Image_processing_basics/basic_treat.py at main · dldou/Image_processing_basics · 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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 24 14:37:22 2020

@author: dldou
"""

import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np

#Function to import image
def image_importation(file_path):
    """
        Based on OpenCV
    """
    image = cv.imread(file_path)
    #Arrenging the order
    image = cv.cvtColor(image, cv.COLOR_BGR2RGB)

    return image


def image_show(image1, image2,
               suptitle,
               gray=False,
               subtitle1="Image", subtitle2="Processed image",
               file_path=""):
    """
        Displaying function to compare image before and after processing
    """

    fig, axes = plt.subplots(1,2)

    fig.suptitle(suptitle)

    #Image before
    axes[0].imshow(image1)
    axes[0].set_title(subtitle1 + " $({}x{})$".format(image1.shape[0], image1.shape[1]))
    axes[0].set_axis_off()

    #Image after
    if gray:
        axes[1].imshow(image2, cmap='gray')
    else:
        axes[1].imshow(image2)

    axes[1].set_title(subtitle2 + " $({}x{})$".format(image2.shape[0], image2.shape[1]))
    axes[1].set_axis_off()

    #Save
    plt.savefig(file_path + suptitle + ".pdf")

    #Show
    plt.show()


def red_extract(image):

    #Copying the image to avoid direct modification
    res = np.copy(image)

    #Set other params to zero
    res[:,:,1] = 0
    res[:,:,2] = 0

    return res

def neg(image):

    res = np.copy(image)

    #negatif: pix_value_res = 255 - pix_value
    res[:,:,:] = 255 - res[:,:,:]

    return res


def to_grey(image):

    res = np.zeros((image.shape[0], image.shape[0]))

    #pix_value_res = 0.299*pix_value_red + 0.587∗pix_value_green + 0.114∗pix_value_blue
    res = 0.229*image[:,:,0] + 0.587*image[:,:,1] + 0.114*image[:,:,2]

    return res


def sepia(image, image_red_coeff, image_green_coeff, image_blue_coeff):

    res = np.copy(image)

    #Sepia
    #pix_value_res_red   = 0.393*pix_value_red + 0.769∗pix_value_green + 0.189∗pix_value_blue
    #pix_value_res_green = 0.349*pix_value_red + 0.686∗pix_value_green + 0.168∗pix_value_blue
    #pix_value_res_blue  = 0.272*pix_value_red + 0.534∗pix_value_green + 0.131∗pix_value_blue

    for k in range(3):

        res[:,:,k] = np.where(image_red_coeff[k]*image[:,:,0] + image_green_coeff[k]*image[:,:,1] + image_blue_coeff[k]*image[:,:,2] < 255,
                              image_red_coeff[k]*image[:,:,0] + image_green_coeff[k]*image[:,:,1] + image_blue_coeff[k]*image[:,:,2],
                              255
                             )

    return res


def contrast(image):

    res = np.copy(image)

    res = np.where(res<30, 0, (255.0/195.0)*(res-30)+0.5)
    res = np.where(res>225, 255, (255.0/195.0)*(res-30)+0.5)
    res = res.astype(int)

    return res


def grey_threshold(grey_image, threshold):

    res = np.copy(grey_image)

    res[res<=threshold] = 0
    res[res>threshold]  = 255

    return res


def color_threshold(image, threshold):

    res = np.copy(image)

    res = np.where(res<=threshold,
                   0,
                   res)

    res = np.where(res>threshold,
                   255,
                   res)

    return res


def flip(image):

    res = np.copy(image)

    return res[::-1]