Add some steps
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@ -3,6 +3,7 @@ from keras.models import Model, load_model
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from keras.utils import to_categorical
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from keras.utils import to_categorical
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import cv2
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import cv2
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from skimage import color, exposure
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from skimage import color, exposure
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from _cutter import image_cut
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def man_result_check():
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def man_result_check():
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pred_y = np.load("predicted_results.npy")
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pred_y = np.load("predicted_results.npy")
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@ -50,5 +51,22 @@ def is_Wally(trained_model_path, image, from_file=False):
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# Mark Wally image somehow (colour the border)
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# Mark Wally image somehow (colour the border)
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# Stitch original image back together
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# Stitch original image back together
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if __name__ == '__main__':
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# Read image
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image = cv2.imread("10.jpg")
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# Split image
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cuts = image_cut(image, 64, 64)
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for i in len(cuts):
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# Transform block
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hsv = color.rgb2hsv(cuts[i])
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hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
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block = color.hsv2rgb(hsv)
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block = np.rollaxis(block, -1)
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if is_Wally("Waldo.h5", block):
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# Border block
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cuts[i] = cv2.copyMakeBorder(cuts[i],5,5,5,5,cv2.BORDER_CONSTANT,value=RED)
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is_Wally("Waldo.h5", image)
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# Stitch image TODO!
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# Show image
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cv.imshow('Image', image)
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cv.waitKey(0)
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