94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
import time
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import numpy as np
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from PIL import Image
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import concurrent.futures
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# Estimates the absolute area by using the composite trapezoidal rule.
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# https://numpy.org/doc/stable/reference/generated/numpy.trapz.html
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def calculate_area(f, a, b, num_points=10000):
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x = np.linspace(a, b, num_points)
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y = f(x)
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area = np.trapz(np.abs(y), x)
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return area
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# Estimates the integral by using the composite trapezoidal rule.
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# https://numpy.org/doc/stable/reference/generated/numpy.trapz.html
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def calculate_integral(f, a, b, num_points=10000):
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x = np.linspace(a, b, num_points)
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y = f(x)
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integral = np.trapz(y, x)
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return integral
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def mysterious_transformation(data):
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y_pixel, x_pixel, _ = data.shape
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data_new = np.zeros([y_pixel, x_pixel, 3], dtype=np.uint8)
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for i in range(1, y_pixel - 1):
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for j in range(1, x_pixel - 1):
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for k in range(3):
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new_value = 5 * data[i, j, k] - data[i, j - 1, k] - data[i, j + 1, k] - data[i - 1, j, k] - data[
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i + 1, j, k]
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data_new[i, j, k] = max(0, min(new_value, 255))
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return data_new
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def mysterious_transformation_parallel(data, stripes=16):
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# Divide and conquer!
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y_pixel, x_pixel, _ = data.shape
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stripe_height = y_pixel // stripes
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stripe_width = x_pixel
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stripe_arrays = [data[i * stripe_height:(i + 1) * stripe_height, :] for i in range(stripes)]
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def calculate(stripe, i):
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y_stripe_pixel, x_stripe_pixel, _ = stripe.shape
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data_new = np.zeros([y_stripe_pixel, x_stripe_pixel, 3], dtype=np.uint8)
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height = i * stripe_height
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width = i * stripe_width
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for i in range(height, y_stripe_pixel - 1):
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for j in range(width, x_stripe_pixel - 1):
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for k in range(3):
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# No data race is to be expected (old image is only read)
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new_value = 5 * data[i, j, k] - data[i, j - 1, k] - data[i, j + 1, k] - data[i - 1, j, k] - data[i + 1, j, k]
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data_new[i, j, k] = max(0, min(new_value, 255))
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return data_new
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i = 0
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with concurrent.futures.ThreadPoolExecutor(stripes) as executor:
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# Compute for each slide and resample afterward
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data_new_stripes = list(executor.map(calculate, stripe_arrays, [i]))
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i += 1
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return np.concatenate(data_new_stripes)
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img = Image.open("lokomotive.png")
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pixels = np.asarray(img, dtype=np.uint8)
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# Measure execution time for mysterious_transformation
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# AMD Ryzen 7 5800X: 4.976848363876343 seconds
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start_time_normal = time.time()
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data_new_normal = mysterious_transformation(pixels)
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end_time_normal = time.time()
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execution_time_normal = end_time_normal - start_time_normal
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print("Execution time for normal method:", execution_time_normal, "seconds")
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# Measure execution time for mysterious_transformation_parallel
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# Execution time for parallel method: 0.0123138427734375 seconds
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start_time_parallel = time.time()
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data_new_parallel = mysterious_transformation_parallel(pixels, 256)
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end_time_parallel = time.time()
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execution_time_parallel = end_time_parallel - start_time_parallel
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print("Execution time for parallel method:", execution_time_parallel, "seconds")
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# Each time one pixel is different - don't know the reason? But this difference is negligible
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num_different_pixels = np.count_nonzero(np.sum(data_new_normal != data_new_parallel, axis=-1))
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print("Number of different pixels:", num_different_pixels)
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img_new_normal = Image.fromarray(data_new_normal, 'RGB')
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img_new_normal.save("new_normal_lokomotive.png")
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img_new_parallel = Image.fromarray(data_new_normal, 'RGB')
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img_new_parallel.save("new_parallel_lokomotive.png")
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