1 #18

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ptinqsey9 merged 3 commits from shf_branch into master 2 years ago

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Can't render this file because it is too large.

@ -0,0 +1,118 @@
import csv
import random
import tensorflow as tf
import os
column_names = []
train_feature=[]
train_label=[]
test_feature=[]
test_label=[]
with open("test.csv","r") as r:
reader = csv.reader(r)
for i in reader:
rand = random.randint(0,100)
if rand<=85:
train_label.append((eval(i.pop())))
b = []
for j in range(len(i)//3):
b.append([j])
train_feature.append(b)
else:
test_label.append((eval(i.pop())))
b = [eval(j) for j in i]
test_feature.append(b)
train_features = tf.constant(train_feature)
train_labels = tf.constant(train_label)
test_features = tf.constant(test_feature)
test_labels = tf.constant(test_label)
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(252,)),
tf.keras.layers.Dense(10, activation=tf.nn.relu), # input shape required
tf.keras.layers.Dense(10, activation=tf.nn.relu),
tf.keras.layers.Dense(3)
])
predictions = model(train_features)
tf.nn.softmax(predictions[:5])
print("Prediction: {}".format(tf.argmax(predictions, axis=1)))
print(" Labels: {}".format(train_labels))
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def loss(model, x, y, training):
# training=training is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
y_ = model(x, training=training)
return loss_object(y_true=y, y_pred=y_)
l = loss(model, train_features, train_labels, training=False)
def grad(model, inputs, targets):
with tf.GradientTape() as tape:
loss_value = loss(model, inputs, targets, training=True)
return loss_value, tape.gradient(loss_value, model.trainable_variables)
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
loss_value, grads = grad(model, train_features, train_labels)
print("Step: {}, Initial Loss: {}".format(optimizer.iterations.numpy(),
loss_value.numpy()))
optimizer.apply_gradients(zip(grads, model.trainable_variables))
print("Step: {}, Loss: {}".format(optimizer.iterations.numpy(),
loss(model, train_features, train_labels, training=True).numpy()))
## Note: Rerunning this cell uses the same model variables
# Keep results for plotting
train_loss_results = []
train_accuracy_results = []
num_epochs = 201
for epoch in range(num_epochs):
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
# Training loop - using batches of 32
branch = 32
for num in range(len(train_features)//branch):
x = train_features[num*branch:(num+1)*branch]
y = train_labels[num*branch:(num+1)*branch]
# Optimize the model
loss_value, grads = grad(model, x, y)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# Track progress
epoch_loss_avg.update_state(loss_value) # Add current batch loss
# Compare predicted label to actual label
# training=True is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
epoch_accuracy.update_state(y, model(x, training=True))
# End epoch
train_loss_results.append(epoch_loss_avg.result())
train_accuracy_results.append(epoch_accuracy.result())
if epoch % 50 == 0:
print("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format(epoch,
epoch_loss_avg.result(),
epoch_accuracy.result()))
test_accuracy = tf.keras.metrics.Accuracy()
model.save("model")
for num in range(len(test_features)):
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
x = test_features[num:num+1]
y = test_labels[num:num+1]
logits = model(x, training=False)
prediction = tf.argmax(logits, axis=1, output_type=tf.int32)
print("真实值为",y,"预测值为",prediction)
test_accuracy(prediction, y)
print("Test set accuracy: {:.3%}".format(test_accuracy.result()))

@ -0,0 +1,69 @@
strs = "INSERT INTO XYZ VALUES ("
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
wt = []
num = 0
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
wt = []
for i in range(42 * 3):
wt.append(0)
for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label)=="Left":
num = 0
else:
num =1
for j,handlms in enumerate(hand_landmarks.landmark):
wt[num*21*3+j*3],wt[num*21*3+j*3+1],wt[num*21*3+j*3+2] = handlms.x, handlms.y, handlms.z
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
ctl = cv2.waitKey(5)
if ctl == ord('q'):
break
elif ctl==ord('s'):
strs=""
for i in wt:
strs+=str(i)[:7]+","
strs = strs[:-1]
print(strs)
cap.release()
print ("数据插入成功")

@ -0,0 +1,63 @@
import cv2
import mediapipe as mp
import csv
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
ls = []
for i in range(21*3*2+1):
ls.append(0)
wt = ls
num = 0
# For webcam input:
cap = cv2.VideoCapture(0)
with open("num_1_5.csv","w") as csvfile:
writer = csv.writer(csvfile)
with mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label)=="Left":
num = 0
else:
num =1
for j,handlms in enumerate(hand_landmarks.landmark):
wt[num*21*3+j*3],wt[num*21*3+j*3+1],wt[num*21*3+j*3+2] = handlms.x, handlms.y, handlms.z
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
ctl = cv2.waitKey(5)
if ctl == ord('q'):
break
elif ctl>=ord('0') and ctl<=ord('9'):
wt[len(wt)-1]=chr(ctl)
writer.writerow(wt)
wt = ls
cap.release()

@ -0,0 +1,81 @@
import sqlite3
conn = sqlite3.connect('info.db')
c = conn.cursor()
print ("数据库打开成功")
strs = "INSERT INTO XYZ VALUES ("
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
ls = []
for i in range(42*2):
ls.append(0)
wt = ls
num = 0
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
aa=0
if results.multi_hand_landmarks:
for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label)=="Left":
num = 0
else:
num =1
for j,handlms in enumerate(hand_landmarks.landmark):
aa+=1
wt[num*21*2+j*2],wt[num*21*2+j*2+1] = handlms.x, handlms.y
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
ctl = cv2.waitKey(5)
if ctl == ord('q'):
break
elif ctl==ord('s'):
word = "\'"+input()+"\',"
strs+=word
print(aa)
print(len(wt))
for i in wt:
strs+=str(i)[:7]+","
strs = strs[:-1] + ");"
c.execute(strs)
conn.commit()
print(strs)
cap.release()
print ("数据插入成功")
conn.close()

@ -0,0 +1,18 @@
import csv
import random
import time
ls = []
with open("num_1_5.csv","r") as r:
read = csv.reader(r)
for i in read:
ls.append(i)
for i in range(len(ls)):
random.seed(time.time())
rand = random.randint(0,100)
ls[i],ls[rand]=ls[rand],ls[i]
with open("test.csv","w") as w:
writer = csv.writer(w)
for i in ls:
writer.writerow(i)

@ -0,0 +1,6 @@
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root.layer_with_weights-2"_tf_keras_layer*Ï{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 6, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10]}}2

Binary file not shown.

