.sort('synset_id')['name'].values

            indices =(-scores).argsort()[:5]
            predictions = labels[indices]
            print predictions
            print scores

            meta = [(p, '%.5f' % scores[i]) for i,p in zip(indices, predictions)]
            print meta
#增加輸出結果列印到終端vvvvvvvvvvv


    # Save
    print("Saving results into %s" % args.output_file)
    np.save(args.output_file, predictions)


if __name__ == '__main__':
    main(sys.argv)

修改完成後執行如下命令：

 python classifymnist.py --print_results --force_grayscale --center_only --labels_file ../examples/mnist/synset_words.txt ../examples/mnist/4.jpg resultsfile

得到結果如下：：