本文主要是介绍多尺度图片滑动窗口输出，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
简介
上一篇我们介绍了图片Gaussian pyramid（一、二）图片不压缩的情况下，重新resize到不同大小，这样做的目的是为这一节做准备，即利用滑动窗口圈住图片的文字信息内容等，例如车牌的获取。
'''
Created on 2017年8月19日@author: XT
'''
# import the necessary packages
import helpers
import argparse
import time
import cv2# load the image and define the window width and height
image = cv2.imread('./image/cat.jpg')
(winW, winH) = (200, 128)# loop over the image pyramid
for resized in helpers.pyramid(image, scale=1.5):# loop over the sliding window for each layer of the pyramidfor (x, y, window) in helpers.sliding_window(resized, stepSize=32, windowSize=(winW, winH)):# if the window does not meet our desired window size, ignore itif window.shape[0] != winH or window.shape[1] != winW:continue# THIS IS WHERE YOU WOULD PROCESS YOUR WINDOW, SUCH AS APPLYING A# MACHINE LEARNING CLASSIFIER TO CLASSIFY THE CONTENTS OF THE# WINDOW# since we do not have a classifier, we'll just draw the windowclone = resized.copy()cv2.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2)cv2.imshow("Window", clone)cv2.waitKey(1)
# time.sleep(0.025)helpers:
'''
Created on 2017年8月19日@author: XuTing
'''
# import the necessary packages
import imutils
from skimage.transform import pyramid_gaussian
import cv2def pyramid(image, scale=1.5, minSize=(30, 30)):# yield the original imageyield image# keep looping over the pyramidwhile True:# compute the new dimensions of the image and resize itw = int(image.shape[1] / scale)image = imutils.resize(image, width=w)# if the resized image does not meet the supplied minimum# size, then stop constructing the pyramidif image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:break# yield the next image in the pyramidyield imagedef sliding_window(image, stepSize, windowSize):# slide a window across the imagefor y in range(0, image.shape[0], stepSize):for x in range(0, image.shape[1], stepSize):# yield the current windowyield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])if __name__ == '__main__':image = cv2.imread('./image/cat2.jpg') # METHOD #2: Resizing + Gaussian smoothing.for (i, resized) in enumerate(pyramid_gaussian(image, downscale=2)):# if the image is too small, break from the loopif resized.shape[0] < 30 or resized.shape[1] < 30:break# show the resized imageWinName = "Layer {}".format(i + 1)cv2.imshow(WinName, resized)cv2.waitKey(10)resized = resized*255cv2.imwrite('./'+WinName+'.jpg',resized)
效果
参考
【1】Sliding Windows for Object Detection with Python and OpenCV - PyImageSearch
http://www.pyimagesearch.com/2015/03/23/sliding-windows-for-object-detection-with-python-and-opencv/?replytocom=322532
【2】My imutils package: A series of OpenCV convenience functions - PyImageSearch
http://www.pyimagesearch.com/2015/02/02/just-open-sourced-personal-imutils-package-series-opencv-convenience-functions/
【3】《SVM物体分类和定位检测》 - Hans的成长记录 - CSDN博客
http://blog.csdn.net/renhanchi/article/category/7007663
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