RGB 到 grayscale
from skimage.color import rgb2gray
grayscale = rgb2gray(original)
"""
================
RGB to grayscale
================
This example converts an image with RGB channels into an image with a single
grayscale channel.
The value of each grayscale pixel is calculated as the weighted sum of the
corresponding red, green and blue pixels as::
Y = 0.2125 R + 0.7154 G + 0.0721 B
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.color import rgb2gray
original = data.astronaut()
grayscale = rgb2gray(original)
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()
ax[0].imshow(original)
ax[0].set_title("Original")
ax[1].imshow(grayscale, cmap=plt.cm.gray)
ax[1].set_title("Grayscale")
fig.tight_layout()
plt.show()
RGB 到 HSV
from skimage.color import rgb2hsv
hsv_img = rgb2hsv(rgb_img)
實驗：將杯子從背景中簡單分離
"""
==========
RGB to HSV
==========
This example illustrates how RGB to HSV (Hue, Saturation, Value) conversion
can be used to facilitate segmentation processes.
Usually, objects in images have distinct colors (hues) and luminosities, so
that these features can be used to separate different areas of the image.
In the RGB representation the hue and the luminosity are expressed as a linear
combination of the R,G,B channels, whereas they correspond to single channels
of the HSV image (the Hue and the Value channels). A simple segmentation of the
image can then be effectively performed by a mere thresholding of the HSV
channels.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.color import rgb2hsv
##############################################################################
# We first load the RGB image and extract the Hue and Value channels:
rgb_img = data.coffee()
hsv_img = rgb2hsv(rgb_img)
hue_img = hsv_img[:, :, 0]
value_img = hsv_img[:, :, 2]
fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 2))
ax0.imshow(rgb_img)
ax0.set_title("RGB image")
ax0.axis('off')
ax1.imshow(hue_img, cmap='hsv')
ax1.set_title("Hue channel")
ax1.axis('off')
ax2.imshow(value_img)
ax2.set_title("Value channel")
ax2.axis('off')
fig.tight_layout()
##############################################################################
# We then set a threshold on the Hue channel to separate the cup from the
# background:
hue_threshold = 0.04
binary_img = hue_img > hue_threshold
# print(hue_img) # 圖像數值矩陣
# print(binary_img) # True or False 的一個矩陣
fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 3))
# 參數2：bins
ax0.hist(hue_img.ravel(), 512)
ax0.set_title("Histogram of the Hue channel with threshold")
# 設置1條垂直於x軸的紅色的虛線
ax0.axvline(x=hue_threshold, color='r', linestyle='dashed', linewidth=2)
# 設置x軸范圍
ax0.set_xbound(0, 0.12)
ax1.imshow(binary_img)
ax1.set_title("Hue-thresholded image")
ax1.axis('off')
fig.tight_layout()
##############################################################################
# We finally perform an additional thresholding on the Value channel to partly
# remove the shadow of the cup:
fig, ax0 = plt.subplots(figsize=(4, 3))
value_threshold = 0.10
binary_img = (hue_img > hue_threshold) | (value_img < value_threshold)
ax0.imshow(binary_img)
ax0.set_title("Hue and value thresholded image")
ax0.axis('off')
fig.tight_layout()
plt.show()