@ -0,0 +1,65 @@
import os
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
class remote_mhands():
def __init__(self):
self.hands=[]
self.imgs=[]
self.wt = []
for i in range(21*3*2):
self.wt.append(0)
self.num=0
self.len=2
self.create_mhand()
def create_mhand(self):
for i in range(self.len):
mhand = mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
self.hands.append(mhand)
def recv(self,img):
results=self.hands[self.num].process(img)
self.num=self.num%self.len+1
if results.multi_hand_landmarks:
for i in range(len(self.wt)):
self.wt[i]=0
for i, hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label) == "Left":
num = 0
else:
num = 1
for j, handlms in enumerate(hand_landmarks.landmark):
self.wt[num * 21 * 3 + j * 3], self.wt[num * 21 * 3 + j * 3 + 1], self.wt[num * 21 * 3 + j * 3 + 2] = handlms.x, handlms.y, handlms.z
return self.wt
def file(self,path):
img=cv2.imread(path)
results=self.hands[self.num].process(img)
self.num=self.num%self.len+1
if results.multi_hand_landmarks:
for i in range(len(self.wt)):
self.wt[i]=0
for i, hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label) == "Left":
num = 0
else:
num = 1
for j, handlms in enumerate(hand_landmarks.landmark):
self.wt[num * 21 * 3 + j * 3], self.wt[num * 21 * 3 + j * 3 + 1], self.wt[num * 21 * 3 + j * 3 + 2] = handlms.x, handlms.y, handlms.z
os.remove(path)
strs=""
for i in self.wt:
strs+=str(i)[:7]+","
strs = strs[:-1]
return strs

@ -0,0 +1,65 @@
import img_return
print ("数据库打开成功")
strs = "INSERT INTO XYZ VALUES ("
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
num = 0
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
imagec=image
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
wt = []
for i in range(42 * 3):
wt.append(0)
for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label)=="Left":
num =0
else:
num =1
for j,handlms in enumerate(hand_landmarks.landmark):
wt[num*21*3+j*3],wt[num*21*3+j*3+1],wt[num*21*3+j*3+2] = handlms.x, handlms.y, handlms.z
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
ctl = cv2.waitKey(5)
if ctl == ord('q'):
break
elif ctl==ord('s'):
re=img_return.img_return(imagec)
re.run2(hands)
cap.release()

@ -0,0 +1,53 @@
strs = "INSERT INTO XYZ VALUES ("
import mediapipe as mp
import cv2
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
wt = []
num = 0
# For webcam input:
class img_return():
def __init__(self,img):
self.image=img
self.image.flags.writeable = False
self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
self.hands=mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
self.num=0
self.wt = []
for i in range(21*3*2):
self.wt.append(0)
def run(self):
results=self.hands.process(self.image)
if results.multi_hand_landmarks:
for i, hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label) == "Left":
num = 0
else:
num = 1
for j, handlms in enumerate(hand_landmarks.landmark):
self.wt[num * 21 * 3 + j * 3], self.wt[num * 21 * 3 + j * 3 + 1], self.wt[num * 21 * 3 + j * 3 + 2] = handlms.x, handlms.y, handlms.z
print(self.wt)
def run2(self,hands):
results=hands.process(self.image)
if results.multi_hand_landmarks:
for i, hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label) == "Left":
num = 0
else:
num = 1
for j, handlms in enumerate(hand_landmarks.landmark):
self.wt[num * 21 * 3 + j * 3], self.wt[num * 21 * 3 + j * 3 + 1], self.wt[num * 21 * 3 + j * 3 + 2] = handlms.x, handlms.y, handlms.z
print(self.wt)

@ -0,0 +1,66 @@
# -*- coding=utf-8 -*-
import socket
import threading
import time
import sys
import os
import struct
import Gramhands
mhand=Gramhands.remote_mhands()
def socket_service():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(('127.0.0.1', 6666))
s.listen(10)
except socket.error as msg:
print(msg)
sys.exit(1)
print('Waiting connection...')
while 1:
conn, addr = s.accept()
t = threading.Thread(target=deal_data, args=(conn, addr))
t.start()
def deal_data(conn, addr):
print('Accept new connection from {0}'.format(addr))
# conn.settimeout(500)
conn.send('Hi, Welcome to the server!'.encode("utf-8"))
while 1:
fileinfo_size = struct.calcsize('128sl')
buf = conn.recv(fileinfo_size)
now_tim = time.time()
now_tim = str(int(now_tim))
if buf:
filename, filesize = struct.unpack('128sl', buf)
recvd_size = 0 # 定义已接收文件的大小
fp = open(now_tim+".png", 'wb')
while not recvd_size == filesize:
if filesize - recvd_size > 1024:
data = conn.recv(1024)
recvd_size += len(data)
else:
data = conn.recv(filesize - recvd_size)
recvd_size = filesize
fp.write(data)
fp.close()
print('end receive...')
strs=mhand.file(now_tim)
conn.send(strs.encode("utf-8"))
print(conn.recv(1024).decode('utf-8'))
conn.close()
break
if __name__ == '__main__':
socket_service()

@ -0,0 +1,78 @@
# -*- coding=utf-8 -*-
"""
file: recv.py
socket service
"""
import socket
import threading
import time
import sys
import os
import struct
import cv2
import numpy as np
import Gramhands
mhand=Gramhands.remote_mhands()
def socket_service():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(('127.0.0.1', 6666))
s.listen(10)
except socket.error as msg:
print(msg)
sys.exit(1)
print('Waiting connection...')
while 1:
conn, addr = s.accept()
t = threading.Thread(target=deal_data, args=(conn, addr))
t.start()
def deal_data(conn, addr):
print('Accept new connection from {0}'.format(addr))
# conn.settimeout(500)
conn.send('Hi, Welcome to the server!'.encode("utf-8"))
while 1:
buffer_data=bytes()
fileinfo_size = struct.calcsize('128sl')
buf = conn.recv(fileinfo_size)
if buf:
filename, filesize = struct.unpack('128sl', buf)
recvd_size = 0 # 定义已接收文件的大小
now_tim=time.time()
now_tim=str(int(now_tim))
fp = open(now_tim+".png", 'wb')
while not recvd_size == filesize:
if filesize - recvd_size > 1024:
data = conn.recv(1024)
buffer_data+=data
recvd_size += len(data)
else:
data = conn.recv(filesize - recvd_size)
recvd_size = filesize
fp.write(data)
fp.close()
print('end receive...')
img_np_arr = np.frombuffer(buffer_data, np.uint8)
image = cv2.imdecode(img_np_arr, cv2.IMREAD_COLOR)
strs=mhand.recv(image)
print(strs)
conn.send('已发送'.encode("utf-8"))
print(conn.recv(1024).decode('utf-8'))
conn.close()
break
if __name__ == '__main__':
socket_service()

@ -0,0 +1,71 @@
strs = "INSERT INTO XYZ VALUES ("
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
wt = []
num = 0
# For webcam input:
input_video="rtmp://127.0.0.1/myapp/demo"
cap = cv2.VideoCapture(input_video)
with mp_hands.Hands(
model_complexity=0,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
wt = []
for i in range(42 * 3):
wt.append(0)
for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
if (results.multi_handedness[i].classification[0].label)=="Left":
num = 0
else:
num =1
for j,handlms in enumerate(hand_landmarks.landmark):
wt[num*21*3+j*3],wt[num*21*3+j*3+1],wt[num*21*3+j*3+2] = handlms.x, handlms.y, handlms.z
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
ctl = cv2.waitKey(5)
if ctl == ord('q'):
break
elif ctl==ord('s'):
strs=""
print(wt)
for i in wt:
strs+=str(i)[:7]+","
strs = strs[:-1]
# print(strs)
cap.release()
print ("数据插入成功")

