METHOD #1: No smooth, just scaling.

def pyramid(image, scale=1.5, minSize=(30, 30)):

    # yield the original image

    yield image

    # keep looping over the pyramid

    while True:

        # compute the new dimensions of the image and resize it

        w = 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 pyramid

        if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:

            break

        # yield the next image in the pyramid

        yield image

METHOD #2: Resizing + Gaussian smoothing.

# import the necessary packages

import helpers

from skimage.transform import pyramid_gaussian

import argparse

import cv2

# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-i", '--image', required=True, help="Path to the image")

ap.add_argument("-s", "--scale", type=float, default=1.5, help="scale factor size")

args = vars(ap.parse_args())

# load the image

image = cv2.imread(args["image"])

# METHOD #1: No smooth, just scaling.

# loop over the image pyramid

for (i, resized) in enumerate(helpers.pyramid(image, scale=args["scale"])):

    # show the resized image

    cv2.imshow("Layer {}".format(i + 1), resized)

    cv2.waitKey(0)

# close all windows

cv2.destroyAllWindows()

# METHOD #2: Resizing + Gaussian smoothing.

for (i, resized) in enumerate(pyramid_gaussian(image, downscale=2)):

    # if the image is too small, break from the loop

    if resized.shape[0] < 30 or resized.shape[1] < 30:

        break

    # show the resized image

    cv2.imshow("Layer {}".format(i + 1), resized)

    cv2.waitKey(0)

#Run cmd python pyramid.py --image image/cat.jpg --scale 1.5

参考

【1】Image Pyramids with python and OpenCV - PyImageSearch
http://www.pyimagesearch.com/2015/03/16/image-pyramids-with-python-and-opencv/
【2】jrosebr1/imutils: A series of convenience functions to make basic
image processing operations such as translation, rotation, resizing,
skeletonization, and displaying Matplotlib images easier with opencv and
Python.
https://github.com/jrosebr1/imutils
【3】Histogram of Oriented Gradients and Object Detection - PyImageSearch
http://www.pyimagesearch.com/2014/11/10/histogram-oriented-gradients-object-detection/
【4】Module: transform — skimage v0.14dev docs
http://scikit-image.org/docs/dev/api/skimage.transform.html#pyramid-gaussian

上边我们介绍了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。

# 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 pyramid

    for (x, y, window) in helpers.sliding_window(resized, stepSize=32, windowSize=(winW, winH)):

        # if the window does not meet our desired window size, ignore it

        if 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 window

        clone = 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 cv2

def pyramid(image, scale=1.5, minSize=(30, 30)):

    # yield the original image

    yield image

    # keep looping over the pyramid

    while True:

        # compute the new dimensions of the image and resize it

        w = 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 pyramid

        if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:

            break

        # yield the next image in the pyramid

        yield image

def sliding_window(image, stepSize, windowSize):

    # slide a window across the image

    for y in range(0, image.shape[0], stepSize):

        for x in range(0, image.shape[1], stepSize):

            # yield the current window

            yield (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 loop

        if resized.shape[0] < 30 or resized.shape[1] < 30:

            break

        # show the resized image

        WinName = "Layer {}".format(i + 1)

        cv2.imshow(WinName, resized)

        cv2.waitKey(10)

        resized = resized*255

        cv2.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

巴特西

python 图片滑动窗口

METHOD #1: No smooth, just scaling.

METHOD #2: Resizing + Gaussian smoothing.

效果

参考

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