def grab_cut(self, raw_image):
number_of_interation = 5
channel_mask = np.zeros((raw_image.shape[0], raw_image.shape[1]),
np.uint8)
region_of_interest = (50, 50, 450, 290)
background_model = np.zeros((1, 65), np.float64)
foreground_model = np.zeros((1, 65), np.float64)
mode_of_operation = cv2.GC_INIT_WITH_RECT
cv2.grabCut(raw_image, channel_mask, region_of_interest,
background_model, foreground_model, number_of_interation,
mode_of_operation)
normalization_mask = np.where(
(channel_mask == 2) | (channel_mask == 0), 0, 1).astype('uint8')
removed_background_image = raw_image * (normalization_mask[:, :,
np.newaxis])
return removed_background_image
def redraw_grab_cut(self, gc_img):
bgdmodel = np.zeros((1, 65), np.float64)
fgdmodel = np.zeros((1, 65), np.float64)
self.mask, _, __ = cv2.grabCut(gc_img, self.mask, self.rect, bgdmodel,
fgdmodel, 5, cv2.GC_INIT_WITH_MASK)
gp_plot = np.where((self.mask == 0) | (self.mask == 2), 0,
255).astype(np.uint8)
self.score = dice_coef(self.label, gp_plot / 255)
print(f'[DSC]: score f {self.score}')
cv2.imshow('grab-cut', gp_plot)
def cut_background(img_src):
"""
@return x, y, w, h
"""
img = img_src.copy()
h, w = img.shape[:2]
# 背景填充 进行泛洪填充
mask = np.zeros((h + 2, w + 2),
np.uint8) #掩码长和宽都比输入图像多两个像素点，满水填充不会超出掩码的非零边缘
cv2.floodFill(img, mask, (5, 5), (255, 255, 255), (3, 3, 3), (5, 5, 5), 8)
# 去噪
img = cv2.fastNlMeansDenoisingColored(img, None, 20, 20, 7, 21)
# 背景识别
mask = np.zeros((img.shape[:2]), np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (5, 5, w - 5, h - 5)
# 多次计算，保证计算准确度
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 1, cv2.GC_INIT_WITH_RECT)
#关于where函数第一个参数是条件，满足条件的话赋值为0，否则是1。如果只有第一个参数的话返回满足条件元素的坐标。
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
# 绘制轮廓
draw_img = img * mask2[:, :, np.newaxis]
# 前景图使用白色填充，方便识别
draw_img[np.where((draw_img > [0, 0, 0]).all(axis=2))] = [255, 255, 255]
# h, w = draw_img.shape[:2]
draw_img = cv2.cvtColor(draw_img, cv2.COLOR_BGR2GRAY) #得到灰度图
_, binary = cv2.threshold(draw_img, 200, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contours, _ = cv2.findContours(binary, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) < 1:
return img
else:
max_countor = sorted(contours, key=cv2.contourArea, reverse=True)[0]
return cv2.boundingRect(max_countor)
def cut_license(afterimg, rect):
'''
图像分割函数
'''
# 转换为宽度和高度
rect[2] = rect[2] - rect[0]
rect[3] = rect[3] - rect[1]
rect_copy = tuple(rect.copy())
rect = [0, 0, 0, 0]
# 创建掩膜
mask = np.zeros(afterimg.shape[:2], np.uint8)
# 创建背景模型 大小只能为13*5，行数只能为1，单通道浮点型
bgdModel = np.zeros((1, 65), np.float64)
# 创建前景模型
fgdModel = np.zeros((1, 65), np.float64)
# 分割图像
cv2.grabCut(afterimg, mask, rect_copy, bgdModel, fgdModel, 5,
cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img_show = afterimg * mask2[:, :, np.newaxis]
return img_show
def grab_cut():
img = cv2.imread('3.jpg')
mask = np.zeros(img.shape[:2], np.uint8)
bgModel = np.zeros((1, 65), np.float64)
fgModel = np.zeros((1, 65), np.float64)
rect = (420, 1, 676, 881)
cv2.grabCut(img, mask, rect, bgModel, fgModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img = img * mask2[:, :, np.newaxis]
plt.subplot(121)
plt.title('Grabcut')
plt.xticks([]), plt.yticks([])
plt.imshow(img)
plt.subplot(122)
plt.imshow(cv2.cvtColor(cv2.imread('3.jpg'), cv2.COLOR_BGR2RGB))
plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.show()
from cv2 import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('image/front.jpg')
ori = cv.cvtColor(img, cv.COLOR_BGR2RGB)
x_, y_ = 99, 34
x__, y__ = 405, 311
mask = np.zeros(img.shape[:2], np.uint8)
# 前后景模型
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (x_, y_, x__, y__)