@ -0,0 +1,116 @@
# limit the number of cpus used by high performance libraries
import sys
import os
import subprocess
from concurrent.futures import ThreadPoolExecutor
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
sys.path.insert(0, './yolov5')
import socket
sk = socket.socket()
address = ('', 12001)
sk.bind(address) # 将本地地址与一个socket绑定在一起
sk.listen(3) # 最多允许有3个客户称呼
l_x, l_y, l_z = 69, 69, 81
target = 1000000
# 1440 1088
# #######################################################################################################################
import cv2
input_video="rtmp://127.0.0.1/myapp/demo"
#读取视频并获取视频帧率、分辨率、总帧数
VideoCapture = cv2.VideoCapture(input_video)
fps = VideoCapture.get(cv2.CAP_PROP_FPS)
size = (int(VideoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(VideoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
totalFrames = int(VideoCapture.get(7))
#创建新视频
x=10 #水印坐标
y=10 #水印坐标
i=1
step_x=5
step_y=5
success,frame = VideoCapture.read() #读取视频第一帧
print(""+str(i)+"帧, 共"+str(totalFrames)+"")
threadPool = ThreadPoolExecutor(
max_workers=500, thread_name_prefix="push_stream_")
def push_stream(pipe, frame):
pipe.stdin.write(frame.tobytes())
while success:
stream_fps = int(fps * 2)
rtmp = "rtmp://127.0.0.1:1935/myapp/demo2"
command = ['ffmpeg',
'-y',
'-f', 'rawvideo',
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-s', "{}x{}".format(size[0],
size[1]),
'-r', str(stream_fps),
'-i', '-',
'-c:v', 'libx264',
'-pix_fmt', 'yuv420p',
'-preset', 'ultrafast',
'-max_delay', '2000', # 100
'-g', str(int(fps)),
'-b:v', '99999999',
'-f', 'flv',
'-vprofile', 'baseline',
rtmp]
pipe = subprocess.Popen(command, stdin=subprocess.PIPE)
if True:
threadPool.submit(push_stream, pipe, frame)
success,frame = VideoCapture.read()
print ('Quitted!') #提示程序已停止
cv2.destroyAllWindows() #程序停止前关闭所有窗口
def detect(opt):
# 获取输出文件夹,输入源,权重,参数等参数
# stream_fps = 30 if dataset.fps[0] * 2 > 30 else dataset.fps[0]*2
stream_fps = int(fps * 2)
rtmp = "rtmp:127.0.0.1/vediostream/livestream"
command = ['ffmpeg',
'-y',
'-f', 'rawvideo',
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-s', "{}x{}".format(size[0],
size[1]),
'-r', str(stream_fps),
'-i', '-',
'-c:v', 'libx264',
'-pix_fmt', 'yuv420p',
'-preset', 'ultrafast',
'-max_delay', '2000', # 100
'-g', str(int(fps)),
'-b:v', '99999999',
'-f', 'flv',
'-vprofile', 'baseline',
rtmp]
pipe = subprocess.Popen(command, stdin=subprocess.PIPE)
if True:
threadPool.submit(push_stream, pipe, frame)

@ -0,0 +1,59 @@
# -*- coding=utf-8 -*-
"""
file: send.py
socket client
"""
import socket
import os
import sys
import struct
import cv2
capture = cv2.VideoCapture(0)
def socket_client():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(('127.0.0.1', 6666))
except socket.error as msg:
print(msg)
sys.exit(1)
print(s.recv(1024).decode("utf-8"))
while 1:
filepath = "./demo.png" # 获取选择的文件名称
print(filepath)
# filepath = input('please input file path: ')
if os.path.isfile(filepath):
# 定义定义文件信息。128s表示文件名为128bytes长l表示一个int或log文件类型在此为文件大小
fileinfo_size = struct.calcsize('128sl')
# 定义文件头信息,包含文件名和文件大小
fhead = struct.pack(
'128sl',
os.path.basename(filepath).encode(encoding="utf-8"),
os.stat(filepath).st_size
)
print('client filepath: {0}'.format(filepath))
s.send(fhead)
fp = open(filepath, 'rb')
while 1:
data = fp.read(1024)
if not data:
print('{0} file send over...'.format(filepath))
break
s.send(data)
print(s.recv(1024).decode("utf-8"))
print(s.recv(1024).decode("utf-8"))
s.close()
break
if __name__ == '__main__':
socket_client()

