一、简介

在opencv基础上二次封装，包括 translation, rotation, resizing, skeletonization, and displaying Matplotlib images 等。

github地址: https://github.com/jrosebr1/imutils

二、使用

1. 安装

pip install imutils

查询opencv中的函数
可以使用关键词搜索opencv中的相应函数

import imutils
imutils.find_functions("area")
#output:
1. CC_STAT_AREA
2. INTER_AREA
3. contourArea
4. minAreaRect

2. 图像平移Translation

图像在x轴方向左右平移，y轴方向上下平移，

#向右平移25像素，向上平移75像素
translated = imutils.translate(image,25,-75)

3. 图像旋转Rotation

rotated = imutils.rotate(image,90)

4. 图像大小Resizing

改变图像大小，但保持原来图像的长宽比不变。
可以只单独设置width或者height；

resized = imutils.resize(image,width=300)
resized = imutils.resize(image,height=300)

这个函数会根据图片的比例进行重新绘制大小，如果你的图片是200:200的图片比例，那么如果你使用resize函数的时候，resize(img,height=50,width=20) 那么最后修改的图像是已最小的那个数字对齐，也就是width=20，所以最后出来的图片大小是2020，而不是5020，或者50*50.

5. 骨架化Skeletonization

gray = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY)
skeleton = imutils.skeletonize(gray, size=(3, 3))

cv2.imshow("Skeleton", skeleton)

6. 使用Matplotlib展示图片

opencv读取的图片格式为BGR，使用matplotlib显示的时候也是这个顺序，需要先将BGR转换为RGB才能正常显示，imutils提供了转换函数

plt.imshow(imutils.opencv2matplotlib(image))

7. URL转换为Image

image = imutils.url_to_image(url)

8. OpenCV版本

Example:
print("Your OpenCV version: {}".format(cv2.__version__))
print("Are you using OpenCV 2.X? {}".format(imutils.is_cv2()))
print("Are you using OpenCV 3.X? {}".format(imutils.is_cv3()))
输出:
Your OpenCV version: 3.0.0
Are you using OpenCV 2.X? False
Are you using OpenCV 3.X? True

9. 自动边缘检测Automatic Canny Edge Detection

gray = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY)
edgeMap = imutils.auto_canny(gray)
#源码
def auto_canny(image, sigma=0.33):
 # compute the median of the single channel pixel intensities
 v = np.median(image)
 # apply automatic Canny edge detection using the computed median
 lower = int(max(0, (1.0 - sigma) * v))
 upper = int(min(255, (1.0 + sigma) * v))
 edged = cv2.Canny(image, lower, upper)
 # return the edged image
 return edged

10. 透视变换

# author: Adrian Rosebrock
# website: http://www.pyimagesearch.com

# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python perspective_transform.py

# import the necessary packages
from imutils import perspective
import numpy as np
import cv2

# load the notecard code image, clone it, and initialize the 4 points
# that correspond to the 4 corners of the notecard
notecard = cv2.imread("../demo_images/notecard.png")
clone = notecard.copy()
pts = np.array([(73, 239), (356, 117), (475, 265), (187, 443)])

# loop over the points and draw them on the cloned image
for (x, y) in pts:
 cv2.circle(clone, (x, y), 5, (0, 255, 0), -1)

# apply the four point tranform to obtain a "birds eye view" of
# the notecard
warped = perspective.four_point_transform(notecard, pts)

# show the original and warped images
cv2.imshow("Original", clone)
cv2.imshow("Warped", warped)
cv2.waitKey(0)

11. 给cv2.findContours结果排序

# author: Adrian Rosebrock
# website: http://www.pyimagesearch.com

# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python sorting_contours.py

# import the necessary packages
from imutils import contours
import imutils
import cv2

# load the shapes image clone it, convert it to grayscale, and
# detect edges in the image
image = cv2.imread("../demo_images/shapes.png")
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edged = imutils.auto_canny(gray)

# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
 cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

# loop over the (unsorted) contours and label them
for (i, c) in enumerate(cnts):
 orig = contours.label_contour(orig, c, i, color=(240, 0, 159))

# show the original image
cv2.imshow("Original", orig)

# loop over the sorting methods
for method in ("left-to-right", "right-to-left", "top-to-bottom", "bottom-to-top"):
 # sort the contours
 (cnts, boundingBoxes) = contours.sort_contours(cnts, method=method)
 clone = image.copy()

 # loop over the sorted contours and label them
 for (i, c) in enumerate(cnts):
 sortedImage = contours.label_contour(clone, c, i, color=(240, 0, 159))

 # show the sorted contour image
 cv2.imshow(method, sortedImage)

# wait for a keypress
cv2.waitKey(0)

10. 返回图片列表

from imutils import paths
for imagePath in paths.list_images("../demo_images"):
 print imagePath