108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""
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Before the first run, you need to have all necessary Python packages installed. For
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that we highly recommend firstly creating Virtual Environment, to have your
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development environment seperated from other projects (https://docs.python.org/3/tutorial/venv.html).
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In system terminal then run: "pip install numpy opencv-python matplotlib Pillow"
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"""
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import numpy as np
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import cv2 as cv2
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from matplotlib import pyplot as plt
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from PIL import Image
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from typing import Union
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def imread(path: str) -> np.float64:
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"""
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Reads an image in RGB order. Image type is transformed from uint8 to float, and
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range of values is reduced from [0, 255] to [0, 1].
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"""
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I = Image.open(path).convert('RGB') # PIL image.
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I = np.asarray(I) # Converting to Numpy array.
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I = I.astype(np.float64) / 255
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return np.float64(I)
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def imread_gray(path: str) -> np.float64:
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"""
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Reads an image in gray. Image type is transformed from uint8 to float, and
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range of values is reduced from [0, 255] to [0, 1].
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"""
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I = Image.open(path).convert('L') # PIL image opening and converting to gray.
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I = np.asarray(I) # Converting to Numpy array.
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I = I.astype(np.float64) / 255
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return np.float64(I)
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def imshow(img: Union[np.float64, np.uint8], title=None) -> None:
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"""
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Shows an image. Image can be of types:
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- type uint8, in range [0, 255]
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- type float, in range [0, 1]
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"""
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if len(img.shape) == 3:
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plt.imshow(img) # if type of data is "float", then values have to be in [0, 1]
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else:
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plt.imshow(img)
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plt.set_cmap('gray') # also "hot", "nipy_spectral"
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plt.colorbar()
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if title is not None:
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plt.title(title)
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plt.show()
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def signal_show(*signals):
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"""
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Plots all given 1D signals in the same plot.
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Signals can be Python lists or 1D numpy array.
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"""
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for s in signals:
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if type(s) == np.ndarray:
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s = s.squeeze()
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plt.plot(s)
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plt.show()
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def convolve(I: np.ndarray, *ks):
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"""
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Convolves input image I with all given kernels.
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:param I: Image, should be of type float64 and scaled from 0 to 1.
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:param ks: 2D Kernels
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:return: Image convolved with all kernels.
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"""
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for k in ks:
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k = np.flip(k) # filter2D performs correlation, so flipping is necessary
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I = cv2.filter2D(I, cv2.CV_64F, k)
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return I
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if __name__ == '__main__': # False if this file is imported to another file and executed from there.
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# For this to work you need to put an image "image.jpg" to where you run the script.
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# Read an image in rgb (check the code in "UZ_utils" for details).
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I = imread('image.jpg') # see the definition above
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imshow(I, 'rgb')
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print(I.shape, I.dtype, np.max(I))
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# gray
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I = imread_gray('image.jpg') # see the definition above
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imshow(I, 'gray')
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print(I.shape, I.dtype, np.max(I))
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# Image filtering.
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I = np.zeros((20, 20), dtype=float)
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I[5, 5] = 1
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k = np.array([[1, 2, 3, 4, 5]], dtype=float)
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I = convolve(I, k, k.T) # see the definition above
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imshow(I)
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# Plotting 1D signal.
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x = np.arange(0, 10, .1) # From 0 to 10 with 0.1 step.
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y = np.sin(x)
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signal_show(y, 2 * y) # see the definition above
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