@ -0,0 +1,510 @@
# limit the number of cpus used by high performance libraries
import sys
import os
from threading import Thread
from sympy import false
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
sys.path.insert(0, './yolov5')
import subprocess
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.plots import Annotator, colors, save_one_box
from yolov5.utils.general import (LOGGER, check_img_size, check_imshow,
colorstr, increment_path,
non_max_suppression, scale_coords,
strip_optimizer, xyxy2xywh)
from yolov5.utils.downloads import attempt_download
from yolov5.utils.datasets import VID_FORMATS, LoadImages, LoadStreams
from yolov5.models.experimental import attempt_load
from yolov5.models.common import DetectMultiBackend
from deep_sort.utils.parser import get_config
from deep_sort.deep_sort import DeepSort
import torch.backends.cudnn as cudnn
import torch
import numpy as np
import cv2
from pathlib import Path
import time
import shutil
import platform
import argparse
from concurrent.futures import ThreadPoolExecutor
import socket
sk = socket.socket()
address = ('', 12001)
sk.bind(address) # 将本地地址与一个socket绑定在一起
sk.listen(3) # 最多允许有3个客户称呼
l_x,l_y,l_z=69,69,81
target=1000000
def recv():
while True:
conn, addr = sk.accept()
global target
data = conn.recv(1024) # 缓冲区大小,接收文件的个数 第一次获取请求
target = data.decode('utf-8')
LOGGER.info(target)
# 1440 1088
# #########################################################################################################################
def find(bbox,img_size):
global l_x,l_y,l_z
x = (bbox[0]+bbox[2])//2
z = (bbox[1]+bbox[3])//2
y = (bbox[3]-bbox[1])
c_x=69
if x < 426:
c_x=37
elif x < 852:
c_x = 69
else:
c_x = 39
c_z = 81
if z < 426:
c_z = 87
elif z < 852:
c_z = 81
else:
c_z = 83
c_y = 69
if y < 320:
c_y=38
elif y <640:
c_y=69
else:
c_y=40
print(" ")
print(c_x," ",c_y," ",c_z)
print(" ")
send_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
if l_x!=69:
send_socket.connect(("", 12000))
send_socket.send(str(c_x).encode('utf-8'))
print("cx:", c_x)
elif l_y!=69:
send_socket.connect(("", 12000))
send_socket.send(str(c_y).encode('utf-8'))
print("cy:", c_y)
elif l_z!=81:
send_socket.connect(("", 12000))
send_socket.send(str(c_z).encode('utf-8'))
print("cz:",c_z)
l_x = c_x
l_y = c_y
l_z = c_z
threadPool = ThreadPoolExecutor(
max_workers=500, thread_name_prefix="push_stream_")
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # yolov5 deepsort root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
def push_stream(pipe, frame):
pipe.stdin.write(frame.tobytes())
def detect(opt):
# 获取输出文件夹,输入源,权重,参数等参数
out, source, yolo_model, deep_sort_model, show_vid, show_stream, save_vid, save_txt, imgsz, evaluate, half, \
project, exist_ok, update, save_crop = \
opt.output, opt.source, opt.yolo_model, opt.deep_sort_model, opt.show_vid, opt.show_stream, opt.save_vid, \
opt.save_txt, opt.imgsz, opt.evaluate, opt.half, opt.project, opt.exist_ok, opt.update, opt.save_crop
# print(source)
webcam = source == '0' or source.startswith(
'rtsp') or source.startswith('http') or source.endswith('.txt')
rtmp_stream = source.startswith('rtmp')
# Initialize
# 获取设备
device = select_device(opt.device)
# /////////////////////////////////////////////////////////////////////////#
# 如果设备为gpu使用Float16
half &= device.type != 'cpu' # half precision only supported on CUDA
# half &= device.type != 'gpu' # half precision only supported on CUDA
# /////////////////////////////////////////////////////////////////////////#
# The MOT16 evaluation runs multiple inference streams in parallel, each one writing to
# its own .txt file. Hence, in that case, the output folder is not restored
if not evaluate:
if os.path.exists(out):
pass
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Directories
if type(yolo_model) is str: # single yolo model
exp_name = yolo_model.split(".")[0]
elif type(yolo_model) is list and len(yolo_model) == 1: # single models after --yolo_model
exp_name = yolo_model[0].split(".")[0]
else: # multiple models after --yolo_model
exp_name = "ensemble"
exp_name = exp_name + "_" + deep_sort_model.split('/')[-1].split('.')[0]
# increment run if project name exists
save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok)
(save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True,
exist_ok=True) # make dir
# Load model
model = DetectMultiBackend(yolo_model, device=device, dnn=opt.dnn)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
# half precision only supported by PyTorch on CUDA
half &= pt and device.type != 'cpu'
if pt:
model.model.half() if half else model.model.float()
# Set Dataloader
# 通过不同的输入源来设置不同的数据加载方式
vid_path, vid_writer = None, None
# Check if environment supports image displays
if show_vid:
show_vid = check_imshow()
if show_stream:
show_stream = check_imshow()
# Dataloader
if webcam:
show_vid = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
nr_sources = len(dataset)
elif rtmp_stream:
cudnn.benchmark = True
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
nr_sources = len(dataset)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
nr_sources = 1
vid_path, vid_writer, txt_path = [
None] * nr_sources, [None] * nr_sources, [None] * nr_sources
th = Thread(target=recv)
th.start()
# initialize deepsort
cfg = get_config()
cfg.merge_from_file(opt.config_deepsort)
# Create as many trackers as there are video sources
deepsort_list = []
for i in range(nr_sources):
deepsort_list.append(
DeepSort(
deep_sort_model,
device,
max_dist=cfg.DEEPSORT.MAX_DIST,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
)
)
outputs = [None] * nr_sources
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# Run tracking
model.warmup(imgsz=(1 if pt else nr_sources, 3, *imgsz)) # warmup
dt, seen = [0.0, 0.0, 0.0, 0.0], 0
print(dataset.vediosize)
# stream_fps = 30 if dataset.fps[0] * 2 > 30 else dataset.fps[0]*2
stream_fps = int(dataset.fps[0]*2)
tmp_g = int(dataset.fps[0]/4)
print(stream_fps)
rtmp = "rtmp:127.0.0.1/vediostream/livestream"
command = ['ffmpeg',
'-y',
'-f', 'rawvideo',
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-s', "{}x{}".format(dataset.vediosize[0],
dataset.vediosize[1]),
'-r', str(stream_fps),
'-i', '-',
'-c:v', 'libx264',
'-pix_fmt', 'yuv420p',
'-preset', 'ultrafast',
'-max_delay', '2000', # 100
'-g', str(int(dataset.fps[0])),
'-b:v', '99999999',
'-f', 'flv',
'-vprofile', 'baseline',
rtmp]
pipe = subprocess.Popen(command, stdin=subprocess.PIPE)
# fourcc = cv2.VideoWriter_fourcc('F','L','V','1') #opencv3.0
# videoWriter = cv2.VideoWriter('output2.mp4', fourcc, 24, (1280, 536))
count_frame = 0
"""
path 图片/视频路径
img进行resize+pad之后的图片
im0s 原size图片
vid_cap 当读取图片时为None读取视频时为视频源
"""
for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):
count_frame += 1
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255.0 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(
save_dir / Path(path[0]).stem, mkdir=True) if opt.visualize else False
"""
前向传播 返回pred的shape是(1, num_boxes, 5+num_class)
h,w为传入网络图片的长和宽注意dataset在检测时使用了矩形推理所以这里h不一定等于w
num_boxes = h/32 * w/32 + h/16 * w/16 + h/8 * w/8
pred[..., 0:4]为预测框坐标
预测框坐标为xywh(中心点+宽长)格式
pred[..., 4]为objectness置信度
pred[..., 5:-1]为分类结果
"""
pred = model(im, augment=opt.augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# Apply NMS
"""
pred:前向传播的输出
conf_thres:置信度阈值
iou_thres:iou阈值
classes:是否只保留特定的类别
agnostic:进行nms是否也去除不同类别之间的框
经过nms之后,预测框格式:xywh-->xyxy(左上角右下角)
pred是一个列表list[torch.tensor],长度为batch_size
每一个torch.tensor的shape为(num_boxes, 6),内容为box+conf+cls
"""
pred = non_max_suppression(
pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms, max_det=opt.max_det)
dt[2] += time_sync() - t3
# Process detections
# 对每一张图片作处理
for i, det in enumerate(pred): # detections per image
seen += 1
# 如果输入源是webcam则batch_size不为1取出dataset中的一张图片
if webcam: # nr_sources >= 1
p, im0, _ = path[i], im0s[i].copy(), dataset.count
p = Path(p) # to Path
s += f'{i}: '
# 设置保存框坐标txt文件的路径
txt_file_name = p.name
# 设置保存图片/视频的路径
save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
elif rtmp_stream:
p, im0, _ = path[i], im0s[i].copy(), dataset.count
p = Path(p) # to Path
s += f'{i}: '
# 设置保存框坐标txt文件的路径
txt_file_name = p.name
# 设置保存图片/视频的路径
save_path = str(save_dir / p.name)
else:
p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
# video file
if source.endswith(VID_FORMATS):
# 设置保存框坐标txt文件的路径
txt_file_name = p.stem
# 设置保存图片/视频的路径
save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
# folder with imgs
else:
# 设置保存框坐标txt文件的路径
txt_file_name = p.parent.name # get folder name containing current img
# 设置保存图片/视频的路径
# im.jpg, vid.mp4, ...
save_path = str(save_dir / p.parent.name)
# 设置保存图片/视频的路径
txt_path = str(save_dir / 'tracks' / txt_file_name) # im.txt
# 设置打印信息(图片长宽)
s += '%gx%g ' % im.shape[2:] # print string
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=2, pil=not ascii)
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
# 调整预测框的坐标基于resize+pad的图片的坐标-->基于原size图片的坐标
# 此时坐标格式为xyxy
det[:, :4] = scale_coords(
im.shape[2:], det[:, :4], im0.shape).round()
# Print results
# 打印检测到的类别数量
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
# add to string
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
xywhs = xyxy2xywh(det[:, 0:4])
confs = det[:, 4]
clss = det[:, 5]
# pass detections to deepsort
t4 = time_sync()
outputs[i] = deepsort_list[i].update(
xywhs.cpu(), confs.cpu(), clss.cpu(), im0)
t5 = time_sync()
dt[3] += t5 - t4
# draw boxes for visualization
# 保存预测结果id 4
if len(outputs[i]) > 0:
# LOGGER.info("开始寻找"+str(target))
for j, (output, conf) in enumerate(zip(outputs[i], confs)):
bboxes = output[0:4]
id = output[4]
if int(id)==int(target):
find(bboxes, dataset.vediosize)
LOGGER.info("开始跟踪目标")
cls = output[5]
if save_txt:
# to MOT format
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2] - output[0]
bbox_h = output[3] - output[1]
# Write MOT compliant results to file
# with open(txt_path + '.txt', 'a') as f:
# f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left, # MOT format
# bbox_top, bbox_w, bbox_h, -1, -1, -1, i))
# 在原图上画框
if save_vid or save_crop or show_vid or show_stream: # Add bbox to image
c = int(cls) # integer class
label = f'{id} {names[c]} {conf:.2f}'
annotator.box_label(
bboxes, label, color=colors(c, True))
if save_crop:
txt_file_name = txt_file_name if (
isinstance(path, list) and len(path) > 1) else ''
save_one_box(bboxes, imc, file=save_dir / 'crops' /
txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True)
# LOGGER.info(
# f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)')
else:
deepsort_list[i].increment_ages()
# LOGGER.info('No detections')
# Stream results
im0 = annotator.result()
# 如果设置展示则show图片/视频
if show_vid:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# pass
# ############################################
if show_stream:
# print()
threadPool.submit(push_stream, pipe, im0)
# pipe.stdin.write(im0.tobytes())
# videoWriter.write(im0)
# ############################################
# Save results (image with detections)
# 设置保存图片/视频
if save_vid:
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
# release previous video writer
vid_writer[i].release()
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
# force *.mp4 suffix on results videos
save_path = str(Path(save_path).with_suffix('.mp4'))
vid_writer[i] = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
time.sleep(0.02)
print("Sum is:", count_frame)
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms deep sort update \
per image at shape {(1, 3, *imgsz)}' % t)
# 打开保存图片和txt的路径(好像只适用于MacOS系统)
if save_txt or save_vid:
s = f"\n{len(list(save_dir.glob('tracks/*.txt')))} tracks saved to {save_dir / 'tracks'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
# update model (to fix SourceChangeWarning)
strip_optimizer(yolo_model)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--yolo_model', nargs='+', type=str,
default='best.torchscript', help='model.pt path(s)')
parser.add_argument('--deep_sort_model', type=str, default='osnet_x0_75_msmt17')
# file/folder, 0 for webcam
parser.add_argument('--source', type=str, default='rtmp://localhost/live/livestream', help='source')
parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--show-vid', action='store_true', help='display tracking video results')
parser.add_argument('--show-stream', action='store_true', help='display tracking video stream results')
parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
parser.add_argument('--save-txt', action='store_true', help='save MOT compliant results to *.txt')
# class 0 is person, 1 is bycicle, 2 is car... 79 is oven
parser.add_argument('--classes', default='25', nargs='+', type=int, help='filter by class: --class 0, or --class 16 17')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--evaluate', action='store_true', help='augmented inference')
parser.add_argument("--config_deepsort", type=str, default="deep_sort/configs/deep_sort.yaml")
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
parser.add_argument('--visualize', action='store_true', help='visualize features')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detection per image')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
parser.add_argument('--project', default=ROOT /
'runs/track', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
with torch.no_grad():
detect(opt)

