https://github.com/gpbonillas/lab_image_enhancement

Mejora de detalles en imágenes
https://github.com/gpbonillas/lab_image_enhancement

colab colab-notebook jupyter-notebook python3 skimage

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Mejora de detalles en imágenes

• Host: GitHub
• URL: https://github.com/gpbonillas/lab_image_enhancement
• Owner: gpbonillas
• License: mit
• Created: 2024-06-03T02:42:06.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-03T03:50:17.000Z (7 months ago)
• Last Synced: 2024-06-04T04:31:29.724Z (7 months ago)
• Topics: colab, colab-notebook, jupyter-notebook, python3, skimage
• Language: Jupyter Notebook
• Homepage:
• Size: 28.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Laboratorio para mejorar imágenes usando skimage: Operaciones elementales.

Para este ejercicio hemos usado las imagenes 33, 77, 333 y 777 del dataset [The Dark Face](https://www.kaggle.com/datasets/soumikrakshit/dark-face-dataset). 

## Tecnología usadas: 

- Jupyter Notebook

- Python

- skimage

## Funciones aplicadas: 

Principalmente, nos centraremos en las siguientes técnicas:

- Funciones de transformación o ajuste de la intensidad.

- Procesamiento del histograma.

- Operadores aritméticos.

``` python

import matplotlib

import matplotlib.pyplot as plt

import numpy as np

from skimage import io, img_as_float, img_as_ubyte

from skimage import exposure

from skimage import util

from skimage import filters

from skimage.filters.rank import median

from skimage.filters.rank import mean

from skimage.color import rgb2gray

from skimage.color import rgb2yuv

from skimage.morphology import disk, ball

from skimage.exposure import is_low_contrast

matplotlib.rcParams['font.size'] = 8

```

``` python

def plot_img_and_hist(image, axes, bins=256):

    """Plot an image along with its histogram and cumulative histogram.

    """

    image = img_as_float(image)

    ax_img, ax_hist = axes

    ax_cdf = ax_hist.twinx()

    # Display image

    ax_img.imshow(image, cmap=plt.cm.gray)

    ax_img.set_axis_off()

    # Display histogram

    ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')

    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))

    ax_hist.set_xlabel('Pixel intensity')

    ax_hist.set_xlim(0, 1)

    ax_hist.set_yticks([])

    # Display cumulative distribution

    img_cdf, bins = exposure.cumulative_distribution(image, bins)

    ax_cdf.plot(bins, img_cdf, 'r')

    ax_cdf.set_yticks([])

    return ax_img, ax_hist, ax_cdf

```

``` python

# Load an example image

img33 = img_as_float(io.imread("33.png"))

img333 = img_as_float(io.imread("333.png"))

img77 = img_as_float(io.imread("77.png"))

img777 = img_as_float(io.imread("777.png"))

#image2 = io.imread("images/test_image.jpg").astype(np.float)

#avoid using astype as it violates assumptions about dtype range.

#for example float should range from 0 to 1 (or -1 to 1) but if you use

#astype to convert to float, the values do not lie between 0 and 1.

plt.imshow(img33)

```

![](/images/1.png)

``` python

img33.shape

```

> (720, 1080, 3)

# FUNCIONES DE TRANSFORMACIÓN O AJUSTE DE LA INTENSIDAD

``` python

# Gamma (0.10)

gamma_corrected_01 = exposure.adjust_gamma(img33, 0.10)

# Gamma (0.20)

gamma_corrected_02 = exposure.adjust_gamma(img33, 0.20)

# Gamma (0.30)

gamma_corrected_03 = exposure.adjust_gamma(img33, 0.30)

# Gamma (0.40)

gamma_corrected_04 = exposure.adjust_gamma(img33, 0.40)

# Gamma (0.50)

gamma_corrected_05 = exposure.adjust_gamma(img33, 0.50)

# Display results

fig = plt.figure(figsize=(18, 6))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 7))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected_01, axes[:, 1])

ax_img.set_title('Gamma  k=0.10')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected_02, axes[:, 2])

ax_img.set_title('Gamma  k=0.20')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected_03, axes[:, 3])

ax_img.set_title('Gamma  k=0.30')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected_04, axes[:, 4])

ax_img.set_title('Gamma  k=0.40')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected_05, axes[:, 5])

ax_img.set_title('Gamma  k=0.50')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 7))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/2.png)

