In [1]:

    

import time
import cv2
from imutils.convenience import resize


    

In [2]:

    

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 = 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 xrange(0, image.shape[0], stepSize):
		for x in xrange(0, image.shape[1], stepSize):
			# yield the current window
			yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])


    

In [3]:

    

# load the image and define the window width and height
image = cv2.imread("./data/hr.jpeg")
(winW, winH) = (128, 128)

# loop over the image pyramid
for resized in pyramid(image, scale=1.5):
	# loop over the sliding window for each layer of the pyramid
	for (x, y, window) in 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)