86 lines
2.0 KiB
Python
86 lines
2.0 KiB
Python
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import threading
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import time
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import numpy as np
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from PIL import Image
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pool = []
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conv_counter = 0
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def image_convolution(data, conv_matrix):
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global conv_counter, pool
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y_data_pixels, x_data_pixels, _ = data.shape
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for y_data in range(1, y_data_pixels - 1):
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t = threading.Thread(target=image_convolution_row, args=(data, conv_matrix, y_data))
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pool.append(t)
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t.start()
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while any(t.is_alive() for t in pool):
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time.sleep(1)
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print(f"Loading with {len(pool)} threads on convolution {conv_counter}...")
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for thread in pool:
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thread.join()
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pool = []
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conv_counter += 1
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def image_convolution_row(data, conv_matrix, y_data):
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y_data_pixels, x_data_pixels, _ = data.shape
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for x_data in range(1, x_data_pixels - 1):
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t = threading.Thread(target=image_convolution_pixel, args=(data, conv_matrix, y_data, x_data))
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pool.append(t)
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t.start()
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def image_convolution_pixel(data, conv_matrix, y_data, x_data):
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for color in range(3):
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new_val = 0
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for y_conv in range(0, 3):
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for x_conv in range(0, 3):
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# new_val += conv_matrix[y_conv, x_conv] * data[y_data + y_conv - 1, x_data + x_conv - 1]
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continue
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global grid_new
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new_val = max(0, min(new_val, 255))
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grid_new[y_data, x_data, color] = new_val
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image = Image.open("koko.jpg")
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grid = np.array(image, dtype=np.uint8)
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grid_new = np.zeros(grid.shape)
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y_pixels, x_pixels, _ = grid.shape
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convolutions = [
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np.array([
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[0, 1, 0],
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[0, 0, 0],
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[0, -1, 0]
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]),
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np.array([
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[1, 0, 0],
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[0, 0, 0],
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[0, 0, -1]
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]),
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np.array([
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[0, 0, 0],
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[1, 0, -1],
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[0, 0, 0]
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]),
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np.array([
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[0, 0, 1],
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[0, 0, 0],
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[-1, 0, 0]
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])
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]
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for y in range(y_pixels):
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for x in range(x_pixels):
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grid[y][x] = (grid[y][x] - (256 / 2)) % 256
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for convolution in convolutions:
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image_convolution(grid, convolution)
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grid = grid_new
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image = Image.fromarray(grid)
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image.show()
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