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34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
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from __future__ import print_function
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import numpy as np
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import cv2
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from cv2 import dnn
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import timeit
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def prepare_image(img):
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img = cv2.resize(img, (224, 224))
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#convert interleaved image (RGBRGB) to planar(RRGGBB)
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blob = np.moveaxis(img, 2, 0)
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blob = np.reshape(blob.astype(np.float32), (-1, 3, 224, 224))
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return blob
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def timeit_forward(net):
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print("OpenCL:", cv2.ocl.useOpenCL())
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print("Runtime:", timeit.timeit(lambda: net.forward(), number=10))
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def get_class_list():
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with open('synset_words.txt', 'rt') as f:
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return [ x[x.find(" ") + 1 :] for x in f ]
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blob = prepare_image(cv2.imread('space_shuttle.jpg'))
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print("Input:", blob.shape, blob.dtype)
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cv2.ocl.setUseOpenCL(True) #Disable OCL if you want
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net = dnn.readNetFromCaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel')
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net.setBlob(".data", blob)
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net.forward()
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#timeit_forward(net) #Uncomment to check performance
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prob = net.getBlob("prob")
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print("Output:", prob.shape, prob.dtype)
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classes = get_class_list()
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print("Best match", classes[prob.argmax()])
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