shlex.split(mc), stdout=open('null', 'w'), cwd=script_dirname)

              我们来看程序了：

# Execute command in MATLAB.
mc = "matlab -nojvm -r "try; {}; catch; exit; end; exit"".format(command)
pid = subprocess.Popen(
        shlex.split(mc), stdout=open('null', 'w'), cwd=script_dirname)
retcode = pid.wait() %此句在windows下没有效果，我们可以改为time.sleep(5)。5即让主程序暂停5s，也可以写其他数值，不过在此之前，应该到代码顶部加一句import time。
if retcode != 0:
    raise Exception("Matlab script did not exit successfully!")

这下应该没什么错了吧，我忘了，实在遇到的错误太多，记得没有那么清楚了，有问题可以再提。

第三步，来运行detect.py。怎么说呢，这个文件要改的东西太多了，毕竟原文件时在linux系统下运行的，我的技术又太渣，并不会改成windows版本，所以决定放弃，转用Ubuntu。然而我在Ubuntu上装matlab的时候无聊搜了搜知乎，发现有人已经改成windows版了。所以我又决定再试试。参考的知乎https://zhuanlan.zhihu.com/p/22287237。

    把他的detect.py代码考过来，不过有很多错误，缩进格式就有错误，我还是上传一份我改过后的代码吧，

import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import os

import caffe

CROP_MODES = ['list', 'selective_search']
COORD_COLS = ['ymin', 'xmin', 'ymax', 'xmax']


def detect(pretrained_model,model_def,mean_file,input_file,gpu=False):
    mean=None
    if mean_file:
        mean = np.load(mean_file)
    if mean.shape[1:] != (1, 1):
        mean = mean.mean(1).mean(1)
        channel_swap = [2,1,0]

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

# Make detector.
    detector = caffe.Detector(model_def, pretrained_model, mean=mean,
    raw_scale=255.0,channel_swap=channel_swap,context_pad=16)

# Load input.
    t = time.time()
    print("Loading input...")
    if input_file.lower().endswith('txt'):
        with open(input_file) as f:
           inputs = [_.strip() for _ in f.readlines()]
    elif input_file.lower().endswith('csv'):
        inputs = pd.read_csv(input_file, sep=',', dtype={'filename': str})
        inputs.set_index('filename', inplace=True)
    else:
        raise Exception("Unknown input file type: not in txt or csv.")

# Detect.
    detections = detector.detect_selective_search(inputs)
    print("Processed {} windows in {:.3f} s.".format(len(detections),
                                                     time.time() - t))

# Collect into dataframe with labeled fields.
    df = pd.DataFrame(detections)
    df.set_index('filename', inplace=True)
    df[COORD_COLS] = pd.DataFrame(
        data=np.vstack(df['window']), index=df.index, columns=COORD_COLS)
    del(df['window'])
#此处我也使用了绝对路径
    with open('D:/caffeDev/caffe-windows/data/ilsvrc12/det_synset_words.txt', 'r') 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()])

    for filename in inputs:
        spec_df=df.loc[os.path.abspath(filename)]
        predictions_df = pd.DataFrame(np.vstack(spec_df.prediction.values), columns=labels_df['name'])
        max_s=predictions_df.max(0)
        max_s.sort_values(ascending=False,inplace=True)
        print(max_s[:10])

        i = predictions_df[max_s.index[0]].argmax()
        j = predictions_df[max_s.index[1]].argmax()

        im = plt.imread(filename)
        currentAxis = plt.gca()

        det = spec_df.iloc[i]
        coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
        currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=3))
        currentAxis.text(det['xmin'], det['ymin'], max_s.index[0],
        bbox=dict(facecolor='blue', alpha=0.5),
        fontsize=10, color='white')

        det = spec_df.iloc[j]
        coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
        currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=3))
        currentAxis.text(det['xmin'], det['ymin'], max_s.index[1],
        bbox=dict(facecolor='blue', alpha=0.5),
        fontsize=10, color='white')

        plt.axis('off')
        plt.imshow(im)
        plt.show()


if __name__=='__main__':
    pretrained_model='D:/caffeDev/caffe-windows/models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel'

	#为了避免错误，我使用的都是绝对路径。这里相对于原文件来说不同点就是原文件是在运行detect.py时在后面传相应的参数，而这里是直接定义了。
    model_def='D:/caffeDev/caffe-windows/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt'
    mean_file='D:/caffeDev/caffe-windows/models/bvlc_reference_rcnn_ilsvrc13/ilsvrc_2012_mean.npy'
    input_file='D:/caffeDev/caffe-windows/Build/x64/Release/pycaffe/det_input.txt'
    detect(pretrained_model,model_def,mean_file,input_file)

det_input.txt中只有一行图片的路径信息，如下图所示：

运行报错信息

ValueError: 'axis' entry 2 is out of bounds (-2, 2)

修改文件：~/caffe-master/python/caffe/detector.py
修改前：predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
修改后：predictions = out[self.outputs[0]].squeeze(

运行报错