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Tetragramm
GitHub Repository: Tetragramm/opencv
 Path: blob/master/modules/dnn/test/imagenet_cls_test_googlenet.py
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import numpy as np

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import sys

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import os

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import argparse

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from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation

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try:

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    import caffe

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except ImportError:

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    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

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                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')

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try:

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    import cv2 as cv

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except ImportError:

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    raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '

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                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')

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if __name__ == "__main__":

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    parser = argparse.ArgumentParser()

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    parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")

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    parser.add_argument("--img_cls_file", help="path to file with classes ids for images, val.txt file from this "

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                                               "archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")

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    parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "

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                                        "https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt")

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    parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "

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                                             "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel")

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    parser.add_argument("--log", help="path to logging file")

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    parser.add_argument("--batch_size", help="size of images in batch", default=500, type=int)

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    parser.add_argument("--frame_size", help="size of input image", default=224, type=int)

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    parser.add_argument("--in_blob", help="name for input blob", default='data')

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    parser.add_argument("--out_blob", help="name for output blob", default='prob')

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    args = parser.parse_args()

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    data_fetcher = MeanChannelsFetch(args.frame_size, args.imgs_dir)

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    frameworks = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob),

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                  DnnCaffeModel(args.prototxt, args.caffemodel, '', args.out_blob)]

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    acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size)

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    acc_eval.process(frameworks, data_fetcher)

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