@ -0,0 +1,72 @@
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: mediapipe/framework/calculator_options.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='mediapipe/framework/calculator_options.proto',
package='mediapipe',
syntax='proto2',
serialized_options=_b('\n\032com.google.mediapipe.protoB\026CalculatorOptionsProto'),
serialized_pb=_b('\n,mediapipe/framework/calculator_options.proto\x12\tmediapipe\"9\n\x11\x43\x61lculatorOptions\x12\x18\n\x0cmerge_fields\x18\x01 \x01(\x08\x42\x02\x18\x01*\n\x08\xa0\x9c\x01\x10\x80\x80\x80\x80\x02\x42\x34\n\x1a\x63om.google.mediapipe.protoB\x16\x43\x61lculatorOptionsProto')
)
_CALCULATOROPTIONS = _descriptor.Descriptor(
name='CalculatorOptions',
full_name='mediapipe.CalculatorOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='merge_fields', full_name='mediapipe.CalculatorOptions.merge_fields', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=_b('\030\001'), file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=True,
syntax='proto2',
extension_ranges=[(20000, 536870912), ],
oneofs=[
],
serialized_start=59,
serialized_end=116,
)
DESCRIPTOR.message_types_by_name['CalculatorOptions'] = _CALCULATOROPTIONS
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
CalculatorOptions = _reflection.GeneratedProtocolMessageType('CalculatorOptions', (_message.Message,), dict(
DESCRIPTOR = _CALCULATOROPTIONS,
__module__ = 'mediapipe.framework.calculator_options_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.CalculatorOptions)
))
_sym_db.RegisterMessage(CalculatorOptions)
DESCRIPTOR._options = None
_CALCULATOROPTIONS.fields_by_name['merge_fields']._options = None
# @@protoc_insertion_point(module_scope)

