Ubuntu16.04 + Opencv3.4.2 + python3.5 + caffe

ゝ一纸荒年。 2024-04-19 15:13 44阅读 0赞

# Ubuntu16.04 + Opencv3.4.2 + python3.5 + caffe-ssd #

这一篇的前提是已经按照 [Ubuntu 16.04 装机指南（分区 + 显卡驱动 + Cuda9.0 + CUDNN7.0）][Ubuntu 16.04 _ _ _ _ Cuda9.0 _ CUDNN7.0] 安装好 驱动， Cuda9.0 和 CUDNN7.0 。

## 1. opencv3.4.2 ##

编译 opencv 是一个比较麻烦的过程，一步不对就可能会编译不成功。这部分主要参考

*  [Ubuntu16.04 + 源码编译OpenCV3.4.2][Ubuntu16.04 _ _OpenCV3.4.2]
 *  [Install OpenCV 3.0 and Python 3.4+ on Ubuntu][Install OpenCV 3.0 and Python 3.4_ on Ubuntu]
 *  [Ubuntu 16.04 Python3.5.2 下安装 OpenCV 3.4.1][Ubuntu 16.04 Python3.5.2 _ OpenCV 3.4.1]

这里进行一点补充：

*  根据参考1, 把依赖项单独下载下来再进行编译
 *  可以选择用 cmake-gui， 其实用命令也是不错的选择,下面是我用的命令，用的是 ubuntu16.04 自带的 python3.5， 你需要检查你的 python3 的相关路径，以及 EXTRA\_MODULES 放的位置。

cmake -D CMAKE_BUILD_TYPE=RELEASE  \
        -D INSTALL_C_EXAMPLES=OFF \
        -D INSTALL_PYTHON_EXAMPLES=ON \
        -D BUILD_EXAMPLES=OFF \
        -DENABLE_CXX11=ON \
        -D BUILD_opencv_python3=ON  \
        -D BUILD_opencv_python2=ON  \
        -D BUILD_opencv_java=OFF  \
        -D OPENCV_EXTRA_MODULES_PATH=~/opencv/opencv_contrib-3.4.2/modules \
        -D PYTHON3_EXECUTABLE=$(which python3)\
        -D PYTHON3_INCLUDE_DIR=/usr/include/python3.5m \
        -D PYTHON3_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.5m.so.1 \
        -D PYTHON3_NUMPY_PATH=/usr/local/lib/python3.5/dist-packages \
        -D PYTHON2_EXECUTABLE=$(which python2)\
        -D PYTHON2_INCLUDE_DIR=/usr/include/python2.7 \
        -D PYTHON2_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython2.7.so.1 \
        -D PYTHON2_NUMPY_PATH=/usr/local/lib/python2.7/dist-packages ..

这一步很关键，你需要检查输出的结果，如下必须要有 python3 相关的路径信息，否则后面即使编译成功了，其 python3 接口也无法调用。

![\[外链图片转存失败(img-m9LGarMB-1568997072943)(2019-09-20-20-51-38.png)\]][img-m9LGarMB-1568997072943_2019-09-20-20-51-38.png]

编译成功后可以看到 python2 和 python3 接口都编译成功了。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2t1d2VpY2Fp_size_16_color_FFFFFF_t_70]

![在这里插入图片描述][20190921114529961.png]

## 2. caffe-ssd ##

主要参考

*  [安装:caffe-ssd+python3.5(系统自带)+cuda9.0+cudnn7.1+ubuntu16.04][caffe-ssd_python3.5_cuda9.0_cudnn7.1_ubuntu16.04]
 *  [ubuntu16.04 在caffe中安装python3时踩到的坑][ubuntu16.04 _caffe_python3]

第一篇博客已经写的比较全了，不过文中没有将 opencv3 放出来。另外在执行

for req in $(cat requirements.txt); do sudo pip3 install --no-cache-dir $req; done

需要看是不是每项都符合要求，如果有哪一项不满足，会下载安装，但是比较慢，如果下载不成功，可以自己手动单独安装对应的项，然后在运行上面的命令，看是否都已经满足，在把所有的要求项都满足之后，后面 build 起来其实是非常顺利的。

# 3. 测试 caffe #

这部分主要参考 [SSD: Single Shot MultiBox Detector 检测单张图片][SSD_ Single Shot MultiBox Detector].  
但是在实际操作中，上面参考的代码并不能跑起来，部分是因为 上面测试的是 python2， 而我需要测试的是 python3，这一部分改动比较容易。还有一个原因就是环境还是有差别，比如在用到 python 的路径时直接指定。下面是我测试成功的代码。

################################################
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
    
    plt.rcParams['figure.figsize'] = (10, 10)
    plt.rcParams['image.interpolation'] = 'nearest'
    plt.rcParams['image.cmap'] = 'gray'
    