``` python

# Logarithmic (k=3)

logarithmic_corrected_3 = exposure.adjust_log(img33, 3)

# Logarithmic (k=10)

logarithmic_corrected_10 = exposure.adjust_log(img33, 10)

# Logarithmic (k=20)

logarithmic_corrected_20 = exposure.adjust_log(img33, 20)

# Logarithmic (k=25)

logarithmic_corrected_25 = exposure.adjust_log(img33, 25)

# Logarithmic (k=40)

logarithmic_corrected_40 = exposure.adjust_log(img33, 40)

# Display results

fig = plt.figure(figsize=(18, 6))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 7))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected_3, axes[:, 1])

ax_img.set_title('Logarithmic k=3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected_10, axes[:, 2])

ax_img.set_title('Logarithmic k=10')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected_20, axes[:, 3])

ax_img.set_title('Logarithmic k=20')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected_25, axes[:, 4])

ax_img.set_title('Logarithmic k=25')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected_40, axes[:, 5])

ax_img.set_title('Logarithmic k=40')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 7))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/3.png)

### **Los mejores resultados para las funciones Gamma y Log son:**

``` python

# Gamma

gamma_corrected = exposure.adjust_gamma(img33, 0.3)

# Logarithmic

logarithmic_corrected = exposure.adjust_log(img33, 25)

# Display results

fig = plt.figure(figsize=(12, 6))

axes = np.zeros((2, 3), dtype=object)

axes[0, 0] = plt.subplot(2, 3, 1)

axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0])

axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0])

axes[1, 0] = plt.subplot(2, 3, 4)

axes[1, 1] = plt.subplot(2, 3, 5)

axes[1, 2] = plt.subplot(2, 3, 6)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1])

ax_img.set_title('Gamma correction (k=0.3)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2])

ax_img.set_title('Logarithmic correction (k=25)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/4.png)

# PROCESAMIENTO DEL HISTOGRAMA

``` python

# Contrast stretching

p2, p98 = np.percentile(img33, (2, 98))

img_rescale2_98 = exposure.rescale_intensity(img33, in_range=(p2, p98))

# Contrast stretching

p5, p95 = np.percentile(img33, (5, 95))

img_rescale5_95 = exposure.rescale_intensity(img33, in_range=(p5, p95))

# Contrast stretching

p10, p90 = np.percentile(img33, (10, 90))

img_rescale10_90 = exposure.rescale_intensity(img33, in_range=(p10, p90))

# Display results

fig = plt.figure(figsize=(15, 8))

axes = np.zeros((2, 4), dtype=object)

axes[0, 0] = fig.add_subplot(2, 4, 1)

for i in range(1, 4):

    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 4):

    axes[1, i] = fig.add_subplot(2, 4, 5+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale2_98, axes[:, 1])

ax_img.set_title('Contrast stretching 2-98')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale5_95, axes[:, 2])

ax_img.set_title('Contrast stretching 5-95')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale10_90, axes[:, 3])

ax_img.set_title('Contrast stretching 10-90')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/5.png)

``` python

# Equalization

img_eq = exposure.equalize_hist(img33)

# Equalization 2-98

img_eq298 = exposure.equalize_hist(img_rescale2_98)

# Equalization 5-95

img_eq595 = exposure.equalize_hist(img_rescale5_95)

# Equalization 10-90

img_eq1090 = exposure.equalize_hist(img_rescale10_90)

# Display results

fig = plt.figure(figsize=(15, 8))

axes = np.zeros((2, 5), dtype=object)

axes[0, 0] = fig.add_subplot(2, 5, 1)

for i in range(1, 5):

    axes[0, i] = fig.add_subplot(2, 5, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 5):

    axes[1, i] = fig.add_subplot(2, 5, 6+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 1])

ax_img.set_title('Histogram equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq298, axes[:, 2])

ax_img.set_title('Histogram equalization (2-98%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq595, axes[:, 3])

ax_img.set_title('Histogram equalization (5-95%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq1090, axes[:, 4])

ax_img.set_title('Histogram equalization (10-90%)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/6.png)

``` python

# Adaptive Equalization (clip_limit=0.01)

img33_clahe_001 = exposure.equalize_adapthist(img33, clip_limit=0.01)

# Adaptive Equalization (clip_limit=0.03)

img33_clahe_003 = exposure.equalize_adapthist(img33, clip_limit=0.03)

