10.5

'''
Učitati sliku lena_color.png i prevesti je u HSV sistem boja (engl. Hue-Saturation-Value).

1. Povećati vrednost H komponente za 0.33, tri puta za redom. Nakon svake promene prikazati sliku.
2. Prikazati slike nastale modifikacijom S komponente za faktor u intervalu od -0.6 do 0.6 sa korakom 0.2.
3. Ekvalizovati histogram V komponente. Prikazati sliku nakon promene.
'''

import plotly.express as px
from skimage import io

img = io.imread('lena_color.png')
fig = px.imshow(img)
fig.show()

from skimage import color

img_hsv = color.rgb2hsv(img)

fig = px.imshow(img_hsv[:,:,0], color_continuous_scale='gray')
fig.show()

fig = px.imshow(img_hsv[:,:,1], color_continuous_scale='gray')
fig.show()

fig = px.imshow(img_hsv[:,:,2], color_continuous_scale='gray')
fig.show()

img_rgb = color.hsv2rgb(img_hsv)

fig = px.imshow(img_rgb)
fig.show()

'''
Povećati vrednost H komponente za  1/3 , tri puta za redom.
Nakon svake promene prikazati sliku.
'''
import plotly.graph_objects as go
import numpy as np

# Create figure
fig = go.Figure()

increment = 1/3

# Add traces, one for each slider step
for i in range(3):
    img_hsv[:,:,0] = (img_hsv[:,:,0] + increment) % 1
    out = color.hsv2rgb(img_hsv)

    # Izlazna slika je 'float' slika i radi prikaza u colab-u pretvoricemo u uint8 slike
    out = (255*out).astype('uint8')

    fig.add_trace(go.Image(z=out, name="faktor = "+str((i+1)*increment), visible=false))

    # Make the 1st visible
    active_idx = 0
    fig.data[active_idx].visible = True

    # Create and add slider
    steps = []
    for i in range(len(fig.data)):
        step = dict(method="update",
                    args=[{"visible": [False] * len(fig.data)},
                          {"title": "Faktor : " + str((i+1)*increment)}])
        step["args"][0]["visible"][i] = True # Toggle i'th trace to "visible"
        steps.append(step)

    sliders = [dict(active=active_idx, steps=steps)]

    fig.update_layout(sliders = sliders)
    fig.show()

# Create figure
fig = go.Figure()

s_factor = [-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6]

# Add traces, one for each slider step
for i in s_factor:
    img_hsv = color.rgb2hsv(img)

    img_hsv[:, :, 1] = np.maximum(np.minimum(img_hsv[:, :, 1] + i, 1), 0)
    out = color.hsv2rgb(img_hsv)

    # izlazna slika je 'float' slika i radi prikaza u colab-u pretvoricemo u uint8 slike
    out = (255 * out).astype('uint8')

    fig.add_trace(go.Image(z=out, name="faktor = " + str(i), visible=False))

# Make the center visible
active_idx = 3
fig.data[active_idx].visible = True

# Create and add slider
steps = []
for i in range(len(fig.data)):
    step = dict(method="update",
                args=[{"visible": [False] * len(fig.data)},
                      {"title": "Faktor : " + str(s_factor[i])}])

    step["args"][0]["visible"][i] = True  # Toggle i'th trace to "visible"
    steps.append(step)

sliders = [dict(active=active_idx, steps=steps)]

fig.update_layout(sliders=sliders)
fig.show()

from skimage import exposure

img_hsv = color.rgb2hsv(img)
img_hsv[:,:,2] = exposure.equalize_hist(img_hsv[:,:,2])

out = color.hsv2rgb(img_hsv)
fig = px.imshow(out)
fig.show()