caffe利用训练好的模型进行实际测试

前面的博客介绍了如何生成多label的训练数据，也介绍了测试时对图片处理需要注意的点，这篇博客就来介绍如何利用训练好的模型进行实际测试

官方版demo

如果按照前面我的博客使用PIL库生成数据(resize后插值结果不同)，就会和官方的demo不是很一致，因为它使用了skimag库进行resize，虽然都是双线性插值也会有差异。

import numpy as npimport osimport sysimport cv2caffe_root = 'your caffe root'sys.path.insert(0, caffe_root + '/python')import caffecaffe.set_device(1)caffe.set_mode_gpu()model_def = 'xxx_deploy.prototxt'model_weights = 'xxx.caffemodel'net = caffe.Net(model_def, model_weights, caffe.TEST)net.blobs['data'].reshape(1,3,h,w) # (batch_size,c,h,w)transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})transformer.set_transpose('data', (2,0,1))  # (h,w,c)--->(c,h,w)transformer.set_mean('data', np.array([104,117,123])) #注意是 BGR'''或者通过caffe_root/build/tools/compute_image_mean 计算图像均值得到xxx.npymu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')mu = mu.mean(1).mean(1)  #对所有像素值取平均以此获取BGR的均值像素值print 'mean-subtracted values:', zip('BGR', mu)'''transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]transformer.set_channel_swap('data', (2,1,0))# RGB--->BGR'''可能你对均值设置成BGR有疑问，不是到后面才把RGB转为BGR吗?其实transformer.setXXX()这些只是设置属性，实际执行顺序是参考附录preprocess函数定义'''image_names = [图片list]for image_name in image_names:    image = caffe.io.load_image(image_name)# 用的是skimage库，见附录    # 利用刚刚的设置进行图片预处理    transformed_image = transformer.preprocess('data', image)    net.blobs['data'].data[...] = transformed_image    # 网络前传（测试无后传）    output = net.forward()    output_prob= output['prob'][0].argmax()  # 概率最大的label    print image_name,output_prob

自定义版(全部使用PIL)

训练程序不动，只修改测试程序

import numpy as npimport osimport sysfrom PIL import Imagecaffe_root = 'your caffe root'sys.path.insert(0, caffe_root + '/python')import caffecaffe.set_device(1)caffe.set_mode_gpu()model_def = 'xxx_deploy.prototxt'model_weights = 'xxx.caffemodel'net = caffe.Net(model_def, model_weights, caffe.TEST)image_names = [图片list]for image_name in image_names:    im = Image.open(image_name)     im = im.resize((w, h), Image.BILINEAR)    im = np.array(im, dtype=np.float32) # im:(h,w,3) RGB    im = im[:, :, ::-1] # im: (h,w,3) BGR    im -= np.array((104, 117, 123)) # 减去均值，注意是 BGR    im = im.transpose((2, 0, 1)) # im:(3,h,w)    net.blobs['data'].reshape(1, *im.shape)    net.blobs['data'].data[...] = im    output = net.forward()    output_prob= output['prob'][0].argmax()  # 概率最大的label    print image_name,output_prob

自定义版(全部使用skimage)

修改训练数据重新训练(有点浪费时间)，测试程序使用官方版[demo]
(https://github.com/BVLC/caffe/blob/master/examples/00-classification.ipynb)
训练数据生成的详细程序见之前博客caffe实现多标签输入

### 声明：未实际测试，谨慎使用import skimagefrom skimage import io,transform#... in_db=lmdb.open('your data(images) lmdb path',map_size=int(1e12))with in_db.begin(write=True) as in_txn:    for in_idx,in_ in enumerate(file_list):                 im_file='your images path'+in_        im=io.imread(im_file,as_grey=False)        im = transform.resize(im,(h, w)，order=1)        im = im * 255 # [0-1]->[0-255]        im=im[:,:,::-1]#把im的RGB调整为BGR        im_dat=caffe.io.array_to_datum(im)        in_txn.put('{:0>10d}'.format(in_idx),im_dat.SerializeToString())           print 'data train: {} [{}/{}]'.format(in_, in_idx+1, len(file_list))                del im_file, im, im_datin_db.close()print 'train data(images) are done!'