# Adaptive Equalization (clip_limit=0.1)

img33_clahe_01 = exposure.equalize_adapthist(img33, clip_limit=0.1)

# Adaptive Equalization (clip_limit=0.2)

img33_clahe_02 = exposure.equalize_adapthist(img33, clip_limit=0.2)

# Adaptive Equalization (clip_limit=0.5)

img33_clahe_05 = exposure.equalize_adapthist(img33, clip_limit=0.5)

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 7))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_clahe_001, axes[:, 1])

ax_img.set_title('CLAHE Original (clip_limit=0.01)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_clahe_003, axes[:, 2])

ax_img.set_title('CLAHE Original (clip_limit=0.03)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_clahe_01, axes[:, 3])

ax_img.set_title('CLAHE Original (clip_limit=0.1)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_clahe_02, axes[:, 4])

ax_img.set_title('CLAHE Original (clip_limit=0.2)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_clahe_05, axes[:, 5])

ax_img.set_title('CLAHE Original (clip_limit=0.5)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 7))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/7.png)

**Nos damos cuenta que el valor para el parámetro *clip_limit* que mejores

resultados arroja es: 0.2**

``` python

# Adaptive Equalization

img_clahe = exposure.equalize_adapthist(img33, clip_limit=0.2)

# Adaptive Equalization (Rescale 2-98%)

img_clahe_298 = exposure.equalize_adapthist(img_rescale2_98, clip_limit=0.2)

# Adaptive Equalization (Rescale 5-95%)

img_clahe_595 = exposure.equalize_adapthist(img_rescale5_95, clip_limit=0.2)

# Adaptive Equalization (Rescale 10-90%)

img_clahe_1090 = exposure.equalize_adapthist(img_rescale10_90, clip_limit=0.2)

# Display results

fig = plt.figure(figsize=(15, 8))

axes = np.zeros((2, 5), dtype=object)

axes[0, 0] = fig.add_subplot(2, 5, 1)

for i in range(1, 5):

    axes[0, i] = fig.add_subplot(2, 5, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 5):

    axes[1, i] = fig.add_subplot(2, 5, 6+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_clahe, axes[:, 1])

ax_img.set_title('Adaptive equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_clahe_298, axes[:, 2])

ax_img.set_title('Adaptive equalization (Rescale 2-98%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_clahe_595, axes[:, 3])

ax_img.set_title('Adaptive equalization (Rescale 5-95%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_clahe_1090, axes[:, 4])

ax_img.set_title('Adaptive equalization (Rescale 10-99%)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/8.png)

``` python

# Adaptive Equalization

img_adapteq = exposure.equalize_adapthist(img33, clip_limit=0.2)

# Adaptive Equalization (Gamma=0.1)

img_adapteq_gamma_01 = exposure.equalize_adapthist(gamma_corrected_01, clip_limit=0.2)

# Adaptive Equalization (Gamma=0.2)

img_adapteq_gamma_02 = exposure.equalize_adapthist(gamma_corrected_02, clip_limit=0.2)

# Adaptive Equalization (Gamma=0.3)

img_adapteq_gamma_03 = exposure.equalize_adapthist(gamma_corrected_03, clip_limit=0.2)

# Gamma (0.20)

#gamma_corrected_02 = exposure.adjust_gamma(img33, 0.20)

# Display results

fig = plt.figure(figsize=(15, 8))

axes = np.zeros((2, 5), dtype=object)

axes[0, 0] = fig.add_subplot(2, 5, 1)

for i in range(1, 5):

    axes[0, i] = fig.add_subplot(2, 5, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 5):

    axes[1, i] = fig.add_subplot(2, 5, 6+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 1])

ax_img.set_title('Adaptive equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq_gamma_01, axes[:, 2])

ax_img.set_title('Adaptive equalization (Gamma=0.1)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq_gamma_02, axes[:, 3])

ax_img.set_title('Adaptive equalization (Gamma=0.2)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq_gamma_03, axes[:, 4])

ax_img.set_title('Adaptive equalization (Gamma=0.3)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/9.png)