@ -0,0 +1,786 @@
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: mediapipe/framework/calculator.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from mediapipe.framework import calculator_options_pb2 as mediapipe_dot_framework_dot_calculator__options__pb2
from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
from mediapipe.framework import mediapipe_options_pb2 as mediapipe_dot_framework_dot_mediapipe__options__pb2
from mediapipe.framework import packet_factory_pb2 as mediapipe_dot_framework_dot_packet__factory__pb2
from mediapipe.framework import packet_generator_pb2 as mediapipe_dot_framework_dot_packet__generator__pb2
from mediapipe.framework import status_handler_pb2 as mediapipe_dot_framework_dot_status__handler__pb2
from mediapipe.framework import stream_handler_pb2 as mediapipe_dot_framework_dot_stream__handler__pb2
from mediapipe.framework.calculator_options_pb2 import *
DESCRIPTOR = _descriptor.FileDescriptor(
name='mediapipe/framework/calculator.proto',
package='mediapipe',
syntax='proto3',
serialized_options=_b('\n\032com.google.mediapipe.protoB\017CalculatorProto'),
serialized_pb=_b('\n$mediapipe/framework/calculator.proto\x12\tmediapipe\x1a,mediapipe/framework/calculator_options.proto\x1a\x19google/protobuf/any.proto\x1a+mediapipe/framework/mediapipe_options.proto\x1a(mediapipe/framework/packet_factory.proto\x1a*mediapipe/framework/packet_generator.proto\x1a(mediapipe/framework/status_handler.proto\x1a(mediapipe/framework/stream_handler.proto\"Z\n\x0e\x45xecutorConfig\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12,\n\x07options\x18\x03 \x01(\x0b\x32\x1b.mediapipe.MediaPipeOptions\"\x91\x02\n\x0fInputCollection\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x18\n\x10side_packet_name\x18\x02 \x03(\t\x12\x1c\n\x13\x65xternal_input_name\x18\xea\x07 \x03(\t\x12\x38\n\ninput_type\x18\x03 \x01(\x0e\x32$.mediapipe.InputCollection.InputType\x12\x11\n\tfile_name\x18\x04 \x01(\t\"k\n\tInputType\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x0c\n\x08RECORDIO\x10\x01\x12\x14\n\x10\x46OREIGN_RECORDIO\x10\x02\x12\x14\n\x10\x46OREIGN_CSV_TEXT\x10\x03\x12\x17\n\x13INVALID_UPPER_BOUND\x10\x04\"J\n\x12InputCollectionSet\x12\x34\n\x10input_collection\x18\x01 \x03(\x0b\x32\x1a.mediapipe.InputCollection\"7\n\x0fInputStreamInfo\x12\x11\n\ttag_index\x18\x01 \x01(\t\x12\x11\n\tback_edge\x18\x02 \x01(\x08\"\xd1\x04\n\x0eProfilerConfig\x12$\n\x1chistogram_interval_size_usec\x18\x01 \x01(\x03\x12\x1f\n\x17num_histogram_intervals\x18\x02 \x01(\x03\x12\'\n\x1b\x65nable_input_output_latency\x18\x03 \x01(\x08\x42\x02\x18\x01\x12\x17\n\x0f\x65nable_profiler\x18\x04 \x01(\x08\x12\x1d\n\x15\x65nable_stream_latency\x18\x05 \x01(\x08\x12-\n%use_packet_timestamp_for_added_packet\x18\x06 \x01(\x08\x12\x1a\n\x12trace_log_capacity\x18\x07 \x01(\x03\x12\"\n\x1atrace_event_types_disabled\x18\x08 \x03(\x05\x12\x16\n\x0etrace_log_path\x18\t \x01(\t\x12\x17\n\x0ftrace_log_count\x18\n \x01(\x05\x12\x1f\n\x17trace_log_interval_usec\x18\x0b \x01(\x03\x12\x1d\n\x15trace_log_margin_usec\x18\x0c \x01(\x03\x12%\n\x19trace_log_duration_events\x18\r \x01(\x08\x42\x02\x18\x01\x12 \n\x18trace_log_interval_count\x18\x0e \x01(\x05\x12\x1a\n\x12trace_log_disabled\x18\x0f \x01(\x08\x12\x15\n\rtrace_enabled\x18\x10 \x01(\x08\x12 \n\x18trace_log_instant_events\x18\x11 \x01(\x08\x12\x19\n\x11\x63\x61lculator_filter\x18\x12 \x01(\t\"\xf0\n\n\x15\x43\x61lculatorGraphConfig\x12\x33\n\x04node\x18\x01 \x03(\x0b\x32%.mediapipe.CalculatorGraphConfig.Node\x12\x36\n\x0epacket_factory\x18\x06 \x03(\x0b\x32\x1e.mediapipe.PacketFactoryConfig\x12:\n\x10packet_generator\x18\x07 \x03(\x0b\x32 .mediapipe.PacketGeneratorConfig\x12\x13\n\x0bnum_threads\x18\x08 \x01(\x05\x12\x36\n\x0estatus_handler\x18\t \x03(\x0b\x32\x1e.mediapipe.StatusHandlerConfig\x12\x14\n\x0cinput_stream\x18\n \x03(\t\x12\x15\n\routput_stream\x18\x0f \x03(\t\x12\x19\n\x11input_side_packet\x18\x10 \x03(\t\x12\x1a\n\x12output_side_packet\x18\x11 \x03(\t\x12\x16\n\x0emax_queue_size\x18\x0b \x01(\x05\x12\x17\n\x0freport_deadlock\x18\x15 \x01(\x08\x12\x41\n\x14input_stream_handler\x18\x0c \x01(\x0b\x32#.mediapipe.InputStreamHandlerConfig\x12\x43\n\x15output_stream_handler\x18\r \x01(\x0b\x32$.mediapipe.OutputStreamHandlerConfig\x12+\n\x08\x65xecutor\x18\x0e \x03(\x0b\x32\x19.mediapipe.ExecutorConfig\x12\x32\n\x0fprofiler_config\x18\x12 \x01(\x0b\x32\x19.mediapipe.ProfilerConfig\x12\x0f\n\x07package\x18\x13 \x01(\t\x12\x0c\n\x04type\x18\x14 \x01(\t\x12-\n\x07options\x18\xe9\x07 \x01(\x0b\x32\x1b.mediapipe.MediaPipeOptions\x12,\n\rgraph_options\x18\xea\x07 \x03(\x0b\x32\x14.google.protobuf.Any\x1a\xe6\x04\n\x04Node\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x12\n\ncalculator\x18\x02 \x01(\t\x12\x14\n\x0cinput_stream\x18\x03 \x03(\t\x12\x15\n\routput_stream\x18\x04 \x03(\t\x12\x19\n\x11input_side_packet\x18\x05 \x03(\t\x12\x1a\n\x12output_side_packet\x18\x06 \x03(\t\x12-\n\x07options\x18\x07 \x01(\x0b\x32\x1c.mediapipe.CalculatorOptions\x12*\n\x0cnode_options\x18\x08 \x03(\x0b\x32\x14.google.protobuf.Any\x12\x14\n\x0csource_layer\x18\t \x01(\x05\x12\x18\n\x10\x62uffer_size_hint\x18\n \x01(\x05\x12\x41\n\x14input_stream_handler\x18\x0b \x01(\x0b\x32#.mediapipe.InputStreamHandlerConfig\x12\x43\n\x15output_stream_handler\x18\x0c \x01(\x0b\x32$.mediapipe.OutputStreamHandlerConfig\x12\x35\n\x11input_stream_info\x18\r \x03(\x0b\x32\x1a.mediapipe.InputStreamInfo\x12\x10\n\x08\x65xecutor\x18\x0e \x01(\t\x12\x36\n\x0fprofiler_config\x18\x0f \x01(\x0b\x32\x19.mediapipe.ProfilerConfigB\x02\x18\x01\x12\x15\n\rmax_in_flight\x18\x10 \x01(\x05\x12\x14\n\x0coption_value\x18\x11 \x03(\t\x12\x17\n\x0e\x65xternal_input\x18\xed\x07 \x03(\tB-\n\x1a\x63om.google.mediapipe.protoB\x0f\x43\x61lculatorProtoP\x00\x62\x06proto3')
,
dependencies=[mediapipe_dot_framework_dot_calculator__options__pb2.DESCRIPTOR,google_dot_protobuf_dot_any__pb2.DESCRIPTOR,mediapipe_dot_framework_dot_mediapipe__options__pb2.DESCRIPTOR,mediapipe_dot_framework_dot_packet__factory__pb2.DESCRIPTOR,mediapipe_dot_framework_dot_packet__generator__pb2.DESCRIPTOR,mediapipe_dot_framework_dot_status__handler__pb2.DESCRIPTOR,mediapipe_dot_framework_dot_stream__handler__pb2.DESCRIPTOR,],
public_dependencies=[mediapipe_dot_framework_dot_calculator__options__pb2.DESCRIPTOR,])
_INPUTCOLLECTION_INPUTTYPE = _descriptor.EnumDescriptor(
name='InputType',
full_name='mediapipe.InputCollection.InputType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='UNKNOWN', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='RECORDIO', index=1, number=1,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FOREIGN_RECORDIO', index=2, number=2,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FOREIGN_CSV_TEXT', index=3, number=3,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INVALID_UPPER_BOUND', index=4, number=4,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=598,
serialized_end=705,
)
_sym_db.RegisterEnumDescriptor(_INPUTCOLLECTION_INPUTTYPE)
_EXECUTORCONFIG = _descriptor.Descriptor(
name='ExecutorConfig',
full_name='mediapipe.ExecutorConfig',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='mediapipe.ExecutorConfig.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='mediapipe.ExecutorConfig.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='options', full_name='mediapipe.ExecutorConfig.options', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=339,
serialized_end=429,
)
_INPUTCOLLECTION = _descriptor.Descriptor(
name='InputCollection',
full_name='mediapipe.InputCollection',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='mediapipe.InputCollection.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='side_packet_name', full_name='mediapipe.InputCollection.side_packet_name', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='external_input_name', full_name='mediapipe.InputCollection.external_input_name', index=2,
