Opencv流媒体太慢 [英] Opencv streaming is too laggy
问题描述
我已经用张量流对我的模型进行了重新训练,以建立诗人的初始模型.预测需要0.4秒,排序需要2秒.由于需要花费很长时间,因此帧比较滞后,并且在预测时会被打乱.是否可以通过某种方式使帧平滑,尽管预测需要时间? 以下是我的代码...
I have retrained my model with tensorflow for poets inception model. It takes 0.4 seconds for predictions and 2 seconds for sorting. Since it takes a so long time, the frame is laggy and it is scrambled while predicting. Is there by any means that I can make frame smooth though the prediction takes time? Following is my code...
camera = cv2.VideoCapture(0)
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile('retrained_labels.txt')]
def grabVideoFeed():
grabbed, frame = camera.read()
return frame if grabbed else None
def initialSetup():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
start_time = timeit.default_timer()
# This takes 2-5 seconds to run
# Unpersists graph from file
with tf.gfile.FastGFile('retrained_graph.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
print 'Took {} seconds to unpersist the graph'.format(timeit.default_timer() - start_time)
initialSetup()
with tf.Session() as sess:
start_time = timeit.default_timer()
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
print 'Took {} seconds to feed data to graph'.format(timeit.default_timer() - start_time)
while True:
frame = grabVideoFeed()
if frame is None:
raise SystemError('Issue grabbing the frame')
frame = cv2.resize(frame, (299, 299), interpolation=cv2.INTER_CUBIC)
cv2.imshow('Main', frame)
# adhere to TS graph input structure
numpy_frame = np.asarray(frame)
numpy_frame = cv2.normalize(numpy_frame.astype('float'), None, -0.5, .5, cv2.NORM_MINMAX)
numpy_final = np.expand_dims(numpy_frame, axis=0)
start_time = timeit.default_timer()
# This takes 2-5 seconds as well
predictions = sess.run(softmax_tensor, {'Mul:0': numpy_final})
print 'Took {} seconds to perform prediction'.format(timeit.default_timer() - start_time)
start_time = timeit.default_timer()
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
print 'Took {} seconds to sort the predictions'.format(timeit.default_timer() - start_time)
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
print '********* Session Ended *********'
if cv2.waitKey(1) & 0xFF == ord('q'):
sess.close()
break
camera.release()
cv2.destroyAllWindows()
推荐答案
@ dat-tran是正确的,尽管rcnn较快,但也会有些滞后. yolo,很好.
@dat-tran is correct ,Although fater rcnn is fast but it will also lags somewhat.For no lag you can use yolo, ssd models, i have used yolo it is good.
对于队列和多处理,您可以使用以下代码.
for Queues and Multiprocessing you can use following code.
from utils import FPS, WebcamVideoStream
from multiprocessing import Process, Queue, Pool
def worker(input_q, output_q):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
start_time = timeit.default_timer()
# This takes 2-5 seconds to run
# Unpersists graph from file
graph_def = tf.Graph()
with graph_def.as_default():
graph_def_ = tf.GraphDef()
with tf.gfile.FastGFile('retrained_graph.pb', 'rb') as f:
graph_def_.ParseFromString(f.read())
tf.import_graph_def(graph_def_, name='')
sess = tf.Session(graph=graph_def)
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
numpy_frame = np.asarray(frame)
numpy_frame = cv2.normalize(numpy_frame.astype('float'), None, -0.5, .5, cv2.NORM_MINMAX)
numpy_final = np.expand_dims(numpy_frame, axis=0)
start_time = timeit.default_timer()
# This takes 2-5 seconds as well
predictions = sess.run(softmax_tensor, {'Mul:0': numpy_final})
print 'Took {} seconds to perform prediction'.format(timeit.default_timer() - start_time)
start_time = timeit.default_timer()
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
print 'Took {} seconds to sort the predictions'.format(timeit.default_timer() - start_time)
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
output_q.put(frame)
fps.stop()
sess.close()
if __name__ == '__main__':
input_q = Queue(maxsize=10)
output_q = Queue(maxsize=10)
process = Process(target=worker, args=((input_q, output_q)))
process.daemon = True
pool = Pool(1, worker, (input_q, output_q))
video_capture = WebcamVideoStream(src=0,
width=args.width,
height=args.height).start()
fps = FPS().start()
while (video_capture.isOpened()):
_,frame = video_capture.read()
input_q.put(frame)
cv2.namedWindow('Image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Image', 600, 600)
cv2.imshow('Image', output_q.get())
fps.update()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps.stop()
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