### **MEJORES RESULTADOS CON LAS TÉNICAS DE PROCESAMIENTO DEL HISTOGRAMA**

``` python

# Contrast stretching

p10, p90 = np.percentile(img33, (10, 90))

img_rescale_1090 = exposure.rescale_intensity(img33, in_range=(p10, p90))

# Equalization Original

img33_eq = exposure.equalize_hist(img33)

# Equalization Rescale

img_rescale_eq_1090 = exposure.equalize_hist(img_rescale_1090)

# Adaptive Equalization Original

img_adapteq = exposure.equalize_adapthist(img33, clip_limit=0.2)

# Adaptive Equalization (Gamma=0.3)

img_adapteq_gamma = exposure.equalize_adapthist(gamma_corrected, clip_limit=0.2)

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 7))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale_1090, axes[:, 1])

ax_img.set_title('Contrast stretching (10-90%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_eq, axes[:, 2])

ax_img.set_title('Histogram equalization Original')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale_eq_1090, axes[:, 3])

ax_img.set_title('Histogram equalization (Rescale 10-90%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 4])

ax_img.set_title('CLAHE Original (clip_limit=0.2)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq_gamma, axes[:, 5])

ax_img.set_title('CLAHE (Gamma=0.3)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 7))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/10.png)

# OPERACIONES MATEMÁTICAS

``` python

constante = 0.2

k = 2.5

# SUMA BLOQUE 1

img33_add = img33 + img33

img33_add_gamma03 = gamma_corrected_03 + gamma_corrected_03

img33_add_log25 = logarithmic_corrected_25 + logarithmic_corrected_25

img33_add_rescale1090 = img_rescale10_90 + img_rescale10_90

img33_add_adapte_02 = img_adapteq + img_adapteq # Perfecta

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add, axes[:, 1])

ax_img.set_title('Suma Iguales')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_gamma03, axes[:, 2])

ax_img.set_title('Suma Gamma=0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_log25, axes[:, 3])

ax_img.set_title('Suma Log=25')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_rescale1090, axes[:, 4])

ax_img.set_title('Suma Rescale (10-90%)')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapte_02, axes[:, 5])

ax_img.set_title('Suma Ig. CLAHE Optimizada')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/11.png)

``` python

# SUMA BLOQUE 2

img33_add_eq = img_eq + img_eq

img33_add_adapteq_gamma_03 = img_adapteq_gamma_03 + img_adapteq_gamma_03

img33_add_adapteq298 = img_clahe_298 + img_clahe_298

img33_add_adapteq_gamma03 = img_adapteq + img33_add_gamma03

img33_add_clahe_opt_k = img_adapteq + constante

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_eq, axes[:, 1])

ax_img.set_title('Suma Histogram Equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapteq_gamma_03, axes[:, 2])

ax_img.set_title('Suma CLAHE Gamma=0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapteq298, axes[:, 3])

ax_img.set_title('Suma CLAHE Rescale 2-98%')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapteq_gamma03, axes[:, 4])

ax_img.set_title('Suma CLAHE Optimizada + Gamma 0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_clahe_opt_k, axes[:, 5])

ax_img.set_title('Suma CLAHE Optimizado + k=0.2')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/12.png)

### **MEJORES RESULTADOS CON LAS TÉNICAS DE OPERADORES MATEMÁTICOS (SUMA)**

``` python

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_gamma03, axes[:, 1])

ax_img.set_title('Suma Gamma=0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapte_02, axes[:, 2])

ax_img.set_title('Suma Ig. CLAHE Optimizada')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapteq298, axes[:, 3])

ax_img.set_title('Suma CLAHE Rescale 2-98%')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_adapteq_gamma03, axes[:, 4])

ax_img.set_title('Suma CLAHE Optimizada + Gamma 0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_add_clahe_opt_k, axes[:, 5])

ax_img.set_title('Suma CLAHE Optimizado + k=0.2')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/13.png)

``` python

# MULTIPLICACIÓN

k = 2.5

img33_mul_k = img_adapteq * k

img33_mul_gamma03 = img33_add_gamma03 * img33_add_gamma03

img33_mul_adapteq = img33_add_adapte_02 * img33_add_adapte_02

img33_mul_original_k = img33 * k

img33_mul_add_clahe_opt_k = img33_add_clahe_opt_k * img33_add_clahe_opt_k

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_mul_k, axes[:, 1])

ax_img.set_title('Mult. CLAHE Opt. k=2.5')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_mul_gamma03, axes[:, 2])

ax_img.set_title('Mult. Gamma=0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_mul_adapteq, axes[:, 3])

ax_img.set_title('Mult. Iguales CLAHE Opt.')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_mul_original_k, axes[:, 4])

ax_img.set_title('Mult. Original por k')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img33_mul_add_clahe_opt_k, axes[:, 5])

ax_img.set_title('Mult. Ig. Suma  CLAHE Opt. (k=0.2)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/14.png)

