def grid_suppress(points, weights, cell_size, cell_point_n):
cells = np.array(points // cell_size, np.int32).view(np.int64).ravel()
n = len(points)
mask = np.zeros(n, np.bool8)
for _, idxs in common.iter_grouped(cells, np.arange(n)):
best_idxs = idxs[weights[idxs].argsort()[-cell_point_n:]]
mask[best_idxs] = True
return mask
def grid_suppress(points, weights, cell_size, cell_point_n):
cells = np.array(points // cell_size, np.int32).view(np.int64).ravel()
n = len(points)
mask = np.zeros(n, np.bool8)
for _, idxs in common.iter_grouped(cells, np.arange(n)):
best_idxs = idxs[weights[idxs].argsort()[-cell_point_n:]]
mask[best_idxs] = True
return mask
def grid_suppress_kp(key_points, target_count, columns, img_size):
if len(key_points) == 0:
return key_points
w, h = img_size
rows = 1.0 * h / w * columns
target_per_cell = int(target_count // (rows * columns))
cell_size = w / columns
key_points = np.array(key_points)
cells = np.int32(np.int32([kp.pt for kp in key_points]) // cell_size)
cells = cells.view(np.int64).ravel()
res = []
for _, kps in common.iter_grouped(cells, key_points):
w = np.float32([kp.response for kp in kps])
idx = w.argsort()[-target_per_cell:]
res.extend(kps[idx])
return res
def grid_suppress_kp(key_points, target_count, columns, img_size):
if len(key_points) == 0:
return key_points
w, h = img_size
rows = 1.0 * h / w * columns
target_per_cell = int(target_count // (rows * columns))
cell_size = w / columns
key_points = np.array(key_points)
cells = np.int32(np.int32([kp.pt for kp in key_points]) // cell_size)
cells = cells.view(np.int64).ravel()
res = []
for _, kps in common.iter_grouped(cells, key_points):
w = np.float32([kp.response for kp in kps])
idx = w.argsort()[-target_per_cell:]
res.extend(kps[idx])
return res
