try to extract tiger from the scene
grabcut mask is generated
tiger is extracted
#main.py
import numpy as np
import argparse
import time
import cv2
import os
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str,
default=os.path.sep.join(["images", "adrian.jpg"]),
help="path to input image that we'll apply GrabCut to")
ap.add_argument("-c", "--iter", type=int, default=10,
help="# of GrabCut iterations (larger value => slower runtime)")
args = vars(ap.parse_args())
# load the input image from disk and then allocate memory for the
# output mask generated by GrabCut -- this mask should hae the same
# spatial dimensions as the input image
image = cv2.imread(args["image"])
xl, yl, xr, yr = (0, 0, 0, 0)
btn_down = False
img = image.copy()
def drag_event(event, x, y, flags, param):
global btn_down, xl, yl, xr, yr, img
if event == cv2.EVENT_LBUTTONUP and btn_down:
btn_down = False
cv2.rectangle(img, (xl - 2, yl - 2), (xr + 2, yr + 2), (0, 0, 255), 2)
cv2.imshow('original', img)
grab_cut((xl, yl, xr, yr))
elif event == cv2.EVENT_MOUSEMOVE and btn_down:
xr, yr = (x, y)
cv2.rectangle(img, (xl - 2, yl - 2), (xr + 2, yr + 2), (0, 0, 255), 2)
cv2.imshow('original', img)
img = image.copy()
elif event == cv2.EVENT_LBUTTONDOWN:
btn_down = True
xl, yl = (x, y)
def grab_cut(rect):
mask = np.zeros(image.shape[:2], dtype="uint8")
# allocate memory for two arrays that the GrabCut algorithm internally
# uses when segmenting the foreground from the background
fgModel = np.zeros((1, 65), dtype="float")
bgModel = np.zeros((1, 65), dtype="float")
# apply GrabCut using the the bounding box segmentation method
start = time.time()
(mask, bgModel, fgModel) = cv2.grabCut(image, mask, rect, bgModel,
fgModel, iterCount=args["iter"], mode=cv2.GC_INIT_WITH_RECT)
end = time.time()
print("[INFO] applying GrabCut took {:.2f} seconds".format(end - start))
# the output mask has for possible output values, marking each pixel
# in the mask as (1) definite background, (2) definite foreground,
# (3) probable background, and (4) probable foreground
values = (
("Definite Background", cv2.GC_BGD),
("Probable Background", cv2.GC_PR_BGD),
("Definite Foreground", cv2.GC_FGD),
("Probable Foreground", cv2.GC_PR_FGD),
)
# loop over the possible GrabCut mask values
for (name, value) in values:
# construct a mask that for the current value
print("[INFO] showing mask for '{}'".format(name))
valueMask = (mask == value).astype("uint8") * 255
# display the mask so we can visualize it
cv2.imshow(name, valueMask)
# we'll set all definite background and probable background pixels
# to 0 while definite foreground and probable foreground pixels are
# set to 1
outputMask = np.where((mask == cv2.GC_BGD) | (mask == cv2.GC_PR_BGD),
0, 1)
# scale the mask from the range [0, 1] to [0, 255]
outputMask = (outputMask * 255).astype("uint8")
# apply a bitwise AND to the image using our mask generated by
# GrabCut to generate our final output image
output = cv2.bitwise_and(image, image, mask=outputMask)
# show the input image followed by the mask and output generated by
# GrabCut and bitwise masking
cv2.imshow("Input", image)
cv2.imshow("GrabCut Mask", outputMask)
cv2.imshow("GrabCut Output", output)
cv2.imshow("original image", image)
cv2.setMouseCallback('original image', drag_event)
cv2.waitKey(0)
--------------------------
#logs
(venv) C:\Users\zchen\PycharmProjects\opencv>python grabcut.py -i assets/tiger.jpg
[INFO] applying GrabCut took 2.90 seconds
[INFO] showing mask for 'Definite Background'
[INFO] showing mask for 'Probable Background'
[INFO] showing mask for 'Definite Foreground'
[INFO] showing mask for 'Probable Foreground'
reference:
mouse drag event
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