opencv3 深度估计与分割

GrabCut进行前景检测：

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('statue_small.jpg')
mask = np.zeros(img.shape[:2],np.uint8)

bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)

rect = (100,1,421,378)
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.subplot(121), plt.imshow(img)
plt.title("grabcut"), plt.xticks([]), plt.yticks([])
plt.subplot(122)
plt.imshow(cv2.cvtColor(cv2.imread('statue_small.jpg'), cv2.COLOR_BGR2RGB))
plt.title("original"), plt.xticks([]), plt.yticks([])
plt.show()

输出：

2：使用分水岭算法进行图像分割

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('1.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)

# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)

# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)

# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)

# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]

plt.imshow(img)
plt.show()

输出：

3：使用普通摄像头进行深度估计

import numpy as np
import cv2

l_camera = cv2.VideoCapture(0)
r_camera = cv2.VideoCapture(1)


# create windows
cv2.namedWindow('left_Webcam', cv2.WINDOW_NORMAL)
cv2.namedWindow('right_Webcam', cv2.WINDOW_NORMAL)
cv2.namedWindow('disparity', cv2.WINDOW_NORMAL)

blockSize = 40

while(cv2.waitKey(1) & 0xFF != ord('q')):
    ret1, left_frame = l_camera.read()
    ret2, right_frame = r_camera.read()
    
    # our operations on the frame come here
    gray_left = cv2.cvtColor(left_frame, cv2.COLOR_BGR2GRAY)
    gray_right = cv2.cvtColor(right_frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('left_Webcam', gray_left)
    cv2.imshow('right_Webcam', gray_right)
    stereo = cv2.StereoSGBM_create(minDisparity=1,
        numDisparities=16,
        blockSize=15,
        #uniquenessRatio = 10,
        speckleWindowSize = 10,
        speckleRange = 32,
        disp12MaxDiff = 1,
        P1 = 8*3*blockSize**2,
        P2 = 32*3*blockSize**2)
    disparity = stereo.compute(gray_left, gray_right)
    disparity = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
    cv2.imshow('disparity', disparity)
# When everything done, release the capture
camera.release()
cv2.destroyAllWindows()

输出：因为电脑上没有两个摄像头就没有进行试验

opencv3 深度估计与分割

猜你喜欢