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Open Source Computer Vision Library
https://opencv.org/
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39 lines
2.3 KiB
39 lines
2.3 KiB
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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