``` python

constante = 0.2

k = 2.5

img333_gamma_corrected = exposure.adjust_gamma(img333, 0.3)

img333_logarithmic_corrected = exposure.adjust_log(img333, 25)

img333_adapteq = exposure.equalize_adapthist(img333, clip_limit=0.2)

img333_add_k = img333_adapteq + constante

img333_mul_add_k = img333_add_k * img333_add_k

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_gamma_corrected, axes[:, 1])

ax_img.set_title('Gamma 0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_logarithmic_corrected, axes[:, 2])

ax_img.set_title('Log 25')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_adapteq, axes[:, 3])

ax_img.set_title('CLAHE Óptimizada')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_add_k, axes[:, 4])

ax_img.set_title('Suma CLAHE Optimizada + k=2.5')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_mul_add_k, axes[:, 5])

ax_img.set_title('Mult. Suma (k=0.2)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/15.png)

``` python

constante = 0.2

k = 2.5

img77_gamma_corrected = exposure.adjust_gamma(img77, 0.3)

img77_logarithmic_corrected = exposure.adjust_log(img77, 25)

img77_adapteq = exposure.equalize_adapthist(img77, clip_limit=0.2)

img77_add_k = img77_adapteq + constante

img77_mul_add_k = img77_add_k * img77_add_k

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_gamma_corrected, axes[:, 1])

ax_img.set_title('Gamma 0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_logarithmic_corrected, axes[:, 2])

ax_img.set_title('Log 25')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_adapteq, axes[:, 3])

ax_img.set_title('CLAHE Óptimizada')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_add_k, axes[:, 4])

ax_img.set_title('Suma CLAHE Optimizada + k=2.5')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_mul_add_k, axes[:, 5])

ax_img.set_title('Mult. Suma (k=0.2)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/16.png)

``` python

constante = 0.2

k = 2.5

img777_gamma_corrected = exposure.adjust_gamma(img777, 0.3)

img777_logarithmic_corrected = exposure.adjust_log(img777, 25)

img777_adapteq = exposure.equalize_adapthist(img777, clip_limit=0.2)

img777_add_k = img777_adapteq + constante

img777_mul_add_k = img777_add_k * img777_add_k

# Display results

fig = plt.figure(figsize=(18, 8))

axes = np.zeros((2, 6), dtype=object)

axes[0, 0] = fig.add_subplot(2, 6, 1)

for i in range(1, 6):

    axes[0, i] = fig.add_subplot(2, 6, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 6):

    axes[1, i] = fig.add_subplot(2, 6, 7+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777, axes[:, 0])

ax_img.set_title('Imagen Original')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Número de pixeles')

ax_hist.set_yticks(np.linspace(0, y_max, 6))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_gamma_corrected, axes[:, 1])

ax_img.set_title('Gamma 0.3')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_logarithmic_corrected, axes[:, 2])

ax_img.set_title('Log 25')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_adapteq, axes[:, 3])

ax_img.set_title('CLAHE Óptimizada')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_add_k, axes[:, 4])

ax_img.set_title('Suma CLAHE Optimizada + k=2.5')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_mul_add_k, axes[:, 5])

ax_img.set_title('Mult. Suma (k=0.2)')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 6))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/17.png)

**Hemos determinado que las imagenes procesadas con la función CLAHE,

produce una imagen de mayor calidad en su nitidez, contraste y

luminosidad**

``` python

# Display results

fig = plt.figure(figsize=(20, 8))

axes = np.zeros((2, 4), dtype=object)

axes[0, 0] = fig.add_subplot(2, 4, 1)

for i in range(1, 4):

    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 4):

    axes[1, i] = fig.add_subplot(2, 4, 5+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 0])

ax_img.set_title('Imagen #33')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img333_adapteq, axes[:, 1])

ax_img.set_title('Imagen #333')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img77_adapteq, axes[:, 2])

ax_img.set_title('Imagen #77')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img777_adapteq, axes[:, 3])

ax_img.set_title('Imagen #777')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

```

![](/images/18.png)