def update():
colors = ((1.0, 1.0, 1.0, 1.0))
# out = cv2.imread('car_depth.png')
# out = out.astype(float)
# out = out/16
# colors = cv2.imread('car.png')
# colors = colors.astype(float)
# colors = colors/255
# ret, frame = cap.read()
#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
retL, left_img = capL.read()
retR, right_img = capR.read()
# left_img = frame[:,0:(width/2)]
# right_img = frame[:,(width/2):width]
disparity_img1 = df.disparity_with_filter(left_img, right_img, 32, 5)
disparity_img2, max_val = df.disparity_without_filter(left_img, right_img, 64, 9)
disparity_img1 = cv2.normalize(src=disparity_img1, dst=disparity_img1, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
disparity_img1 = np.uint8(disparity_img1)
depthMapImg = cv2.reprojectImageTo3D(disparity_img1, Q)
colors = left_img.astype(float)
colors = colors/255
sp2.setData(pos=np.array(depthMapImg, dtype=np.float64), color=colors, size=2)
def update():
colors = ((1.0, 1.0, 1.0, 1.0))
# retL, imgL = capL.read()
# retR, imgR = capR.read()
nameL = sys.argv[1]
nameR = sys.argv[2]
imgL = cv2.imread(nameL)
imgR = cv2.imread(nameR)
left_img = cv2.remap(imgL, left_maps[0], left_maps[1], cv2.INTER_LINEAR)
right_img = cv2.remap(imgR, right_maps[0], right_maps[1], cv2.INTER_LINEAR)
left_img_gray = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY)
right_img_gray = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY)
disparity_img = df.disparity_with_filter(left_img_gray, right_img_gray, 160, 5)
disparity_img = disparity_img/16
disparity_img[disparity_img < 50] = 50
depth_img = ((724*20)/disparity_img) * math.cos(math.atan2(26,37))
xyz = np.copy(xyz_init)
xyz[:,:,0] = np.multiply(xyz[:,:,0], depth_img)
xyz[:,:,0] = xyz[:,:,0] / 724
xyz[:,:,1] = np.multiply(xyz[:,:,1], depth_img)
xyz[:,:,1] = xyz[:,:,1] / 724
xyz[:,:,2] = depth_img
colors = left_img.astype(float)
colors = colors/255
temp_clr = colors[:,:,0]
colors[:,:,0] = colors[:,:,2]
colors[:,:,2] = temp_clr
sp2.setData(pos=np.array(xyz, dtype=np.float64), color=colors, size=2)
import numpy as np
import cv2
import sys
from matplotlib import pyplot as plt
import scipy.io as sio
Q = np.float32([[1, 0, 0, -3.77753695e+02], [0, 1, 0, -2.06903945e+02],
[0, 0, 0, 6.74158207e+02], [0, 0, 4.97033865e-02, 0]])
left_img = cv2.imread(sys.argv[1])
right_img = cv2.imread(sys.argv[2])
#print sys.argv[0]
#print left_img.shape
disparity_img = df.disparity_with_filter(left_img, right_img, 160, 5)
#disparity_img2, max_val = df.disparity_without_filter(left_img, right_img, 32, 9)
disparity_img1 = np.copy(disparity_img)
disparity_img1 = disparity_img1 / 16
disparity_img = cv2.normalize(src=disparity_img,
dst=disparity_img,
beta=0,
alpha=255,
norm_type=cv2.NORM_MINMAX)
disparity_img = np.uint8(disparity_img)
print np.amax(disparity_img)
print np.amin(disparity_img)
print np.amax(disparity_img1)
print np.amin(disparity_img1)
import disparity_functions as df
import numpy as np
import cv2
import sys
Q = np.float32([[1, 0, 0, 4.95838611e+02],
[0, 1, 0, 2.24740481e+01],
[0, 0, 0, 4.23789559e+00],
[0, 0, 4.66947240e-03, 0]])
left_img = cv2.imread('resources/left_image/left_img6.png')
right_img = cv2.imread('resources/right_image/right_img6.png')
disparity_img1 = df.disparity_with_filter(left_img, right_img, 32, 5)
disparity_img2, max_val = df.disparity_without_filter(left_img, right_img, 32, 9)
disparity_img1 = cv2.normalize(src=disparity_img1, dst=disparity_img1, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
disparity_img1 = np.uint8(disparity_img1)
disparity_img2 = cv2.normalize(src=disparity_img2, dst=disparity_img2, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
disparity_img2 = np.uint8(disparity_img2)
cv2.imshow('with filter', disparity_img1)
cv2.imshow('without filter', disparity_img2)
cv2.waitKey(0)