number=1002, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_type', full_name='mediapipe.InputCollection.input_type', index=3,
number=3, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='file_name', full_name='mediapipe.InputCollection.file_name', index=4,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_INPUTCOLLECTION_INPUTTYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=432,
serialized_end=705,
)
_INPUTCOLLECTIONSET = _descriptor.Descriptor(
name='InputCollectionSet',
full_name='mediapipe.InputCollectionSet',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='input_collection', full_name='mediapipe.InputCollectionSet.input_collection', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=707,
serialized_end=781,
)
_INPUTSTREAMINFO = _descriptor.Descriptor(
name='InputStreamInfo',
full_name='mediapipe.InputStreamInfo',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='tag_index', full_name='mediapipe.InputStreamInfo.tag_index', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='back_edge', full_name='mediapipe.InputStreamInfo.back_edge', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=783,
serialized_end=838,
)
_PROFILERCONFIG = _descriptor.Descriptor(
name='ProfilerConfig',
full_name='mediapipe.ProfilerConfig',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='histogram_interval_size_usec', full_name='mediapipe.ProfilerConfig.histogram_interval_size_usec', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='num_histogram_intervals', full_name='mediapipe.ProfilerConfig.num_histogram_intervals', index=1,
number=2, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='enable_input_output_latency', full_name='mediapipe.ProfilerConfig.enable_input_output_latency', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=_b('\030\001'), file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='enable_profiler', full_name='mediapipe.ProfilerConfig.enable_profiler', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='enable_stream_latency', full_name='mediapipe.ProfilerConfig.enable_stream_latency', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='use_packet_timestamp_for_added_packet', full_name='mediapipe.ProfilerConfig.use_packet_timestamp_for_added_packet', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_capacity', full_name='mediapipe.ProfilerConfig.trace_log_capacity', index=6,
number=7, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_event_types_disabled', full_name='mediapipe.ProfilerConfig.trace_event_types_disabled', index=7,
number=8, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_path', full_name='mediapipe.ProfilerConfig.trace_log_path', index=8,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_count', full_name='mediapipe.ProfilerConfig.trace_log_count', index=9,
number=10, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_interval_usec', full_name='mediapipe.ProfilerConfig.trace_log_interval_usec', index=10,
number=11, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_margin_usec', full_name='mediapipe.ProfilerConfig.trace_log_margin_usec', index=11,
number=12, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_duration_events', full_name='mediapipe.ProfilerConfig.trace_log_duration_events', index=12,
number=13, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=_b('\030\001'), file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_interval_count', full_name='mediapipe.ProfilerConfig.trace_log_interval_count', index=13,
number=14, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_disabled', full_name='mediapipe.ProfilerConfig.trace_log_disabled', index=14,
number=15, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_enabled', full_name='mediapipe.ProfilerConfig.trace_enabled', index=15,
number=16, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='trace_log_instant_events', full_name='mediapipe.ProfilerConfig.trace_log_instant_events', index=16,
number=17, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='calculator_filter', full_name='mediapipe.ProfilerConfig.calculator_filter', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=841,
serialized_end=1434,
)
_CALCULATORGRAPHCONFIG_NODE = _descriptor.Descriptor(
name='Node',
full_name='mediapipe.CalculatorGraphConfig.Node',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='mediapipe.CalculatorGraphConfig.Node.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='calculator', full_name='mediapipe.CalculatorGraphConfig.Node.calculator', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_stream', full_name='mediapipe.CalculatorGraphConfig.Node.input_stream', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_stream', full_name='mediapipe.CalculatorGraphConfig.Node.output_stream', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_side_packet', full_name='mediapipe.CalculatorGraphConfig.Node.input_side_packet', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_side_packet', full_name='mediapipe.CalculatorGraphConfig.Node.output_side_packet', index=5,
number=6, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='options', full_name='mediapipe.CalculatorGraphConfig.Node.options', index=6,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='node_options', full_name='mediapipe.CalculatorGraphConfig.Node.node_options', index=7,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='source_layer', full_name='mediapipe.CalculatorGraphConfig.Node.source_layer', index=8,
number=9, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='buffer_size_hint', full_name='mediapipe.CalculatorGraphConfig.Node.buffer_size_hint', index=9,
number=10, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_stream_handler', full_name='mediapipe.CalculatorGraphConfig.Node.input_stream_handler', index=10,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_stream_handler', full_name='mediapipe.CalculatorGraphConfig.Node.output_stream_handler', index=11,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_stream_info', full_name='mediapipe.CalculatorGraphConfig.Node.input_stream_info', index=12,
number=13, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='executor', full_name='mediapipe.CalculatorGraphConfig.Node.executor', index=13,
number=14, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='profiler_config', full_name='mediapipe.CalculatorGraphConfig.Node.profiler_config', index=14,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=_b('\030\001'), file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='max_in_flight', full_name='mediapipe.CalculatorGraphConfig.Node.max_in_flight', index=15,
number=16, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='option_value', full_name='mediapipe.CalculatorGraphConfig.Node.option_value', index=16,
number=17, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='external_input', full_name='mediapipe.CalculatorGraphConfig.Node.external_input', index=17,
number=1005, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2215,
serialized_end=2829,
)
_CALCULATORGRAPHCONFIG = _descriptor.Descriptor(
name='CalculatorGraphConfig',
full_name='mediapipe.CalculatorGraphConfig',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='node', full_name='mediapipe.CalculatorGraphConfig.node', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='packet_factory', full_name='mediapipe.CalculatorGraphConfig.packet_factory', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='packet_generator', full_name='mediapipe.CalculatorGraphConfig.packet_generator', index=2,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='num_threads', full_name='mediapipe.CalculatorGraphConfig.num_threads', index=3,
