69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
import numpy as np
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from numpy.lib.stride_tricks import as_strided
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from PIL import Image
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import matplotlib.pyplot as plt
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def load_data():
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return np.asarray(Image.open("cat.png").convert("L"), dtype=np.uint8) / 255
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image = load_data()
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def dimension(length, padding, stride, kernel):
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return int((length + 2 * padding - kernel + 1)/stride)
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def pad(image, size, strategy):
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(ih, iw) = image.shape
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(ph, pw) = size
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(rh, rw) = (ih + 2*ph, iw + 2*pw)
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if strategy == 'zero':
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base = np.zeros((rh, rw))
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base[ph:ph+ih, pw:pw+iw] = image.copy()
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return base
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elif strategy == 'mirror':
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raise NotImplementedError("Missing")
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def convolute(weights):
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return lambda view : np.dot(view.flatten(), weights.flatten())
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def pool(image, stride=1, kernel_size=(2,2), transform=np.max, padding_size=(0,0), padding_strategy='zero'):
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# calculate output size
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(height, width) = [dimension(image.shape[i], padding_size[i], stride, kernel_size[i]) for i in range(2)]
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padded = pad(image, padding_size, padding_strategy)
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next = np.zeros((height, width))
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#'zero'|'mirror'
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for i in range(height):
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for j in range(width):
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# Translate upper left corner into original image space
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(y, x) = (i*stride, j*stride)
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# Retrieve image window
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view = padded[y:y+kernel_size[0], x:x+kernel_size[1]]
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#print(view)
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# Set pooled image value
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next[i][j] = transform(view)
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return next
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weights = np.array([
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[-10,-5,0,5,10],
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[-10,-5,0,5,10],
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[-10,-5,0,5,10],
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[-10,-5,0,5,10],
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[-10,-5,0,5,10]
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])
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new1 = pool(image, kernel_size=(5,5), padding_size=(2,2), transform=convolute(weights))
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new2 = pool(new1, stride=3, kernel_size=(30,30), transform=np.max)
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fig = plt.figure()
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fig.canvas.manager.set_window_title("A Cat")
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plt.imshow(new2, cmap="gray")
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plt.show() |