    # Make sure that caffe is on the python path:
    caffe_root = '/home/alvin/caffe/caffe-ssd/examples'  # this file is expected to be in {caffe_root}/examples
    import os
    os.chdir(caffe_root)
    import sys
    sys.path.insert(0, '/home/alvin/caffe/caffe-ssd/python')
    
    import caffe
    caffe.set_device(0)
    caffe.set_mode_gpu()
    
    ##################################################
    from google.protobuf import text_format
    from caffe.proto import caffe_pb2
    
    # load PASCAL VOC labels
    labelmap_file = '/home/alvin/caffe/caffe-ssd/data/VOC0712/labelmap_voc.prototxt'
    file = open(labelmap_file, 'r')
    labelmap = caffe_pb2.LabelMap()
    text_format.Merge(str(file.read()), labelmap)
    
    def get_labelname(labelmap, labels):
        num_labels = len(labelmap.item)
        labelnames = []
        if type(labels) is not list:
            labels = [labels]
        for label in labels:
            found = False
            for i in range(0, num_labels):
                if label == labelmap.item[i].label:
                    found = True
                    labelnames.append(labelmap.item[i].display_name)
                    break
            assert found == True
        return labelnames
        ###################################################
        model_def = '/home/alvin/caffe/caffe-ssd/models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt'
    model_weights = '/home/alvin/caffe/caffe-ssd/models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'
    
    net = caffe.Net(model_def,      # defines the structure of the model
                    model_weights,  # contains the trained weights
                    caffe.TEST)     # use test mode (e.g., don't perform dropout)
    
    # input preprocessing: 'data' is the name of the input blob == net.inputs[0]
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    transformer.set_transpose('data', (2, 0, 1))
    transformer.set_mean('data', np.array([104,117,123])) # mean pixel
    transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
    transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB
    ######################################################
    # set net to batch size of 1
    image_resize = 300
    net.blobs['data'].reshape(1,3,image_resize,image_resize)
    ######################################################
    image = caffe.io.load_image('/home/alvin/caffe/caffe-ssd/examples/images/fish-bike.jpg')
    plt.imshow(image)
    transformed_image = transformer.preprocess('data', image)
    net.blobs['data'].data[...] = transformed_image
    
    # Forward pass.
    detections = net.forward()['detection_out']
    
    # Parse the outputs.
    det_label = detections[0,0,:,1]
    det_conf = detections[0,0,:,2]
    det_xmin = detections[0,0,:,3]
    det_ymin = detections[0,0,:,4]
    det_xmax = detections[0,0,:,5]
    det_ymax = detections[0,0,:,6]
    
    # Get detections with confidence higher than 0.6.
    top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
    
    top_conf = det_conf[top_indices]
    top_label_indices = det_label[top_indices].tolist()
    top_labels = get_labelname(labelmap, top_label_indices)
    top_xmin = det_xmin[top_indices]
    top_ymin = det_ymin[top_indices]
    top_xmax = det_xmax[top_indices]
    top_ymax = det_ymax[top_indices]
    ############################################################
    colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
    
    plt.imshow(image)
    currentAxis = plt.gca()
    
    for i in range(top_conf.shape[0]):
        xmin = int(round(top_xmin[i] * image.shape[1]))
        ymin = int(round(top_ymin[i] * image.shape[0]))
        xmax = int(round(top_xmax[i] * image.shape[1]))
        ymax = int(round(top_ymax[i] * image.shape[0]))
        score = top_conf[i]
        label = int(top_label_indices[i])
        label_name = top_labels[i]
        display_txt = '%s: %.2f'%(label_name, score)
        coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
        color = colors[label]
        currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
        currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})

[Ubuntu 16.04 _ _ _ _ Cuda9.0 _ CUDNN7.0]: https://blog.csdn.net/kuweicai/article/details/101083390
[Ubuntu16.04 _ _OpenCV3.4.2]: https://blog.csdn.net/qq_26239525/article/details/83059905
[Install OpenCV 3.0 and Python 3.4_ on Ubuntu]: https://www.pyimagesearch.com/2015/07/20/install-opencv-3-0-and-python-3-4-on-ubuntu/
[Ubuntu 16.04 Python3.5.2 _ OpenCV 3.4.1]: https://www.jianshu.com/p/80f74f47f987
[img-m9LGarMB-1568997072943_2019-09-20-20-51-38.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/b59bd3d22a5b4debb3bc227d7844bd47.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2t1d2VpY2Fp_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/272eae3171ba42a98098a003c31fdaac.png
[20190921114529961.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/00213df447974f80b2e51b9df2277c35.png
[caffe-ssd_python3.5_cuda9.0_cudnn7.1_ubuntu16.04]: https://blog.csdn.net/weixin_43317842/article/details/88352670
[ubuntu16.04 _caffe_python3]: https://blog.csdn.net/jieshaoxiansen/article/details/81321918
[SSD_ Single Shot MultiBox Detector]: https://blog.csdn.net/jesse_mx/article/details/52965281