in eval_retrieval.py [0:0]
def get_rmac_region_coordinates(self, H, W, L):
# Almost verbatim from Tolias et al Matlab implementation.
# Could be heavily pythonized, but really not worth it...
# Desired overlap of neighboring regions
ovr = 0.4
# Possible regions for the long dimension
steps = np.array((2, 3, 4, 5, 6, 7), dtype=np.float32)
w = np.minimum(H, W)
b = (np.maximum(H, W) - w) / (steps - 1)
# steps(idx) regions for long dimension. The +1 comes from Matlab
# 1-indexing...
idx = np.argmin(np.abs(((w**2 - w * b) / w**2) - ovr)) + 1
# Region overplus per dimension
Wd = 0
Hd = 0
if H < W:
Wd = idx
elif H > W:
Hd = idx
regions_xywh = []
for l in range(1, L+1):
wl = np.floor(2 * w / (l + 1))
wl2 = np.floor(wl / 2 - 1)
# Center coordinates
if l + Wd - 1 > 0:
b = (W - wl) / (l + Wd - 1)
else:
b = 0
cenW = np.floor(wl2 + b * np.arange(l - 1 + Wd + 1)) - wl2
# Center coordinates
if l + Hd - 1 > 0:
b = (H - wl) / (l + Hd - 1)
else:
b = 0
cenH = np.floor(wl2 + b * np.arange(l - 1 + Hd + 1)) - wl2
for i_ in cenH:
for j_ in cenW:
regions_xywh.append([j_, i_, wl, wl])
# Round the regions. Careful with the borders!
for i in range(len(regions_xywh)):
for j in range(4):
regions_xywh[i][j] = int(round(regions_xywh[i][j]))
if regions_xywh[i][0] + regions_xywh[i][2] > W:
regions_xywh[i][0] -= ((regions_xywh[i][0] + regions_xywh[i][2]) - W)
if regions_xywh[i][1] + regions_xywh[i][3] > H:
regions_xywh[i][1] -= ((regions_xywh[i][1] + regions_xywh[i][3]) - H)
return np.array(regions_xywh)