def kafkastream():
c = Consumer({
'group.id': args.groupid,
'default.topic.config': {
'auto.offset.reset': 'earliest',
'enable.auto.commit': 'false'
}
})
# c.subscribe(['/user/mapr/nextgenDLapp/rawvideostream:topic1'])
c.subscribe([args.stream + ':' + args.topic])
running = True
while running:
msg = c.poll(timeout=0.2)
if msg is None: continue
if not msg.error():
nparr = np.fromstring(msg.value(), np.uint8)
image = cv2.imdecode(nparr, 1)
ret, jpeg = cv2.imencode('.png', image)
bytecode = jpeg.tobytes()
time.sleep(args.timeout)
yield (b'--frame\r\n'
b'Content-Type: image/png\r\n\r\n' + bytecode + b'\r\n\r\n')
elif msg.error().code() != KafkaError._PARTITION_EOF:
print(msg.error())
running = False
c.close()
def kafkastream():
c = Consumer({
'group.id': 'consumer1',
'default.topic.config': {
'auto.offset.reset': 'earliest',
'enable.auto.commit': 'false'
}
})
c.subscribe(['/tmp/rawvideostream:topic1'])
running = True
while running:
msg = c.poll(timeout=1.0)
if msg is None: continue
if not msg.error():
nparr = np.fromstring(msg.value(), np.uint8)
image = cv2.imdecode(nparr, 1)
frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
ret, jpeg = cv2.imencode('.png', frame)
bytecode = jpeg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/png\r\n\r\n' + bytecode + b'\r\n\r\n')
elif msg.error().code() != KafkaError._PARTITION_EOF:
print(msg.error())
running = False
c.close()
def kafkastream():
c = Consumer({
'group.id': 'consumer2',
'default.topic.config': {
'auto.offset.reset': 'earliest',
'enable.auto.commit': 'false'
}
})
# c.subscribe(['/user/mapr/nextgenDLapp/rawvideostream:topic1'])
c.subscribe(['/tmp/rawvideostream:topic1'])
running = True
while running:
msg = c.poll(timeout=0.2)
if msg is None: continue
if not msg.error():
nparr = np.fromstring(msg.value(), np.uint8)
bytecode = detect(nparr)
yield (b'--frame\r\n'
b'Content-Type: image/png\r\n\r\n' + bytecode + b'\r\n\r\n')
elif msg.error().code() != KafkaError._PARTITION_EOF:
print(msg.error())
running = False
c.close()
dets = dets[keep, :]
print("video position (ms): "+msg.key())
print(dets.shape[0])
toc = time.time()
img_final = img_orig.copy()
# color = cv2.cvtColor(img_orig, cv2.COLOR_RGB2BGR)
print("time cost is:{}s".format(toc-tic))
embedding_vector = []
bbox_vector = []
for i in range(dets.shape[0]):
bbox = dets[i, :4]
roundfunc = lambda t: int(round(t/scale))
vfunc = np.vectorize(roundfunc)
bbox = vfunc(bbox)
# cv2.rectangle(color, (int(round(bbox[0]/scale)), int(round(bbox[1]/scale))),
f_temp, img_orig_temp = model.get_feature(img_orig, bbox, None)
embedding_vector.append(f_temp)
bbox_vector.append(bbox)
cv2.rectangle(img_final, (int(round(bbox[0])), int(round(bbox[1]))),
(int(round(bbox[2])), int(round(bbox[3]))), (0, 255, 0), 2)
img_final = cv2.cvtColor(img_final, cv2.COLOR_RGB2BGR)
ret, jpeg = cv2.imencode('.png', img_final)
# p.produce(args.writetopic2, jpeg.tostring())
p_orig.produce(args.writetopic1, pickle.dumps([msg.value(), bbox_vector, embedding_vector]), msg.key())
elif msg.error().code() != KafkaError._PARTITION_EOF:
print(msg.error())
running = False
c.close()
p.flush()
def kafkastream():
if args.gpuid >= 0:
ctx = mx.gpu(args.gpuid)
else:
ctx = mx.cpu()
_, arg_params, aux_params = mx.model.load_checkpoint('mxnet-face-fr50', 0)
arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
sym = resnet_50(num_class=2)
model = face_embedding.FaceModel(args.gpuid)
f1T = get_face_embedding(args.filename, arg_params, aux_params, sym, model,
ctx)
c = Consumer({
'group.id': args.groupid,
'default.topic.config': {
'auto.offset.reset': 'earliest',
'enable.auto.commit': 'false'
}
})
c.subscribe([args.readstream + ':' + args.readtopic])
running = True
p = Producer({'streams.producer.default.stream': args.writestream})
while running:
msg = c.poll(timeout=0)
if msg is None: continue
if not msg.error():
pickle_vector = pickle.loads(msg.value())
nparr = np.fromstring(pickle_vector[0], np.uint8)
img_orig = cv2.imdecode(nparr, 1)
bbox_vector = pickle_vector[1]
print(len(bbox_vector))
embedding_vector = pickle_vector[2]
if len(embedding_vector) > 0:
sim_vector = [np.dot(f, f1T) for f in embedding_vector]
idx = sim_vector.index(max(sim_vector))
bbox = bbox_vector[idx]
sim = sim_vector[idx]
if sim > args.threshold:
img = cv2.cvtColor(img_orig, cv2.COLOR_RGB2BGR)
cv2.rectangle(img,
(int(round(bbox[0])), int(round(bbox[1]))),
(int(round(bbox[2])), int(round(bbox[3]))),
(0, 255, 0), 2)
ret, jpeg = cv2.imencode('.png', img)
bytecode = jpeg.tobytes()
time.sleep(args.timeout)
yield (b'--frame\r\n'
b'Content-Type: image/png\r\n\r\n' + bytecode +
b'\r\n\r\n')
if args.writetostream:
p.produce(args.writetopic, jpeg.tostring())
print(args.writetopic)
elif msg.error().code() != KafkaError._PARTITION_EOF:
print(msg.error())
running = False
c.close()
p.flush()