def detect(self, frame, mask=None):
if self.row_divs == 1 and self.col_divs == 1:
return self.detector.detect(frame, mask)
else:
if kVerbose:
print('BlockAdaptor ', self.row_divs, 'x', self.col_divs)
block_generator = img_mask_blocks(frame, mask, self.row_divs,
self.col_divs)
kps_all = [] # list are thread-safe
def detect_block(b_m_i_j):
b, m, i, j = b_m_i_j
if kVerbose and False:
print('BlockAdaptor in block (', i, ',', j, ')')
kps = self.detector.detect(b, mask=m)
#print('adaptor: detected #features: ', len(kps), ' in block (',i,',',j,')')
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0] + j, kp.pt[1] + i)
#print('kp.pt after: ', kp.pt)
kps_all.extend(kps)
if not self.do_parallel:
# process the blocks sequentially
for b, m, i, j in block_generator:
detect_block((b, m, i, j))
else:
with ThreadPoolExecutor(max_workers=4) as executor:
executor.map(
detect_block, block_generator
) # automatic join() at the end of the `width` block
return np.array(kps_all)
def detectAndCompute(self, frame, mask=None):
if self.row_divs == 1 and self.col_divs == 1:
return self.detector.detectAndCompute(frame, mask)
else:
if kVerbose:
print('BlockAdaptor ', self.row_divs, 'x', self.col_divs)
block_generator = img_mask_blocks(frame, mask, self.row_divs,
self.col_divs)
kps_all = []
des_all = []
kps_des_map = {} # (i,j) -> (kps,des)
def detect_and_compute_block(b_m_i_j):
b, m, i, j = b_m_i_j
if kVerbose and False:
print('BlockAdaptor in block (', i, ',', j, ')')
if self.is_detector_equal_to_descriptor:
kps, des = self.detector.detectAndCompute(b, mask=m)
else:
kps = self.detector.detect(b, mask=m)
kps, des = self.descriptor.compute(b, kps)
#print('adaptor: detected #features: ', len(kps), ' in block (',i,',',j,')')
# transform the points
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0] + j, kp.pt[1] + i)
#print('kp.pt after: ', kp.pt)
kps_des_map[(i, j)] = (kps, des)
if not self.do_parallel:
# process the blocks sequentially
for b, m, i, j in block_generator:
detect_and_compute_block((b, m, i, j))
else:
with ThreadPoolExecutor(
max_workers=kBlockAdaptorMaxNumWorkers) as executor:
executor.map(
detect_and_compute_block, block_generator
) # automatic join() at the end of the `width` block
# now merge the computed results
for ij, (kps, des) in kps_des_map.items():
kps_all.extend(kps)
if des is not None and len(des) > 0:
if len(des_all) > 0:
des_all = np.vstack([des_all, des])
else:
des_all = des
return np.array(kps_all), np.array(des_all)