# 获取第一个mask
mask, bgdModel, fgdModel = cv.grabCut(img, mask, rect, bgdModel, fgdModel, 5,
cv.GC_INIT_WITH_RECT)
plt.subplot(141), plt.imshow(mask), plt.xticks([]), plt.yticks([])
# 一开始以为交互程序是要自己画矩形
# drawing = False
# cv.namedWindow('win')
# def draw(event, x, y, flags, param):
# global x_, y_, drawing, rect, x__, y__
# if event == cv.EVENT_LBUTTONDOWN and drawing == False:
# cv.circle(img, (x, y), 3, (0, 0, 255), 4)
# x_, y_ = x, y
# drawing = True
# elif event == cv.EVENT_LBUTTONDOWN and drawing == True:
# cv.rectangle(img, (x_, y_), (x, y), (0, 0, 255), 4)
cv2.imshow('Result', result)
cv2.imshow('Foreground', fgMask)
#get position of trackbar each time its moved
kernel_val = cv2.getTrackbarPos('Blur', 'Masking')
if kernel_val == 0:
kernel_val = 1
if img1.drawingLineDone == True or img1.drawingRectDone == True or img1.blurSet == True:
#perform grabcut to get mask depending on rect or line mask
if img1.drawingRectDone == True:
rect = [img1.x1, img1.y1, img1.x2, img1.y2]
img1.mask, _, _ = cv2.grabCut(img1.main,
img1.mask,
rect,
bgdModel,
fgdModel,
5,
mode=cv2.GC_INIT_WITH_RECT)
elif img1.drawingLineDone == True:
#create a mask with drawn outline and merge with rectangular mask
img1.mask, _, _ = cv2.grabCut(img1.main, img1.mask, None, bgdModel,
fgdModel, 5, cv2.GC_INIT_WITH_MASK)
#extract fg and bg from mask
fgMask = np.where(
(img1.mask == cv2.GC_BGD) | (img1.mask == cv2.GC_PR_BGD), 0,
1).astype('uint8')
bgMask = np.where(
(img1.mask == cv2.GC_FGD) | (img1.mask == cv2.GC_PR_FGD), 0,
1).astype('uint8')
# 검은색 영역은 배경이라고 확실하게 가정한 사각형의 바깥쪽 영역이며, 회색 영역은 그랩컷이 배경이라고 생각하는 영역, 흰색 영역은 전경
image_bgr = cv2.imread('../img/black.jpg')
image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) # RGB로 변환
rectangle = (0, 56, 256, 150) # 사각형 좌표: 시작점의 x, 시작점의 y, 너비, 높이
mask = np.zeros(image_rgb.shape[:2], np.uint8) # 초기 마스크를 만듭니다.
bgdModel = np.zeros((1, 65), np.float64) # grabCut에 사용할 임시 배열을 만듭니다.
fgdModel = np.zeros((1, 65), np.float64)
# 그랩컷(grabCut) 실행
cv2.grabCut(
image_rgb, # 원본 이미지
mask, # 마스크
rectangle, # 사각형
bgdModel, # 배경을 위한 임시 배열
fgdModel, # 전경을 위한 임시 배열
5, # 반복 횟수
cv2.GC_INIT_WITH_RECT) # 사각형을 사용한 초기화
# 배경인 곳은 0, 그외에는 1로 설정한 마스크를 만듭니다.
mask_2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
# 이미지에 새로운 마스크를 곱해 배경을 제외합니다.
image_rgb_nobg = image_rgb * mask_2[:, :, np.newaxis]
plt.imshow(image_rgb_nobg), plt.axis("off") # 이미지 출력
plt.show()
plt.imshow(mask, cmap='gray'), plt.axis("off") # 마스크 출력
plt.show()
from cv2 import cv2
import numpy as np
from PIL import Image
in_path = r'D:\pythonVscode\people.jpg'
out_path = r'D:\pythonVscode\photo_new.jpg'
img = cv2.imread(in_path)
rect = (0, 0, img.shape[0], img.shape[1])
mask = np.zeros(img.shape[:2], np.uint8)
bgModel = np.zeros((1,65), np.float64)
fgModel = np.zeros((1,65), np.float64)
cv2.grabCut(img, mask, rect, bgModel, fgModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype(np.uint8)
out = img * mask2[:, :, np.newaxis]
#out += 255 * (1 - cv2.cvtColor(mask2, cv2.COLOR_GRAY2BGR))
cv2.imshow('output', out)
cv2.waitKey()
cv2.imwrite(out_path, out)
cv_img = []
for img in glob.glob(
"/home/juan-rios/Documentos/python/trackMove/transparente/*.png"):
n = cv2.imread(img)
cv_img.append(n)
#images = [cv2.imread(file) for file in glob.glob('/Users/jior/Desktop/iglue/trasparente/*.png')]
background = Image.open(
"/home/juan-rios/Documentos/python/trackMove/original/background.png")
foreground = Image.open(img)
size = 128, 128
background.paste(foreground, (0, 0))
background.show()
final()
'''img = cv2.imread('C:\\Users\\USUARIO\\Desktop\\iglue\\foto.png')
