Image analysis

gistfile1.py

# -*- coding: utf-8 -*-

import copy
from copy import deepcopy
from functools import reduce

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('.\Meduse_degrade.jpg',cv2.IMREAD_GRAYSCALE)
img_minmax = copy.deepcopy(img)
img_egalization = copy.deepcopy(img)
img_inversion = copy.deepcopy(img)


# ─── TRAITEMENT MINMAX ──────────────────────────────────────────────────────────
min_value = np.min(img_minmax)
max_value = np.max(img_minmax)
for row_index, row in enumerate(img_minmax):
    for pixel_index, pixel in enumerate(row):
        img_minmax[row_index][pixel_index] = (255 / (max_value - min_value)) * (pixel - min_value)

# ─── TRAITEMENT ÉGALISATION ─────────────────────────────────────────────────────

by_intensity = [np.count_nonzero(img == intensity) for intensity in range(0,255)]
cumulated = []
for index, intensity_count in enumerate(list(by_intensity)):
    if index == 0:
        cumulated.append(intensity_count)
    else:
        cumulated.append(intensity_count + cumulated[-1])

total_pixel = img_egalization.size

for row_index, row in enumerate(img_egalization):
    for pixel_index, pixel in enumerate(row):
        img_egalization[row_index][pixel_index] = 254 / total_pixel * cumulated[pixel]


# ─── TRAITEMENT INVERSION ───────────────────────────────────────────────────────

for row_index, row in enumerate(img_inversion):
    for pixel_index, pixel in enumerate(row):
        img_inversion[row_index][pixel_index] = 255 - pixel

#-----------------------------
# Histogramme de l'image
plt.subplot(421),plt.imshow(img, cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(422),plt.hist(img.ravel(), bins=256)
plt.title('Histogram, min: {} max:{}'.format(np.min(img), np.max(img))), plt.yticks([])
plt.subplot(423),plt.imshow(img_minmax, cmap = 'gray')
plt.title('MinMax Image'), plt.xticks([]), plt.yticks([])
plt.subplot(424),plt.hist(img_minmax.ravel(), bins=256)
plt.title('Histogram, min: {} max:{}'.format(np.min(img_minmax), np.max(img_minmax))), plt.xticks([]), plt.yticks([])
plt.subplot(425),plt.imshow(img_egalization, cmap = 'gray')
plt.title('Egalisataion d\'histogramme'), plt.xticks([]), plt.yticks([])
plt.subplot(426),plt.hist(img_egalization.ravel(), bins=256)
plt.title('Histogram, min: {} max:{}'.format(np.min(img_egalization), np.max(img_egalization))), plt.xticks([]), plt.yticks([])
plt.subplot(427),plt.imshow(img_inversion, cmap = 'gray')
plt.title('Inversion d\'histogramme'), plt.xticks([]), plt.yticks([])
plt.subplot(428),plt.hist(img_inversion.ravel(), bins=256)
plt.title('Histogram, min: {} max:{}'.format(np.min(img_inversion), np.max(img_inversion))), plt.xticks([]), plt.yticks([])
plt.show()