颜色转换，利用HSV颜色空间检测

import numpy as np
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
# Read in the image
image = cv2.imread('images/water_balloons.jpg')
# Change color to RGB (from BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)

绘制颜色通道

#RGB channels
R = image[:, :, 0]
G = image[:, ;, 1]
B = image[:, :, 2]

f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10)) #生成一个一行三列的f图像.

ax1.set_title('Red')
ax1.imshow(r, cmap='gray')

ax2.set_title('Green')
ax2.imshow(g, cmap='gray')

ax3.set_title('Blue')
ax3.imshow(b, cmap='gray')

绘制出这些通道的灰度版本 以便观察各通道的强度,像素越亮 代表的红色、绿色或蓝色值就越高。我们可以看到 粉色气球的红色值很高 蓝色值也相对比较高，但值大小不一 特别是当气球位于阴影下的时候。

# Convert from RGB to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)

# HSV channels
h = hsv[:,:,0]
s = hsv[:,:,1]
v = hsv[:,:,2]

f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))

ax1.set_title('Hue')
ax1.imshow(h, cmap='gray')

ax2.set_title('Saturation')
ax2.imshow(s, cmap='gray')

ax3.set_title('Value')
ax3.imshow(v, cmap='gray')

设置色彩空间选择阈值

# Define our color selection criteria in HSV values
lower_hue = np.array([160,0,0]) 
upper_hue = np.array([180,255,255])

# Define our color selection criteria in RGB values
lower_pink = np.array([180,0,100]) 
upper_pink = np.array([255,255,230])

创建掩模图像

# Define the masked area in RGB space
mask_rgb = cv2.inRange(image, lower_pink, upper_pink)

# mask the image
masked_image = np.copy(image)
masked_image[mask_rgb==0] = [0,0,0]

# Vizualize the mask
plt.imshow(masked_image)

Now try HSV!

# Define the masked area in HSV space
mask_hsv = cv2.inRange(hsv, lower_hue, upper_hue)

# mask the image
masked_image = np.copy(image)
masked_image[mask_hsv==0] = [0,0,0]

# Vizualize the mask
plt.imshow(masked_image)

看看这个选择结果 我们会发现几乎所有粉色气球都被选中了,由此可见 HSV 空间更擅长选泽处于不同光线下的区域