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stereo940.py
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stereo940.py
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import numpy as np
import cv2
import argparse
import sys
from calibration_store import load_stereo_coefficients
import os
def depth_map(imgL, imgR):
""" Depth map calculation. Works with SGBM and WLS. Need rectified images, returns depth map ( left to right disparity ) """
# SGBM Parameters -----------------
window_size = 5 # wsize default 3; 5; 7 for SGBM reduced size image; 15 for SGBM full size image (1300px and above); 5 Works nicely
left_matcher = cv2.StereoSGBM_create(
minDisparity=0,
numDisparities=48, # max_disp has to be dividable by 16 f. E. HH 192, 256
blockSize=window_size,
P1=8 * 1 * window_size ** 2,
# wsize default 3; 5; 7 for SGBM reduced size image; 15 for SGBM full size image (1300px and above); 5 Works nicely
P2=32 * 1 * window_size ** 2,
disp12MaxDiff=1,
uniquenessRatio=15,
speckleWindowSize=0,
speckleRange=2,
preFilterCap=63,
mode=cv2.STEREO_SGBM_MODE_SGBM_3WAY
)
right_matcher = cv2.ximgproc.createRightMatcher(left_matcher)
# FILTER Parameters
lmbda = 10000
sigma = 1.2
visual_multiplier = 1.0
wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=left_matcher)
wls_filter.setLambda(lmbda)
wls_filter.setSigmaColor(sigma)
displ = left_matcher.compute(imgL, imgR) # .astype(np.float32)/16
dispr = right_matcher.compute(imgR, imgL) # .astype(np.float32)/16
displ = np.int16(displ)
dispr = np.int16(dispr)
#displ_ori_show = np.int8(displ)
#cv2.imshow('odis',displ_ori_show )
disp_temp = cv2.normalize(displ, displ, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
cv2.imshow('displ', disp_temp)
filteredImg = wls_filter.filter(displ, imgL, None, dispr) # important to put "imgL" here!!!
filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX)
filteredImg = np.uint8(filteredImg)
return filteredImg
if __name__ == '__main__':
# Args handling -> check help parameters to understand
parser = argparse.ArgumentParser(description='Camera calibration')
parser.add_argument('--calibration_file', type=str, required=True, help='Path to the stereo calibration file')
parser.add_argument('--test_dir', type=str, required=True)
parser.add_argument('--im_h', type=int, default=1280)
parser.add_argument('--im_w', type=int, default=720)
parser.add_argument('--format', type=str, default='.jpg')
args = parser.parse_args()
base = args.test_dir#'image_transform'
height = args.im_h
width = args.im_w
l_save_rectify_path = os.path.join(base, 'l_rectify')
r_save_rectify_path = os.path.join(base, 'r_rectify')
import os
if not os.path.exists(l_save_rectify_path):
os.makedirs(l_save_rectify_path)
if not os.path.exists(r_save_rectify_path):
os.makedirs(r_save_rectify_path)
K1, D1, K2, D2, R, T, E, F, R1, R2, P1, P2, Q = load_stereo_coefficients(args.calibration_file) # Get cams params
print('K1, D1, K2, D2, R, T, E, F, R1, R2, P1, P2, Q:', K1, D1, K2, D2, R, T, E, F, R1, R2, P1, P2, Q)
line_num = 10
line_x0 = [0 for i in range(height // line_num)]
line_y = [i for i in range(0, height, height // line_num)]
line_x1 = [width * 2 - 1 for i in range(height // line_num)]
start = tuple(zip(line_x0, line_y))
end = tuple(zip(line_x1, line_y))
left_src_dir = os.path.join(base, 'image0')
right_src_dir = os.path.join(base, 'image1')
left_lst = os.listdir(left_src_dir)
right_lst = os.listdir(right_src_dir)
left_lst = sorted(left_lst)
right_lst = sorted(right_lst)
for index_local in range(len(left_lst)):
if not left_lst[index_local].endswith(args.format):
continue
print('left:', os.path.join(left_src_dir, left_lst[index_local]))
leftFrame = cv2.imread(os.path.join(left_src_dir, left_lst[index_local]), 0)
leftFrame = cv2.resize(leftFrame, (width, height))
print('right:', os.path.join(left_src_dir, left_lst[index_local]))
rightFrame = cv2.imread(os.path.join(right_src_dir, right_lst[index_local]), 0)
rightFrame = cv2.resize(rightFrame, (width, height))
#height, width = leftFrame.shape # We will use the shape for remap
# Undistortion and Rectification part!
leftMapX, leftMapY = cv2.initUndistortRectifyMap(K1, D1, R1, P1, (width, height), cv2.CV_32FC1)
left_rectified = cv2.remap(leftFrame, leftMapX, leftMapY, cv2.INTER_NEAREST, borderMode=cv2.BORDER_REPLICATE)
rightMapX, rightMapY = cv2.initUndistortRectifyMap(K2, D2, R2, P2, (width, height), cv2.CV_32FC1)
right_rectified = cv2.remap(rightFrame, rightMapX, rightMapY, cv2.INTER_NEAREST, borderMode=cv2.BORDER_REPLICATE)
merge = np.hstack((left_rectified, right_rectified))
#print('fdsafsdafsdA:')
for index in range(len(start)):
s = start[index]
e = end[index]
show_remap = cv2.line(merge, s, e, (0), thickness=1)
cv2.imshow('remap', cv2.resize(show_remap, (720, 640)))
disparity_image = depth_map(left_rectified, right_rectified) # Get the disparity map
# Show the images
cv2.imshow('left', cv2.resize(leftFrame, (360, 640)))
cv2.imshow('right', cv2.resize(rightFrame, (360, 640)))
cv2.imshow('Disparity', cv2.resize(disparity_image, (360, 640)))
cv2.waitKey(0)
cv2.destroyAllWindows()