Uno dei problemi maggiori dell'uso dei tag aruco in esterno per misurare distanze e' che le condizioni di illuminazione solare sono molto variabili e la compressione jpg completa l'opera aggiungendo rumore su rumore
Per cercare di limitare il problema ho provato a fare image stacking sovrapponendo 8 foto (una ogni ora dalle 09 alle 16) con lo script al link https://github.com/maitek/image_stacking ed applicando il programma visto qui
Il miglioramento non e' trascurabile perche' la standard deviation e' passata
3.1 m => da 0.19% a 0.16% (0.5 cm)
5.4 m => da 0.35% a 0.28% (1.5 cm)
7.6 m => da 0.71% a 0.38% (2.8 cm)
9.6 m => da 0.5% a 0.41% (4.8 cm)
import os
import cv2
import numpy as np
from time import time
# Align and stack images with ECC method
# Slower but more accurate
def stackImagesECC(file_list):
M = np.eye(3, 3, dtype=np.float32)
first_image = None
stacked_image = None
for file in file_list:
image = cv2.imread(file,1).astype(np.float32) / 255
print(file)
if first_image is None:
# convert to gray scale floating point image
first_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
stacked_image = image
else:
# Estimate perspective transform
s, M = cv2.findTransformECC(cv2.cvtColor(image,cv2.COLOR_BGR2GRAY), first_image, M, cv2.MOTION_HOMOGRAPHY)
w, h, _ = image.shape
# Align image to first image
image = cv2.warpPerspective(image, M, (h, w))
stacked_image += image
stacked_image /= len(file_list)
stacked_image = (stacked_image*255).astype(np.uint8)
return stacked_image
# Align and stack images by matching ORB keypoints
# Faster but less accurate
def stackImagesKeypointMatching(file_list):
orb = cv2.ORB_create()
# disable OpenCL to because of bug in ORB in OpenCV 3.1
cv2.ocl.setUseOpenCL(False)
stacked_image = None
first_image = None
first_kp = None
first_des = None
for file in file_list:
print(file)
image = cv2.imread(file,1)
imageF = image.astype(np.float32) / 255
# compute the descriptors with ORB
kp = orb.detect(image, None)
kp, des = orb.compute(image, kp)
# create BFMatcher object
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
if first_image is None:
# Save keypoints for first image
stacked_image = imageF
first_image = image
first_kp = kp
first_des = des
else:
# Find matches and sort them in the order of their distance
matches = matcher.match(first_des, des)
matches = sorted(matches, key=lambda x: x.distance)
src_pts = np.float32(
[first_kp[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32(
[kp[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
# Estimate perspective transformation
M, mask = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC, 5.0)
w, h, _ = imageF.shape
imageF = cv2.warpPerspective(imageF, M, (h, w))
stacked_image += imageF
stacked_image /= len(file_list)
stacked_image = (stacked_image*255).astype(np.uint8)
return stacked_image
# ===== MAIN =====
# Read all files in directory
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('input_dir', help='Input directory of images ()')
parser.add_argument('output_image', help='Output image name')
parser.add_argument('--method', help='Stacking method ORB (faster) or ECC (more precise)')
parser.add_argument('--show', help='Show result image',action='store_true')
args = parser.parse_args()
image_folder = args.input_dir
if not os.path.exists(image_folder):
print("ERROR {} not found!".format(image_folder))
exit()
file_list = os.listdir(image_folder)
file_list = [os.path.join(image_folder, x)
for x in file_list if x.endswith(('.jpg', '.png','.bmp'))]
if args.method is not None:
method = str(args.method)
else:
method = 'KP'
tic = time()
if method == 'ECC':
# Stack images using ECC method
description = "Stacking images using ECC method"
print(description)
stacked_image = stackImagesECC(file_list)
elif method == 'ORB':
#Stack images using ORB keypoint method
description = "Stacking images using ORB method"
print(description)
stacked_image = stackImagesKeypointMatching(file_list)
else:
print("ERROR: method {} not found!".format(method))
exit()
print("Stacked {0} in {1} seconds".format(len(file_list), (time()-tic) ))
print("Saved {}".format(args.output_image))
cv2.imwrite(str(args.output_image),stacked_image)
# Show image
if args.show:
cv2.imshow(description, stacked_image)
cv2.waitKey(0)
