ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help='Path to input images')
ap.add_argument('-W', '--width', type=int, help='width of sliding window')
ap.add_argument('-H', '--height', type=int, help='height of sliding window')
ap.add_argument('-s', '--scale', type=float, default=1.5, help='Scale factor')
args = ap.parse_args()
image = cv2.imread(args.image)
(winW, winH) = (args.width, args.height)
# window counter
win_number = 0
# loop over the image pyramid
for layer in pyramid(image, scale=args.scale):
# loop over the sliding windows
for (x, y, window) in sliding_window(layer,
stepSize=10,
windowSize=(winW, winH)):
# ignore truncated window at boundary
if window.shape[0] != winH or window.shape[1] != winW:
continue
clone = layer.copy()
cv2.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2)
cv2.imshow("Window", clone)
win_number += 1
cv2.waitKey(1)
time.sleep(0.025)
args = vars(ap.parse_args())
# initialize some useful constants
WIDTH = 600
PYR_SCALE = 1.5
WIN_STEP = 16
ROI_SIZE = eval(args["size"])
INPUT_SIZE = (224, 224)
# read the input image and resize it to the appropriate size
orig = cv2.imread(args["image"])
orig = imutils.resize(orig, width=WIDTH)
(H, W) = orig.shape[:2]
# initialize the image pyramid
pyr = pyramid(orig, scale=PYR_SCALE, min_size=ROI_SIZE)
# initialize lists to hold the ROIs and their locations
rois = []
locs = []
# time how long it takes to generate the rois
start = time.time()
# loop through the pyramids and slide a window through the images
for img in pyr:
# calculate the scale (to upsample bbox locations)
scale = W / float(img.shape[1])
# slide the window through the current img
for (x, y, orig_roi) in sliding_window(img, WIN_STEP, ROI_SIZE):
# USAGE
# python test_pyramid.py --image mai-ngoc.jpg --scale 1.5
# import the necessary packages
from object_detection.helpers import pyramid
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 input image")
ap.add_argument("-s", "--scale", type=float, default=1.5, help="scale factor size")
args = vars(ap.parse_args())
# load the input image
image = cv2.imread(args["image"])
# loop over the layers of the image pyramid and display them
for (i, layer) in enumerate(pyramid(image, scale=args["scale"])):
cv2.imshow("Layer {}".format(i + 1), layer)
cv2.waitKey(0)
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to the input image")
ap.add_argument("-w", "--width", required=True, type=int, help="width of sliding window")
ap.add_argument("-t", "--height", required=True, type=int, help="height of sliding window")
ap.add_argument("-s", "--scale", type=float, default=1.5, help="scale factor size")
args = vars(ap.parse_args())
# load the input image and unpack the command line arguments
image = cv2.imread(args["image"])
(winW, winH) = (args["width"], args["height"])
# loop over the image pyramid
for layer in pyramid(image, scale=args["scale"]):
# loop over the sliding window for each layer of the pyramid
for (x, y, window) in sliding_window(layer, stepSize=32, windowSize=(winW, winH)):
# if the current window does not meed our desired window size, ignore it
if window.shape[0] != winH or window.shape[1] != winW:
continue
# THIS IS WHERE WE WOULD PROCESS THE WINDOW, EXTRACT HOG FEATURES, AND
# APPLY A MACHINE LEARNING CLASSIFIER TO PERFORM OBJECT DETECTION
# since we do not have a classifier yet, let's just draw the window
clone = layer.copy()
cv2.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2)
cv2.imshow("Window", clone)
# normally we would leave out this line, but let's pause execution
from object_detection.helpers import pyramid
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help='Path to input images')
ap.add_argument('-s', '--scale', type=float, default=1.5, help='Scale factor')
args = ap.parse_args()
image = cv2.imread(args.image)
# display the image pyramid
for (i, layer) in enumerate(pyramid(image, scale=args.scale)):
cv2.imshow("Layer {}".format(i + 1), layer)
cv2.waitKey(0)