def sendFrame(frame, HOST, PORT, typ=request_pb2.OBJECT, name=None):
print(frame.shape)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
sock.connect((HOST, PORT))
retval, buf = cv2.imencode(".jpg", frame)
req = request_pb2.DNNRequest()
req.type = typ
req.data = str(bytearray(buf))
if (name != None):
req.model = name
send_message(sock, req)
len_buf = socket_read_n(sock, 4)
msg_len = struct.unpack('>L', len_buf)[0]
msg_buf = socket_read_n(sock, msg_len)
msg = request_pb2.DNNResponse()
msg.ParseFromString(msg_buf)
end = time.time()
print(typ, msg.result_str)
finally:
sock.close()
if msg != None:
return msg.result_str, msg.latency * 1000
def handle(self):
# self.rfile is a file-like object created by the handler;
# we can now use e.g. readline() instead of raw recv() calls
beg = time.time()
len_buf = self.rfile.read(4)
length = struct.unpack('<L', len_buf)[0]
print("len" + str(length))
payload = self.read_n(length)
req = request_pb2.DNNRequest()
req.ParseFromString(payload)
prob = 0
latency = 0
"""
with open("test.jpg", "wb") as f:
f.write(req.data)
input_image = caffe.io.load_image("test.jpg")
"""
if (req.type == request_pb2.FACE):
print("starting prediction")
#input_image = load_face_from_memory(req.data)
t1 = time.time()
input_image = load_image_from_memory(req.data)
prepared = face_input_prepare(face_net1, [input_image])
#out = face_net1.forward_all(**{face_net1.inputs[0]: prepared})
out = face_net1.forward(end="Result",
**{face_net1.inputs[0]: prepared})
out2 = face_net2.forward_all(
**{face_net2.inputs[0]: out["Result"]})
i = out2["prob"].argmax()
prob = out2["prob"].squeeze(axis=(2, 3))[0][i]
t2 = time.time()
latency = t2 - t1
print(prob)
#label = face_words[i]
label = target1[i]
print(i, label)
elif (req.type == request_pb2.SCENE):
print("scene")
input_image = load_image_from_memory(req.data)
t1 = time.time()
images = np.asarray(
caffe.io.oversample([input_image], scene_net.crop_dims))
caffe_in = np.zeros(np.array(images.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(images):
caffe_in[ix] = scene_net.preprocess('data', in_)
out = scene_net.forward_all(data=caffe_in)
prediction = out[scene_net.outputs[0]].squeeze(axis=(2, 3))
prediction = prediction.reshape((len(prediction) / 10, 10, -1))
prediction = prediction.mean(1)
top5 = prediction.argsort()[0][-5:]
i = prediction.argmax()
t2 = time.time()
latency = t2 - t1
label = scene_words[i]
top5_label = map(lambda x: scene_words[x].split("\t")[1], top5)
top5_label.reverse()
label = ": ".join(top5_label)
print(i, label)
else:
#prediction = net.forward_all(data=np.asarray([net.preprocess('data', input_image)]))
input_image = load_image_from_memory(req.data)
t1 = time.time()
images = np.asarray(
caffe.io.oversample([input_image], net.crop_dims))
caffe_in = np.zeros(np.array(images.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(images):
caffe_in[ix] = net.preprocess('data', in_)
out = net.forward_all(data=caffe_in)
prediction = out[net.outputs[0]].squeeze(axis=(2, 3))
prediction = prediction.reshape((len(prediction) / 10, 10, -1))
prediction = prediction.mean(1)
top5 = prediction.argsort()[0][-5:]
i = prediction.argmax()
t2 = time.time()
label = words[i]
top5_label = map(lambda x: words[x].split(" ", 1)[1].split(",")[0],
top5)
top5_label.reverse()
label = ": ".join(top5_label)
latency = t2 - t1
print(i, label)
print "{} wrote:".format(self.client_address[0])
response = request_pb2.DNNResponse()
response.success = True
response.result = i
response.result_str = label
response.latency = latency
#response.confidence = prob
s = response.SerializeToString()
packed_len = struct.pack('<L', len(s))
# Likewise, self.wfile is a file-like object used to write back
# to the client
self.wfile.write(packed_len + s)
n -= len(data)
return buf
req = request_pb2.DNNRequest()
req.type = request_pb2.FACE
with open("../cat.jpg", "rb") as f:
req.data = f.read()
#HOST, PORT = "archon.cs.washington.edu", 9999
HOST, PORT = "archon.cs.washington.edu", int(sys.argv[1])
# Create a socket (SOCK_STREAM means a TCP socket)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
sock.connect((HOST, PORT))
beg = time.time()
send_message(sock, req)
len_buf = socket_read_n(sock, 4)
msg_len = struct.unpack('>L', len_buf)[0]
msg_buf = socket_read_n(sock, msg_len)
msg = request_pb2.DNNResponse()
msg.ParseFromString(msg_buf)
end = time.time()
print(msg.success)
print(msg.result)
print((end - beg) * 1000)
finally:
sock.close()