def geometric_voting_rel(boxes, wmax, wmin, rtol, intersect_mode, verb=False):
global centers, w_max, w_min, radii, r_rel_tol, vote_db, relative_search, verbose
relative_search = True
vote_db = {}
r_rel_tol = rtol
w_max = wmax
w_min = wmin
n = boxes.shape[0]
radii = np.zeros(n)
center_points = np.zeros((n, 2))
# approximate all radii in pixels
for i, box in enumerate(boxes):
radii[i] = util.calculate_radius(box)
center_points[i] = util.calculate_center(box)
centers = center_points
# get sorting vector
sort = radii.argsort()[::-1]
# sort dat shit
radii = radii[sort]
tuple_generator = combos(np.arange(n), 2)
for count, (ci, cj) in enumerate(tuple_generator):
# get pair-database indices of pair candidates
if intersect_mode:
ci_candidates, cj_candidates = search_pair(ci, cj, True)
add_votes_to_db(vote_db, ci, ci_candidates)
add_votes_to_db(vote_db, cj, cj_candidates)
else:
ci_d, ci_r, cj_d, cj_r = search_pair(ci, cj, False)
add_votes_to_db(vote_db, ci, ci_d)
add_votes_to_db(vote_db, ci, ci_r)
add_votes_to_db(vote_db, cj, cj_d)
add_votes_to_db(vote_db, cj, cj_r)
return do_final_validation(), vote_db
def exclusion_by_intersection_rel(boxes, wmax, wmin, rtol, verb=False):
global centers, w_max, w_min, radii, r_rel_tol, intersect_db, relative_search, verbose
relative_search = True
verbose = verb
intersect_db = {}
r_rel_tol = rtol
w_max = wmax
w_min = wmin
n = boxes.shape[0]
radii = np.zeros(n)
center_points = np.zeros((n, 2))
# approximate all radii in pixels
for i, box in enumerate(boxes):
radii[i] = util.calculate_radius(box)
center_points[i] = util.calculate_center(box)
centers = center_points
# get sorting vector
sort = radii.argsort()[::-1]
# sort dat shit
radii = radii[sort]
tuple_generator = combos(np.arange(n), 2)
for ci, cj in tuple_generator:
# get pair-database indices of pair candidates
ci_candidates, cj_candidates = search_pair(ci, cj, True)
add_ids_to_db(intersect_db, ci, ci_candidates)
add_ids_to_db(intersect_db, cj, cj_candidates)
return do_final_validation()
def calculate_km_per_pixel():
hdf = pd.HDFStore('crater_database/crater_db.h5', 'r')
crater_db = hdf.get('/db')
km_per_pixel = {}
for img, anns in ann_data.items():
kmpp_img = 0
count = 0
for ann in anns:
real_radius = approximate_visual_distance(crater_db.loc[crater_db.index[(ann['crater_id'])], 'radius'])
pixel_radius = calculate_radius([ann['x1'], ann['y1'], ann['x2'], ann['y2']])
kmpp_img += real_radius / pixel_radius
count += 1
if count != 0:
kmpp_img /= count
km_per_pixel[img] = kmpp_img
hdf.close()
return km_per_pixel
def geometric_voting_abs(boxes, km_pp, r_tol, d_tol, wmax, verb=False):
global radius_candidates, rtol, dtol, centers, vote_db, kmpp, w_max, relative_search, verbose
relative_search = False
vote_db = {}
radius_candidates = {}
dtol = d_tol
rtol = r_tol
w_max = wmax
kmpp = km_pp
n = boxes.shape[0]
radii = np.zeros(n)
center_points = np.zeros((n, 2))
# calculate all radii and center points
for i, box in enumerate(boxes):
radii[i] = util.calculate_radius(box) * kmpp
center_points[i] = util.calculate_center(box)
# get sorting vector
sort = radii.argsort()[::-1]
# sort dat shit
radii = radii[sort]
center_points = center_points[sort]
center_points *= kmpp
centers = center_points
# get the radius candidates
for c, r in enumerate(radii):
