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Tetragramm
GitHub Repository: Tetragramm/opencv
 Path: blob/master/modules/dnn/test/imagenet_cls_test_inception.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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import tensorflow as tf

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from tensorflow.python.platform import gfile

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from imagenet_cls_test_alexnet import MeanValueFetch, DnnCaffeModel, Framework, ClsAccEvaluation

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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 you've got an exception "Cannot load libmkl_avx.so or libmkl_def.so" or similar, try to export next variable

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# before running the script:

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# LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so

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class TensorflowModel(Framework):

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    sess = tf.Session

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    output = tf.Graph

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    def __init__(self, model_file, in_blob_name, out_blob_name):

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        self.in_blob_name = in_blob_name

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        self.sess = tf.Session()

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        with gfile.FastGFile(model_file, 'rb') as f:

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            graph_def = tf.GraphDef()

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            graph_def.ParseFromString(f.read())

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            self.sess.graph.as_default()

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            tf.import_graph_def(graph_def, name='')

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        self.output = self.sess.graph.get_tensor_by_name(out_blob_name + ":0")

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    def get_name(self):

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        return 'Tensorflow'

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    def get_output(self, input_blob):

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        assert len(input_blob.shape) == 4

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        batch_tf = input_blob.transpose(0, 2, 3, 1)

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        out = self.sess.run(self.output,

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                       {self.in_blob_name+':0': batch_tf})

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        out = out[..., 1:1001]

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        return out

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class DnnTfInceptionModel(DnnCaffeModel):

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    net = cv.dnn.Net()

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    def __init__(self, model_file, in_blob_name, out_blob_name):

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        self.net = cv.dnn.readNetFromTensorflow(model_file)

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        self.in_blob_name = in_blob_name

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        self.out_blob_name = out_blob_name

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    def get_output(self, input_blob):

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        return super(DnnTfInceptionModel, self).get_output(input_blob)[..., 1:1001]

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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, download it here:"

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                            "https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/img_classes_inception.txt")

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    parser.add_argument("--model", help="path to tensorflow model, download it here:"

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                                        "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip")

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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=1)

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

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

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    parser.add_argument("--out_blob", help="name for output blob", default='softmax2')

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    args = parser.parse_args()

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    data_fetcher = MeanValueFetch(args.frame_size, args.imgs_dir, True)

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    frameworks = [TensorflowModel(args.model, args.in_blob, args.out_blob),

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                  DnnTfInceptionModel(args.model, '', 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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