我正在尝试加快计算，以进行广泛的实时对象检测和进行计算。
我正在使用openCV与线程池和生产者，视频捕获的消费者。但是执行速度与串行速度相同。
我将如何提高执行速度？
if __name__ == "__main__":
video_name = '2016-11-18_07-30-01.h264'
cap = cv2.VideoCapture(video_name)
det = detector.CarDetector()
car_tracker = Sort_Algorithm.Sort()
ped_tracker = Sort_Algorithm.Sort()
df_region, df_line = load_filter()
region = Region(df_region)
distance = compute_max_polygon_diagonal(df_region) * 0.1
region_buffered = region.buffer(distance)
threadn = cv2.getNumberOfCPUs()
pool = ThreadPool(processes = 2)
pending = deque()
threaded_mode = True
lock = threading.Lock()
while True:
while len(pending) > 0 and pending[0].ready():
res = pending.popleft().get()
cv2.imshow('video ', res)
if len(pending) < threadn:
ret, frame = cap.read()
if threaded_mode:
t1 = time.time()
H = [-2.01134074616, -16.6502442427, -1314.05715739, -3.35391526592, -22.3546973012, 2683.63584335,
-0.00130731963137, -0.0396207582264, 1]
matrix = np.reshape(H, (3, 3))
dst = cv2.warpPerspective(frame.copy(), matrix, (frame.shape[1], frame.shape[0]))
task = pool.apply_async(pipeline, (lock, frame.copy(),car_tracker, ped_tracker,df_region,region_buffered, df_line, det, dst, matrix))
cv2.imshow('dst', dst)
else:
task = DummyTask(pipeline,(lock, frame.copy(),car_tracker, ped_tracker,df_region, region_buffered, df_line, det, dst, matrix))
pending.append(task)
ch = cv2.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
管道的代码：
def pipeline(lock, img, car_tracker, ped_tracker, df_region, region_buffered, df_line, det, dst, H):
lock.acquire()
global point_lists
global df_car_lists
global frame_idx
global counter
global data_peds
global data_cars
global genera_data_pd_cars
global genera_data_pd_peds
car_box, ped_box = det.get_localization(img)
car_detections = car_tracker.update(np.array(car_box))
ped_detections = ped_tracker.update(np.array(ped_box))
saved_region = df_region.values
saved_region = np.delete(saved_region, 2, 1)
frame_idx+=1
cv2.warpPerspective(np.array(df_line, dtype=np.float32), H, (df_line.shape[1], df_line.shape[0]))
cv2.polylines(dst, np.int32([[saved_region]]), False, color=(255, 0, 0))
cv2.polylines(dst, np.int32([np.array(df_line, dtype=np.float32)]), False, color=(255, 0, 0))
for trk in car_detections:
trk = trk.astype(np.int32)
helpers.draw_box_label(img, trk, trk[4]) # Draw the bounding boxes on the
for other in ped_detections:
other = other.astype(np.int32)
helpers.draw_box_label(img, other, other[4]) # Draw the bounding boxes on the
for trk in car_detections:
trk = trk.astype(np.int32)
p = np.array([[((trk[1] + trk[3]) / 2, (trk[0] + trk[2]) / 2)]], dtype=np.float32)
center_pt = cv2.perspectiveTransform(p, H)
ptx = center_pt.T.item(0)
pty = center_pt.T.item(1)
df_cars = compute(trk[4], ptx, pty, frame_idx, df_region, region_buffered, df_line)
genera_data_pd_cars = genera_data_pd_cars.append(df_cars)
for other in ped_detections:
other = other.astype(np.int32)
p = np.array([[((other[1] + other[3]) / 2, (other[0] + other[2]) / 2)]], dtype=np.float32)
center_pt = cv2.perspectiveTransform(p, H)
ptx = center_pt.T.item(0)
pty = center_pt.T.item(1)
df_peds = compute(other[4], ptx, pty, frame_idx, df_region, region_buffered, df_line)
genera_data_pd_peds = genera_data_pd_cars.append(df_peds)
query = "is_in_region == True and is_in_region_now == True"
df_peds = genera_data_pd_peds.query(query)
query = " is_in_region == True"
df_cars = genera_data_pd_cars.query(query)
if len(df_cars)> 1 and len(df_peds) > 1:
df_car_in_t_range_ped = select_slice(df_cars, df_peds)
df_ped_in_t_range_car = select_slice(df_peds, df_cars)
t_abs_crossing_car = df_cars['t_abs_at_crossing'].iloc[0]
t_abs_crossing_ped = df_peds['t_abs_at_crossing'].iloc[0]
dt_crossing = t_abs_crossing_car - t_abs_crossing_ped
is_able_to_pass_before_ped =
((df_car_in_t_range_ped['t_abs_at_crossing_estimated'] -
t_abs_crossing_ped) > 0).any()
behavior = Behavior( # is_passed_before_ped
dt_crossing < 0,
# is_able_to_stop
df_car_in_t_range_ped['is_able_to_halt'].any(),
# is_too_fast
df_car_in_t_range_ped['is_too_fast'].any(),
# is_close_enough
df_car_in_t_range_ped['is_close_enough'].any(),
# is_able_to_pass_before_ped
is_able_to_pass_before_ped)
interaction = Interaction(trk[4], other[4])
interaction = interaction.assess_behavior(behavior)
code, res, msg = interaction.code, interaction.res, interaction.msg
print(msg)
genera_data_pd_cars = genera_data_pd_cars.iloc[0:0]
genera_data_pd_peds = genera_data_pd_peds.iloc[0:0]
lock.release()
return img
python中用于CPU绑定任务的多线程受到GIL的限制，并有效地使单线程运行时间。
COURSE如果您为CPU绑定的任务启动多个线程，则性能甚至会降级，因为对于内核和Python解释器都有很多开销来管理这些线程。
内核想要安排这些线程，而Python希望将这些线程同时运行，并且结果很大程度上会使性能降低。
如果您仅在线程中使用numpy，那么您会没事的，因为numpy不会受到GIL的影响，因为它使用了原子操作，但是我不确定OpenCV是否也是如此。
python ar中的线程不打算用于计算任务。
这是使用Python的线程的经典问题，请考虑使用multiprocessing，并且有关此主题的文章数量，您可能需要检查其中的一些。
线程未在CPYTHON中并行执行。尝试使用ProcessPoolExecutor。