Caffe深度学习入门——python调用caffe训练好的模型检测单帧图片

python调用caffe训练好的cifar10_quick_iter_4000.caffemodel模型检测单帧图片

python直接调用caffe训练好的模型，进行单帧图片检测，并显示检测结果。caffe自带的classify.py文件检测结果直接保存到了foo文件，不能直观显示，这里加入几行显示的代码，方便直接测试查看结果，运行OK，笔记mark。

#!/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 npimport osimport sysimport argparseimport globimport timeimport caffedef 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,                "../models/bvlc_reference_caffenet/deploy.prototxt"),        help="Model definition file."    )    parser.add_argument(        "--pretrained_model",        default=os.path.join(pycaffe_dir,                "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),        help="Trained model weights file."    )    #新增显示部分    parser.add_argument(        "--labels_file",        default=os.path.join("../python/classify_words.txt"),        help="cifar result words file")    parser.add_argument(        "--force_grayscale",        action='store_true',        help="Convert RGB images down to single-channel grayscale versions," +            "usefull for single-channel networks like MNIST.")    parser.add_argument(        "--print_results",        action='store_true',        help="write output text to stdout rather than serializing to a file.")    #新增显示部分    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='256,256',        help="Canonical 'height,width' dimensions of input images."    )    parser.add_argument(        "--mean_file",        default=os.path.join(pycaffe_dir,                             'caffe/imagenet/ilsvrc_2012_mean.npy'),        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 args.mean_file:        mean = np.load(args.mean_file)    mean = mean.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 file: %s" % args.input_file)        inputs = [caffe.io.load_image(args.input_file)]    print("Classifying %d inputs." % len(inputs))    # Classify.    start = time.time()    predictions = classifier.predict(inputs, not args.center_only)    print("Done in %.2f s." % (time.time() - start))    print("Predictions: %s" % predictions)    #新增显示部分    if args.print_results:        scores = predictions.flatten()        with open(args.labels_file) as f:        labels_df = pd.DataFrame([            {                'synset_id': l.strip().split(' ')[0],                'name': ' '.jion(l.strip().split(' ')[1:]).split(',')[0]            }            for l in f.readlines()])        labels = labels_df.sort('synset_id')['name'].values        indices = (-scores).argsort()[:5]        ps = labels[indices]        meta = [(p, '%.5f' % scores[i])            for i, p in zip(indices, ps)]        print meta    #新增显示部分    # Save    print("Saving results into %s" % args.output_file)    np.save(args.output_file, predictions)if __name__ == '__main__':    main(sys.argv)

调用cifar10_quick_iter_4000.caffemodel 模型进行检测

cd ~/caffe/pythonppython classify02.py --model_def ~/caffe/examples/cifar10/cifar10_quick.prototxt --pretrained_model ~/caffe/examples/cifar10/cifar10_quick_iter_4000.caffemodel --labels_file ~/caffe/data/cifar10/cifar10_words.txt --center_only ~/caffe/examples/images/fish-bike.jpg foo

运行结果如下：

直接用caffe默认用的ImageNet的模型进行检测

cd ~/caffe/pythonpython python/classify.py ~/caffe/examples/images/cat.jpg foo