74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
import numpy as np
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from keras.models import Model, load_model
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from keras.utils import to_categorical
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import cv2
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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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pred_y = np.load("predicted_results.npy")
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test_y = np.load("Waldo_test_lbl.npy")
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test_y = to_categorical(test_y)
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f = open("test_output.txt", 'w')
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z = 0
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for i in range(0, len(test_y)):
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print(pred_y[i], test_y[i], file=f)
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# Calculates correct predictions
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if pred_y[i][0] == test_y[i][0]:
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z+=1
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print("Accuracy: {}%".format(z/len(test_y)*100))
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f.close()
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'''
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Purpose:Loads a trained neural network model (using Keras) to classify an image
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Input: path/to/trained_model
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image [or] path/to/image [if from_file=True]
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Returns:Boolean variable
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'''
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def is_Wally(trained_model_path, image, from_file=False):
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if from_file:
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img = cv2.imread(image) # Opens the image (in BGR format)
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# Histogram normalization in v channel
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hsv = color.rgb2hsv(img)
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hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
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img = color.hsv2rgb(hsv)
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image = np.rollaxis(img, -1) # Rolls the colour axis to the front
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trained_model = load_model(trained_model_path)
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if trained_model.predict(image, verbose=1, batch_size=1)[0] == 1:
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return 0
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else:
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return 1
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# Load fully puzzle image
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# Split image into array of images
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# use is_Wally(img) to classify image
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# Mark Wally image somehow (colour the border)
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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 range(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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# if True:
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# Border block
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GREEN = [0, 255, 0]
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cuts[i] = cv2.copyMakeBorder(cuts[i][1:61,1:61],2,2,2,2,cv2.BORDER_CONSTANT,value=GREEN)
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# Stitch image TODO!
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# Show image
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cv2.imwrite('output.png',image)
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