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (50,50,450,290)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]
plt.imshow(img),plt.colorbar(),plt.show()
# newmask es la máscara etiquetada manualmente
newmask = cv2.imread('C:\\Users\\USUARIO\\Desktop\\iglue\\foto.png',0)
# donde sea que esté marcado en blanco (primer plano seguro), cambiar mask=1
# donde sea que esté marcado en negro (fondo seguro), cambiar mask=0
mask[newmask == 0] = 0
mask[newmask == 255] = 1
from cv2 import cv2 as cv
from matplotlib import pyplot as plt
import numpy as np
img = cv.imread('image/front.jpg')
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (50, 50, 450, 290)
# 函数的返回值是更新的 mask, bgdModel, fgdModel
cv.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img = img * mask2[:, :, np.newaxis]
plt.imshow(img), plt.colorbar(), plt.show()
continue
orig = image.copy()
bods = entry.bodies
# only working now for single rectangle
if len(bods) == 1:
bd = bods[0]
# do first iterations of grabcut
bgdmodel = numpy.zeros((1, 65), numpy.float64)
fgdmodel = numpy.zeros((1, 65), numpy.float64)
rect = get_rect(bd)
mask = numpy.zeros(image.shape[:2],
dtype=numpy.uint8) # mask initialized to BG
cv2.grabCut(image, mask, rect, bgdmodel, fgdmodel, 5,
cv2.GC_INIT_WITH_RECT)
# second iterations of grabcut - sometimes it doesn't work
try:
cv2.grabCut(orig, mask, rect, bgdmodel, fgdmodel, 5,
cv2.GC_INIT_WITH_MASK)
except cv2.error as err:
print(err)
# make images with alpha channel
b_channel, g_channel, r_channel = cv2.split(orig)
# if probably background (2) or background (0), set to trasparent (0)
# otherwise make opaque (255)
a_channel = numpy.where((mask == 2) | (mask == 0), 0,
255).astype('uint8')
foreground = cv2.merge((b_channel, g_channel, r_channel, a_channel))
import numpy as np
from cv2 import cv2
from matplotlib import pyplot as plt
img = cv2.imread('../image/FrontGround.jpg')
img = cv2.resize(img, (800, 800))
orgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
mask = np.zeros(img.shape[:2], np.uint8) # 制作黑色遮罩
bgd = np.zeros((1, 65), np.float64)
fgd = np.zeros((1, 65), np.float64)
rect = (70, 94, 631, 660)
mask3 = cv2.grabCut(img, mask, rect, bgd, fgd, 5,
cv2.GC_INIT_WITH_RECT)[0] # 缺少会导致边缘有缺损，矩阵模式
# mask6 = np.where((mask==2)|(mask==0),0,1).astype('uint8') # 如果图像中像素为2或0，则将其改0，否则改为1，
# img = img*mask6[:,:,np.newaxis]
# cv2.imwrite('../image/newmask.jpg',img)
plt.subplot(221)
plt.imshow(mask3)
plt.axis('off')
mask2 = cv2.imread('../image/newmask.jpg', 0)
mask2 = cv2.resize(mask2, (800, 800))
# mask2Show = cv2.imread('../image/newmask.jpg',-1)
# mask2Show = cv2.resize(mask2Show,(800,800))
# m2rgb=cv2.cvtColor(mask2Show,cv2.COLOR_BGR2RGB)
mask[mask2 == 0] = 0 # 颜色映射 ，人工遮罩后的图片中被遮罩的部分 映射到 原图上，对原图进行遮罩
mask[mask2 == 255] = 1 # 这里使用自定义模板
plt.subplot(222)
plt.imshow(mask3)
cv2.setMouseCallback('image', mouse_Callback)
while True:
cv2.imshow('image', show_img)
k = cv2.waitKey(1)
if k == ord('a') and not mouse_pressed:
if w * h > 0:
break
cv2.destroyAllWindows()
labels = np.full(img.shape[:2], cv2.GC_INIT_WITH_MASK, np.uint8)
bgdModel = np.full(img.shape[:2], cv2.GC_PR_BGD, np.float64)
fgdModel = np.full(img.shape[:2], cv2.GC_PR_FGD, np.float64)
labels, bgdModel, fgdModel = cv2.grabCut(img, labels, (x, y, w, h), None, None,
1, cv2.GC_INIT_WITH_RECT)
show_img = np.copy(img)
show_img[(labels == cv2.GC_PR_BGD) | (labels == cv2.GC_BGD)] //= 3
cv2.imshow('image', show_img)
cv2.waitKey()
cv2.destroyAllWindows()
label = cv2.GC_BGD
lbl_clrs = {cv2.GC_BGD: (0, 0, 0), cv2.GC_FGD: (255, 255, 255)}
def mouse_callback(event, x, y, flags, param):
global mouse_pressed
if event == cv2.EVENT_LBUTTONDOWN:
