rgb_data = plt.imread("Data/oldtimer.png")
print("rgb_data.shape:", rgb_data.shape)
_=plt.imshow(rgb_data)

hsv_data = colors.rgb_to_hsv(rgb_data)
print("hsv_data.shape:", hsv_data.shape)
hsv_data2 = np.moveaxis(hsv_data, -1, 0)
print("hsv_data2.shape:", hsv_data2.shape)

f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16,6))
plot1 = ax1.imshow(hsv_data2[0], cmap='hsv', vmin=0, vmax=1)
f.colorbar(plot1, ax=ax1, orientation='horizontal')
_ = ax1.set_title("hue")
plot2 = ax2.imshow(hsv_data2[1], cmap='gray', vmin=0, vmax=1)
f.colorbar(plot2, ax=ax2, orientation='horizontal')
_ = ax2.set_title("saturation")
plot3 = ax3.imshow(hsv_data2[2], cmap='gray', vmin=0, vmax=1)
f.colorbar(plot3, ax=ax3, orientation='horizontal')
_ = ax3.set_title("value")
f.tight_layout()

rgb_img = plt.imread("Data/oldtimer.png")

# ITU-R BT.709 is used for newer high-definition televisions
gray_scale_img = rgb_img[:,:,0] * 0.2125 + rgb_img[:,:,1] * 0.7154 + rgb_img[:,:,2] * 0.0721

# ITU-R 601-2 is used for old analog televisions
#gray_scale_img = rgb_img[:,:,0] * 299/1000 + rgb_img[:,:,1] * 587/1000 + rgb_img[:,:,2] * 114/1000 

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,12))
ax1.imshow(gray_scale_img, cmap="gray", vmin=0, vmax=1)
ax1.set_title("Grayscale Image Converted Using n ITU-R BT.709 luma transform")
ax2.imshow(hsv_data2[2], cmap='gray', vmin=0, vmax=1)
ax2.set_title("Value of the HSV Image")
f.tight_layout()

modified_hsv = hsv_data.copy()
modified_hsv[:,:,1] *= 0.5
reduced_saturation_img = colors.hsv_to_rgb(modified_hsv)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,12))
_ = ax1.imshow(rgb_data)
_ = ax1.set_title("Original Image")
_ = plt.imshow(reduced_saturation_img)
_ = ax2.set_title("Reduced Saturation Image")
f.tight_layout()

# source: https://www.photoshopsupply.com/patterns-textures/free-dust-textures
# license: https://www.photoshopsupply.com/license
# This file is free for Personal and Commercial use with attribution.
!wget https://www.photoshopsupply.com/wp-content/uploads/2019/07/film-texture-1.jpg -O film-texture-1.jpg

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,12))

# Load film texture
film_texture = Image.open("film-texture-1.jpg")
film_texture = film_texture.resize((rgb_data.shape[1], rgb_data.shape[0])) # resize the image as same as the car image
film_texture = np.array(film_texture)/255.0 # normalize between 0 and 1

# brown hsv code: https://en.wikipedia.org/wiki/Brown
film_texture_hsv = colors.rgb_to_hsv(film_texture)
film_texture_hsv[:,:,0] = 30/360
film_texture_hsv[:,:,1] = 0.6  # saturation is modified!
film_texture_hsv[:,:,2] *= 0.59
film_texture_colorized = colors.hsv_to_rgb(film_texture_hsv)
_=plt.imshow(film_texture_colorized)

ax1.imshow(film_texture)
ax1.set_title("Loaded film texture")
ax2.imshow(film_texture_colorized)
ax2.set_title("Colorized film texture")

f.tight_layout()

# https://en.wikipedia.org/wiki/Blend_modes#Overlay
# a is base layer, b is top layer
def overlay(a,b):
    mask = a < 0.5
    c = a.copy()
    c[mask] = 2 * a[mask] * b[mask]
    c[~mask] = 1 - 2*(1-a[~mask]) * (1-b[~mask])
    return c

# Create only brown image
brown_hsv = film_texture_hsv.copy()
brown_hsv[:,:,0] = 30/360
brown_hsv[:,:,1] = 0.3  # saturation is modified!
brown_hsv[:,:,2] = 0.59
brown_rgb = colors.hsv_to_rgb(brown_hsv)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,12))

new_image1 = overlay(film_texture_colorized, reduced_saturation_img)
ax1.imshow(new_image1)
ax1.set_title("Car image overlayed with a brown colored film texture")

new_image2 = overlay(brown_rgb, reduced_saturation_img)
ax2.imshow(new_image2)
ax2.set_title("Car image blended with brown")

f.tight_layout()

rotate_for_blue = 0.6
img = colors.rgb_to_hsv(rgb_data)
modified_hsv = img.copy()
modified_hsv[:,:,0] += rotate_for_blue
modified_hsv[:,:,0] %= 1.0
new_rgb_image = colors.hsv_to_rgb(modified_hsv)
plt.imshow(new_rgb_image)
_ = plt.title("rotated hue: " + str(rotate_for_blue) + " - Blue")

from matplotlib.animation import FuncAnimation
from IPython.display import HTML

fig, ax = plt.subplots(figsize=(9,6))

def update(frame):
    img = colors.rgb_to_hsv(rgb_data)
    modified_hsv = img.copy()
    modified_hsv[:,:,0] += frame
    modified_hsv[:,:,0] = modified_hsv[:,:,0] % 1.0
    new_rgb_image = colors.hsv_to_rgb(modified_hsv)
    plt.imshow(new_rgb_image)
    plt.title("rotated hue: " + str(round(frame,2)))

ani = FuncAnimation(fig, update, frames=np.linspace(0, 1, 40), blit=False)
video = ani.to_html5_video()
plt.close()
HTML(video)

img = cv2.imread("Data/colored_flower.png")
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,12))
_ = ax1.imshow(img); _ = ax1.set_title("RGB image")
_ = ax2.imshow(hsv_img); _ = ax2.set_title("HSV image")

#hsv_img = np.array(hsv_img)
h = hsv_img[:,:,0]
s = hsv_img[:,:,1]
v = hsv_img[:,:,2]

#_ = plt.imshow(img); _ = plt.title("original image")

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(25,25))
_ = ax1.imshow(h); _ = ax1.set_title("hue channel")
_ = ax2.imshow(s, cmap="gray"); _ = ax2.set_title("saturation channel")
_ = ax3.imshow(v, cmap="gray"); _ = ax3.set_title("value channel")

mask = h>120
modified_hsv_img = hsv_img.copy()
modified_hsv_img[:,:,0][mask] = hsv_img[:,:,0][mask] + 60
modified_img = cv2.cvtColor(modified_hsv_img, cv2.COLOR_HSV2RGB)
fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(15,15))
_ = ax1.imshow(img); _ = ax1.set_title("original image")
_ = ax2.imshow(mask, cmap="gray"); _ = ax2.set_title("mask")
_ = ax3.imshow(modified_img); _ = ax3.set_title("yellowish image")

yellowish_hue = np.where(h>120, (h+60)%180, h)
modified_hsv_img = hsv_img.copy()
modified_hsv_img[:,:,0] = yellowish_hue
modified_img = cv2.cvtColor(modified_hsv_img, cv2.COLOR_HSV2RGB)
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,10))
_ = ax1.imshow(img); _ = ax1.set_title("original image")
_ = ax2.imshow(modified_img); _ = ax2.set_title("yellowish image")

img = cv2.imread("Data/reddish_img.jpg")   #opencv uses BGR color as a default color space to display images
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

h = hsv_img[:,:,0]
s = hsv_img[:,:,1]
v = hsv_img[:,:,2]

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(25,25))
_ = ax1.imshow(h); _ = ax1.set_title("hue channel")
_ = ax2.imshow(s, cmap="gray"); _ = ax2.set_title("saturation channel")
_ = ax3.imshow(v, cmap="gray"); _ = ax3.set_title("value channel")

mask = ((h>165) | (h<5)) & (s>80) & (v>80)  # Red color parameters
mask = mask.astype(np.uint8)*255

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,12))
_ = ax1.imshow(img); _ = ax1.set_title("original image")
_ = ax2.imshow(mask, cmap="gray"); _ = ax2.set_title("mask")