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代码附录：

#　都在caffe_root/python/caffe/io.py中定义#　附录preprocess函数定义def preprocess(self, in_, data):"""        Format input for Caffe:        - convert to single        - resize to input dimensions (preserving number of channels)        - transpose dimensions to K x H x W        - reorder channels (for instance color to BGR)        - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)        - subtract mean        - scale feature        Parameters        ----------        in_ : name of input blob to preprocess for        data : (H' x W' x K) ndarray        Returns        -------        caffe_in : (K x H x W) ndarray for input to a Net        """   self.__check_input(in_)        caffe_in = data.astype(np.float32, copy=False)        transpose = self.transpose.get(in_)        channel_swap = self.channel_swap.get(in_)        raw_scale = self.raw_scale.get(in_)        mean = self.mean.get(in_)        input_scale = self.input_scale.get(in_)        in_dims = self.inputs[in_][2:] # 初始化Transformer时传入了        #{'data': net.blobs['data'].data.shape} in_dims就是deploy的(h,w)        # resize_image见附录  shape[:2](h,w)-> in_dims        if caffe_in.shape[:2] != in_dims:            caffe_in = resize_image(caffe_in, in_dims)　# 默认双线性插值        # (h,w,c)->(c,h,w)        if transpose is not None:            caffe_in = caffe_in.transpose(transpose)        # RGB->BGR        if channel_swap is not None:            caffe_in = caffe_in[channel_swap, :, :]        # [0-1]->[0-255]        if raw_scale is not None:            caffe_in *= raw_scale        #　减去均值(BGR)        if mean is not None:            caffe_in -= mean        if input_scale is not None:            caffe_in *= input_scale        return caffe_in#　附录caffe.io.load_image函数定义def load_image(filename, color=True):    """    Load an image converting from grayscale or alpha as needed.    Parameters    ----------    filename : string    color : boolean        flag for color format. True (default) loads as RGB while False        loads as intensity (if image is already grayscale).    Returns    -------    image : an image with type np.float32 in range [0, 1]        of size (H x W x 3) in RGB or        of size (H x W x 1) in grayscale.    """   ############注意skimage###############    img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32)　# 转为float32    if img.ndim == 2: # 灰度图　int gray = 0.3*red + 0.59*green + 0.11*blue;    # 改进版int gray = (77*red + 150*green + 29*blue + 128) >> 8;        img = img[:, :, np.newaxis]　# (h,w)->(h,w,1)        if color:            img = np.tile(img, (1, 1, 3)) # (h,w,1)->(h,w,3)把灰度重复一下    elif img.shape[2] == 4:　# shape:(h,w,c) 即c = 4　png４个通道　alpha透明值        img = img[:, :, :3]　＃　只取０,1,2这三个通道    return img#　附录resize_image函数定义def resize_image(im, new_dims, interp_order=1):    """    Resize an image array with interpolation.    Parameters    ----------    im : (H x W x K) ndarray    new_dims : (height, width) tuple of new dimensions.    interp_order : interpolation order, default is linear.双线性    参考上篇博客中interp_order的设置    Returns    -------    im : resized ndarray with shape (new_dims[0], new_dims[1], K)    """    # 灰度图或者RGB三通道图    if im.shape[-1] == 1 or im.shape[-1] == 3:        im_min, im_max = im.min(), im.max()        if im_max > im_min:            # skimage is fast but only understands {1,3} channel images            # in [0, 1].            im_std = (im - im_min) / (im_max - im_min)            resized_std = resize(im_std, new_dims, order=interp_order)            # 文件开头有from skimage.transform import resize 就是skimage中的resize函数            resized_im = resized_std * (im_max - im_min) + im_min        else:            # the image is a constant -- avoid divide by 0            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),                           dtype=np.float32)            ret.fill(im_min)            return ret    else:        # ndimage interpolates anything but more slowly.        scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))        resized_im = zoom(im, scale + (1,), order=interp_order)    return resized_im.astype(np.float32)