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create_opencv_F_submission_example.py
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create_opencv_F_submission_example.py
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# select the data
import numpy as np
import matplotlib.pyplot as plt
import h5py
import cv2
from utils import *
from tqdm import tqdm
import os
from metrics import *
def create_F_submission_cv2(split = 'val', inlier_th = 1.0, match_th = 0.8):
DIR = split
seqs = os.listdir(DIR)
out_model = {}
inls = {}
for seq in seqs:
matches = load_h5(f'{DIR}/{seq}/matches.h5')
matches_scores = load_h5(f'{DIR}/{seq}/match_conf.h5')
out_model[seq] = {}
inls[seq] = {}
for k, m in tqdm(matches.items()):
ms = matches_scores[k].reshape(-1)
mask = ms <= match_th
tentatives = m[mask]
tentative_idxs = np.arange(len(mask))[mask]
src_pts = tentatives[:,:2]
dst_pts = tentatives[:,2:]
F, mask_inl = cv2.findFundamentalMat(src_pts, dst_pts, cv2.RANSAC,
inlier_th, confidence=0.9999)
out_model[seq][k] = F
final_inliers = np.array([False] * len(mask))
if F is not None:
for i, x in enumerate(mask_inl):
final_inliers[tentative_idxs[i]] = x
inls[seq][k] = final_inliers
return out_model, inls
def evaluate_results(submission, split = 'val'):
ang_errors = {}
DIR = split
seqs = os.listdir(DIR)
for seq in seqs:
matches = load_h5(f'{DIR}/{seq}/matches.h5')
K1_K2 = load_h5(f'{DIR}/{seq}/K1_K2.h5')
R = load_h5(f'{DIR}/{seq}/R.h5')
T = load_h5(f'{DIR}/{seq}/T.h5')
F_pred, inl_mask = submission[0][seq], submission[1][seq]
ang_errors[seq] = {}
for k, m in tqdm(matches.items()):
if F_pred[k] is None:
ang_errors[seq][k] = 3.14
continue
img_id1 = k.split('-')[0]
img_id2 = k.split('-')[1]
K1 = K1_K2[k][0][0]
K2 = K1_K2[k][0][1]
try:
E_cv_from_F = get_E_from_F(F_pred[k], K1, K2)
except:
print ("Fail")
E = np.eye(3)
R1 = R[img_id1]
R2 = R[img_id2]
T1 = T[img_id1]
T2 = T[img_id2]
dR = np.dot(R2, R1.T)
dT = T2 - np.dot(dR, T1)
pts1 = m[inl_mask[k],:2] # coordinates in image 1
pts2 = m[inl_mask[k],2:] # coordinates in image 2
p1n = normalize_keypoints(pts1, K1)
p2n = normalize_keypoints(pts2, K2)
ang_errors[seq][k] = max(eval_essential_matrix(p1n, p2n, E_cv_from_F, dR, dT))
return ang_errors
def grid_search_hypers_opencv(INL_THs = [0.75, 1.0, 1.5, 2.0, 3.0, 4.0],
MATCH_THs = [0.75, 0.8, 0.85, 0.9, 0.95]):
res = {}
for inl_th in INL_THs:
for match_th in MATCH_THs:
key = f'{inl_th}_{match_th}'
print (f"inlier_th = {inl_th}, snn_ration = {match_th}")
cv2_results = create_F_submission_cv2(split = 'val',
inlier_th = inl_th,
match_th = match_th)
MAEs = evaluate_results(cv2_results, 'val')
mAA = calc_mAA_FE(MAEs)
final = 0
for k,v in mAA.items():
final+= v / float(len(mAA))
print (f'Validation mAA = {final}')
res[key] = final
max_MAA = 0
inl_good = 0
match_good = 0
for k, v in res.items():
if max_MAA < v:
max_MAA = v
pars = k.split('_')
match_good = float(pars[1])
inl_good = float(pars[0])
return inl_good, match_good, max_MAA
if __name__ == '__main__':
# Search for the best hyperparameters on the validation set
print ("Searching hypers")
inl_good, match_good, max_MAA = grid_search_hypers_opencv()
print (f"The best hyperparameters for OpenCV H RANSAC are")
print (f"inlier_th = {inl_good}, snn_ration = {match_good}. Validation mAA = {max_MAA}")
print ("Creating submission")
cv2_test_submission = create_F_submission_cv2(split = 'test', inlier_th = inl_good, match_th = match_good)
for ds_name, models in cv2_test_submission.items():
save_h5(models, f'F_opencv_{ds_name}_submission.h5')
print (f"Saved to F_opencv_{ds_name}_submission.h5")