number=8, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='status_handler', full_name='mediapipe.CalculatorGraphConfig.status_handler', index=4,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_stream', full_name='mediapipe.CalculatorGraphConfig.input_stream', index=5,
number=10, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_stream', full_name='mediapipe.CalculatorGraphConfig.output_stream', index=6,
number=15, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_side_packet', full_name='mediapipe.CalculatorGraphConfig.input_side_packet', index=7,
number=16, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_side_packet', full_name='mediapipe.CalculatorGraphConfig.output_side_packet', index=8,
number=17, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='max_queue_size', full_name='mediapipe.CalculatorGraphConfig.max_queue_size', index=9,
number=11, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='report_deadlock', full_name='mediapipe.CalculatorGraphConfig.report_deadlock', index=10,
number=21, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='input_stream_handler', full_name='mediapipe.CalculatorGraphConfig.input_stream_handler', index=11,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='output_stream_handler', full_name='mediapipe.CalculatorGraphConfig.output_stream_handler', index=12,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='executor', full_name='mediapipe.CalculatorGraphConfig.executor', index=13,
number=14, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='profiler_config', full_name='mediapipe.CalculatorGraphConfig.profiler_config', index=14,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='package', full_name='mediapipe.CalculatorGraphConfig.package', index=15,
number=19, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='mediapipe.CalculatorGraphConfig.type', index=16,
number=20, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='options', full_name='mediapipe.CalculatorGraphConfig.options', index=17,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='graph_options', full_name='mediapipe.CalculatorGraphConfig.graph_options', index=18,
number=1002, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_CALCULATORGRAPHCONFIG_NODE, ],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1437,
serialized_end=2829,
)
_EXECUTORCONFIG.fields_by_name['options'].message_type = mediapipe_dot_framework_dot_mediapipe__options__pb2._MEDIAPIPEOPTIONS
_INPUTCOLLECTION.fields_by_name['input_type'].enum_type = _INPUTCOLLECTION_INPUTTYPE
_INPUTCOLLECTION_INPUTTYPE.containing_type = _INPUTCOLLECTION
_INPUTCOLLECTIONSET.fields_by_name['input_collection'].message_type = _INPUTCOLLECTION
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['options'].message_type = mediapipe_dot_framework_dot_calculator__options__pb2._CALCULATOROPTIONS
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['node_options'].message_type = google_dot_protobuf_dot_any__pb2._ANY
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['input_stream_handler'].message_type = mediapipe_dot_framework_dot_stream__handler__pb2._INPUTSTREAMHANDLERCONFIG
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['output_stream_handler'].message_type = mediapipe_dot_framework_dot_stream__handler__pb2._OUTPUTSTREAMHANDLERCONFIG
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['input_stream_info'].message_type = _INPUTSTREAMINFO
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['profiler_config'].message_type = _PROFILERCONFIG
_CALCULATORGRAPHCONFIG_NODE.containing_type = _CALCULATORGRAPHCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['node'].message_type = _CALCULATORGRAPHCONFIG_NODE
_CALCULATORGRAPHCONFIG.fields_by_name['packet_factory'].message_type = mediapipe_dot_framework_dot_packet__factory__pb2._PACKETFACTORYCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['packet_generator'].message_type = mediapipe_dot_framework_dot_packet__generator__pb2._PACKETGENERATORCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['status_handler'].message_type = mediapipe_dot_framework_dot_status__handler__pb2._STATUSHANDLERCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['input_stream_handler'].message_type = mediapipe_dot_framework_dot_stream__handler__pb2._INPUTSTREAMHANDLERCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['output_stream_handler'].message_type = mediapipe_dot_framework_dot_stream__handler__pb2._OUTPUTSTREAMHANDLERCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['executor'].message_type = _EXECUTORCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['profiler_config'].message_type = _PROFILERCONFIG
_CALCULATORGRAPHCONFIG.fields_by_name['options'].message_type = mediapipe_dot_framework_dot_mediapipe__options__pb2._MEDIAPIPEOPTIONS
_CALCULATORGRAPHCONFIG.fields_by_name['graph_options'].message_type = google_dot_protobuf_dot_any__pb2._ANY
DESCRIPTOR.message_types_by_name['ExecutorConfig'] = _EXECUTORCONFIG
DESCRIPTOR.message_types_by_name['InputCollection'] = _INPUTCOLLECTION
DESCRIPTOR.message_types_by_name['InputCollectionSet'] = _INPUTCOLLECTIONSET
DESCRIPTOR.message_types_by_name['InputStreamInfo'] = _INPUTSTREAMINFO
DESCRIPTOR.message_types_by_name['ProfilerConfig'] = _PROFILERCONFIG
DESCRIPTOR.message_types_by_name['CalculatorGraphConfig'] = _CALCULATORGRAPHCONFIG
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ExecutorConfig = _reflection.GeneratedProtocolMessageType('ExecutorConfig', (_message.Message,), dict(
DESCRIPTOR = _EXECUTORCONFIG,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.ExecutorConfig)
))
_sym_db.RegisterMessage(ExecutorConfig)
InputCollection = _reflection.GeneratedProtocolMessageType('InputCollection', (_message.Message,), dict(
DESCRIPTOR = _INPUTCOLLECTION,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.InputCollection)
))
_sym_db.RegisterMessage(InputCollection)
InputCollectionSet = _reflection.GeneratedProtocolMessageType('InputCollectionSet', (_message.Message,), dict(
DESCRIPTOR = _INPUTCOLLECTIONSET,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.InputCollectionSet)
))
_sym_db.RegisterMessage(InputCollectionSet)
InputStreamInfo = _reflection.GeneratedProtocolMessageType('InputStreamInfo', (_message.Message,), dict(
DESCRIPTOR = _INPUTSTREAMINFO,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.InputStreamInfo)
))
_sym_db.RegisterMessage(InputStreamInfo)
ProfilerConfig = _reflection.GeneratedProtocolMessageType('ProfilerConfig', (_message.Message,), dict(
DESCRIPTOR = _PROFILERCONFIG,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.ProfilerConfig)
))
_sym_db.RegisterMessage(ProfilerConfig)
CalculatorGraphConfig = _reflection.GeneratedProtocolMessageType('CalculatorGraphConfig', (_message.Message,), dict(
Node = _reflection.GeneratedProtocolMessageType('Node', (_message.Message,), dict(
DESCRIPTOR = _CALCULATORGRAPHCONFIG_NODE,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.CalculatorGraphConfig.Node)
))
,
DESCRIPTOR = _CALCULATORGRAPHCONFIG,
__module__ = 'mediapipe.framework.calculator_pb2'
# @@protoc_insertion_point(class_scope:mediapipe.CalculatorGraphConfig)
))
_sym_db.RegisterMessage(CalculatorGraphConfig)
_sym_db.RegisterMessage(CalculatorGraphConfig.Node)
DESCRIPTOR._options = None
_PROFILERCONFIG.fields_by_name['enable_input_output_latency']._options = None
_PROFILERCONFIG.fields_by_name['trace_log_duration_events']._options = None
_CALCULATORGRAPHCONFIG_NODE.fields_by_name['profiler_config']._options = None
# @@protoc_insertion_point(module_scope) 2294
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