radius_candidates[c] = pdb.get_craters_by_real_radius(r, r_tol)
tuples = list(combos(np.arange(n), 2))
t = len(tuples)
intersect = False
for count, (ci, cj) in enumerate(tuples):
util.print_progress(count, t, 'Pairs loop', 'complete')
ci_candidates, cj_candidates = search_pair(ci, cj, False)
vote_db = add_votes_to_db(vote_db, ci, ci_candidates)
vote_db = add_votes_to_db(vote_db, cj, cj_candidates)
if not intersect:
vote_db = add_votes_to_db(vote_db, ci, radius_candidates[ci])
vote_db = add_votes_to_db(vote_db, cj, radius_candidates[cj])
# check_db()
return do_final_validation()
def measure(anns, kmpp):
hdf = pd.HDFStore('crater_database/crater_db.h5', 'r')
crater_db = hdf.get('/db')
g = len(anns)
tuples = combos(np.arange(g), 2)
u = 0
v = 0
diff_dist = 0
diff_rad = 0
for a in range(g):
box_a = [anns[a]['x1'], anns[a]['y1'], anns[a]['x2'], anns[a]['y2']]
pixel_derived_radius = calculate_radius(box_a) * kmpp
real_radius = crater_db.loc[crater_db.index[(anns[a]['crater_id'])], 'radius']
d = real_radius / pixel_derived_radius
diff_rad += d
print('RADIUS DIFF: ', d)
v += 1
diff_rad /= v
print('########################### RADIUS AVG DIFF: ', diff_rad)
for a, b in tuples:
u += 1
print('#####################')
print('distances between ', anns[a]['crater_id'], ' with ', anns[a]['lat'], anns[a]['lon'], 'and',
anns[b]['crater_id'], ' with ', anns[b]['lat'], anns[b]['lon'])
hav = calculate_haversine_distance([anns[a]['lat'], anns[a]['lon']], [anns[b]['lat'], anns[b]['lon']])
appr_real = approximate_visual_distance(hav)
print('haversine: ', hav)
print('approx: ', appr_real)
box_a = [anns[a]['x1'], anns[a]['y1'], anns[a]['x2'], anns[a]['y2']]
box_b = [anns[b]['x1'], anns[b]['y1'], anns[b]['x2'], anns[b]['y2']]
approx_dist_pixels = \
cdist(np.array([calculate_center(box_a)]), np.array([calculate_center(box_b)]))[0][0]
appr_img = approx_dist_pixels * kmpp
print('approx pixel dist from bounding box: ', approx_dist_pixels, 'approx km dist from bounding box: ',
appr_img)
print('difference factor: ', appr_img / appr_real)
diff_dist += appr_img / appr_real
print('#####################', 'DIFF_AVG:', diff_dist / u, '####################')
hdf.close()
def exclusion_by_intersection_abs(boxes,
km_pp,
r_tol,
d_tol,
wmax,
verb=False):
global radius_candidates, rtol, dtol, centers, intersect_db, kmpp, w_max, relative_search, verbose
relative_search = False
intersect_db = {}
radius_candidates = {}
dtol = d_tol
rtol = r_tol
w_max = wmax
kmpp = km_pp
n = boxes.shape[0]
radii = np.zeros(n)
center_points = np.zeros((n, 2))
# calculate all radii and center points
for i, box in enumerate(boxes):
radii[i] = util.calculate_radius(box) * kmpp
center_points[i] = util.calculate_center(box)
# get sorting vector
sort = radii.argsort()[::-1]
# sort dat shit
radii = radii[sort]
center_points = center_points[sort]
center_points *= kmpp
centers = center_points
# get the radius candidates
for c, r in enumerate(radii):
radius_candidates[c] = pdb.get_craters_by_real_radius(r, r_tol)
tuples = list(combos(np.arange(n), 2))
for ci, cj in tuples:
ci_candidates, cj_candidates = search_pair(ci, cj, True)
add_ids_to_db(intersect_db, ci, ci_candidates)
add_ids_to_db(intersect_db, cj, cj_candidates)
return do_final_validation()