Atividade-pratica2-PDI/spatial_filtering.py at main · Mathwebb/Atividade-pratica2-PDI · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
from PIL import Image
from math import ceil, floor


def image_to_matrix(image: Image) -> list:
    image_matrix = []
    for line in range(image.width):
        image_matrix.append([image.getpixel((line, 0))])
        for column in range(1, image.height):
            image_matrix[line].append(image.getpixel((line, column)))
    return image_matrix


def matrix_to_image(matrix: list) -> Image:
    image = Image.new('L', (len(matrix), len(matrix[0])))
    for line in range(len(matrix)):
        for column in range(len(matrix[line])):
            image.putpixel((line, column), matrix[line][column])
    return image


def create_empty_matrix(m, n) -> list:
    image_matrix = []
    for line in range(m):
        image_matrix.append([])
        for column in range(n):
            image_matrix[line].append(0)
    return image_matrix


def arithmetic_mean_filter(image: Image, m: int, n: int, modify: bool = False) -> Image:
    if not modify:
        image = image.copy()
    original_image_matrix = image.load()
    m1 = floor(m/2)
    n1 = floor(n/2)
    for line in range(image.width):
        for column in range(image.height):
            central_pixel = 0
            for mask_line in range(-m1, m1+1):
                for mask_column in range(-n1, n1+1):
                    try:
                        central_pixel += original_image_matrix[line+mask_line, column+mask_column]
                    except IndexError:
                        pass
            image.putpixel((line, column), central_pixel//(m*n))
    return image

def weighted_arithmetic_mean_filter(image: Image, mask: list, modify: bool = False) -> Image:
    if not modify:
        image = image.copy()
    original_image_matrix = image_to_matrix(image)
    m1 = floor(len(mask)/2)
    n1 = floor(len(mask[0])/2)
    sum_mask = 0
    for line in range(len(mask)):
        for column in range(len(mask[line])):
            sum_mask += mask[line][column]
    for line in range(image.width):
        for column in range(image.height):
            central_pixel = 0
            for mask_line in range(-m1, m1+1):
                for mask_column in range(-n1, n1+1):
                    try:
                        central_pixel += original_image_matrix[line+mask_line][column+mask_column] * mask[mask_line+m1][mask_column+n1]
                    except IndexError:
                        pass
            image.putpixel((line, column), central_pixel//sum_mask)
    return image


def median_filter(image: Image, m: int, n: int, modify: bool = False) -> Image:
    if not modify:
        image = image.copy()
    original_image_matrix = image_to_matrix(image)
    m1 = floor(m/2)
    n1 = floor(n/2)
    for line in range(image.width):
        for column in range(image.height):
            image_pixels = []
            for mask_line in range(-m1, m1+1):
                for mask_column in range(-n1, n1+1):
                    try:
                        image_pixels.append(original_image_matrix[line+mask_line][column+mask_column])
                    except IndexError:
                        pass
            image_pixels.sort()
            image.putpixel((line, column), image_pixels[len(image_pixels)//2])
    return image


def apply_linear_filter(image: Image, mask: list) -> list:
    original_image_matrix = image_to_matrix(image)
    filtered_image_matrix = create_empty_matrix(image.width, image.height)
    n1 = floor(len(mask)/2)
    m1 = floor(len(mask[0])/2)
    for line in range(image.width):
        for column in range(image.height):
            central_pixel = 0
            for mask_line in range(-m1, m1+1):
                for mask_column in range(-n1, n1+1):
                    try:
                        central_pixel += original_image_matrix[line+mask_line][column+mask_column] *\
                                         mask[mask_column+n1][mask_line+m1]
                    except IndexError:
                        pass
            if central_pixel > 255:
                central_pixel = 255
            if central_pixel < -255:
                central_pixel = -255
            filtered_image_matrix[line][column] = central_pixel
    return filtered_image_matrix


def laplacian_filter(image: Image, center_value: int = -8, modify: bool = False, adjusted_laplacian: bool = False) -> Image:
    if not modify:
        image = image.copy()
    if center_value == -8:
        laplacian_mask = [[1, 1, 1], [1, -8, 1], [1, 1, 1]]
    elif center_value == 8:
        laplacian_mask = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]
    elif center_value == -4:
        laplacian_mask = [[0, 1, 0], [1, -4, 1], [0, 1, 0]]
    elif center_value == 4:
        laplacian_mask = [[0, -1, 0], [-1, 4, -1], [0, -1, 0]]
    else:
        return None

    laplacian_image = image.copy()
    laplacian_image_matrix = apply_linear_filter(image, laplacian_mask)
    if adjusted_laplacian:
        for line in range(len(laplacian_image_matrix)):
            for column in range(len(laplacian_image_matrix[line])):
                pixel = laplacian_image_matrix[line][column]
                if pixel == 0:
                    laplacian_image.putpixel((line, column), 127)
                elif pixel < 0:
                    pixel *= -1
                    if pixel % 2 == 0:
                        laplacian_image.putpixel((line, column), 127 - pixel // 2)
                    else:
                        laplacian_image.putpixel((line, column), 127 - (pixel - 1) // 2)
                else:
                    if pixel % 2 == 0:
                        laplacian_image.putpixel((line, column), pixel // 2 + 127)
                    else:
                        laplacian_image.putpixel((line, column), (pixel - 1) // 2 + 127)
    else:
        laplacian_image = matrix_to_image(laplacian_image_matrix)
    for line in range(image.width):
        for column in range(image.height):
            if center_value < 0:
                image.putpixel((line, column), image.getpixel((line, column)) - laplacian_image_matrix[line][column])
            else:
                image.putpixel((line, column), image.getpixel((line, column)) + laplacian_image_matrix[line][column])

    return image, laplacian_image


def unsharp_masking(image: Image, n: int, m: int, modify: bool = False) -> Image:
    if not modify:
        image = image.copy()
    original_image_matrix = image_to_matrix(image)
    blurred_image = arithmetic_mean_filter(image, n, m)
    blurred_image_matrix = image_to_matrix(blurred_image)

    for line in range(image.width):
        for column in range(image.height):
            image.putpixel((line, column), original_image_matrix[line][column] +
            (original_image_matrix[line][column] - blurred_image_matrix[line][column]))
    return image


def high_boost(image: Image, m: int, n: int, k: float, modify: bool = False) -> Image:
    if not modify:
        image = image.copy()
    original_image_matrix = image_to_matrix(image)
    blurred_image = arithmetic_mean_filter(image, n, m)
    blurred_image_matrix = image_to_matrix(blurred_image)

    for line in range(image.width):
        for column in range(image.height):
            image.putpixel((line, column), original_image_matrix[line][column] + int(k *
            (original_image_matrix[line][column] - blurred_image_matrix[line][column])))
    return image


def prewitt_border_detection(image: Image, horizontal: bool = True) -> Image:
    if horizontal:
        prewitt_mask = [[-1, -1, -1], [0, 0, 0], [1, 1, 1]]
    else:
        prewitt_mask = [[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]
    prewitt_image = apply_linear_filter(image, prewitt_mask)
    return matrix_to_image(prewitt_image)


def sobel_border_detection(image: Image, horizontal: bool = True):
    if horizontal:
        sobel_mask = [[-1, -2, -1], [0, 0, 0], [1, 2, 1]]
    else:
        sobel_mask = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
    sobel_image = apply_linear_filter(image, sobel_mask)
    return matrix_to_image(sobel_image)