Merge pull request '1' (#18) from shf_branch into master

2 years ago · bf05b57d24
parent 3946a6c93f 1f6f16d075
commit bf05b57d24
28 changed files with 2706 additions and 0 deletions
--- a/model/手语翻译.png
+++ b/model/手语翻译.png
--- a/model/界面流类表示.png
+++ b/model/界面流类表示.png
--- a/model/界面流顺序图.png
+++ b/model/界面流顺序图.png
--- a/model/自然语言翻译.png
+++ b/model/自然语言翻译.png
--- a/model/设置.png
+++ b/model/设置.png
--- a/src/p1/Image/demo.csv
+++ b/src/p1/Image/demo.csv
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--- a/src/p1/Image/train.py
+++ b/src/p1/Image/train.py
@ -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()))
--- a/src/p1/makedata/make_str.py
+++ b/src/p1/makedata/make_str.py
@ -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")
--- a/src/p1/makedata/makecsv.py
+++ b/src/p1/makedata/makecsv.py
@ -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()
--- a/src/p1/makedata/makexyz.py
+++ b/src/p1/makedata/makexyz.py
@ -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()
--- a/src/p1/makedata/random_csv.py
+++ b/src/p1/makedata/random_csv.py
@ -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)
--- a/src/p1/model/keras_metadata.pb
+++ b/src/p1/model/keras_metadata.pb
@ -0,0 +1,6 @@
+
+–%root"_tf_keras_sequential*ñ${"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "flatten_input"}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"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}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 11, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 252]}, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 252]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 252]}, "float32", "flatten_input"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 252]}, "float32", "flatten_input"]}, "keras_version": "2.10.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "flatten_input"}, "shared_object_id": 0}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 1}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 7}, {"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}]}}}2
+<EFBFBD>root.layer-0"_tf_keras_layer*æ{"name": "flatten", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 252]}, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 1, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 13}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 252]}}2
+‚root.layer_with_weights-0"_tf_keras_layer*Ë{"name": "dense", "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", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 252}}, "shared_object_id": 14}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 252]}}2
+„root.layer_with_weights-1"_tf_keras_layer*Í{"name": "dense_1", "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_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10]}}2
+†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
--- a/src/p1/model/saved_model.pb
+++ b/src/p1/model/saved_model.pb
--- a/src/p1/model/variables/variables.data-00000-of-00001
+++ b/src/p1/model/variables/variables.data-00000-of-00001
--- a/src/p1/model/variables/variables.index
+++ b/src/p1/model/variables/variables.index
--- a/src/p1/remote/Gramhands.py
+++ b/src/p1/remote/Gramhands.py
@ -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
--- a/src/p1/remote/pycache/Gramhands.cpython-310.pyc
+++ b/src/p1/remote/pycache/Gramhands.cpython-310.pyc
--- a/src/p1/remote/pycache/img_return.cpython-310.pyc
+++ b/src/p1/remote/pycache/img_return.cpython-310.pyc
--- a/src/p1/remote/demo.py
+++ b/src/p1/remote/demo.py
@ -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()
+
--- a/src/p1/remote/img_return.py
+++ b/src/p1/remote/img_return.py
@ -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)
--- a/src/p1/remote/listen.py
+++ b/src/p1/remote/listen.py
@ -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()
--- a/src/p1/remote/recv_img.py
+++ b/src/p1/remote/recv_img.py
@ -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()
--- a/src/p1/remote/return.py
+++ b/src/p1/remote/return.py
@ -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")
--- a/src/p1/remote/send.py
+++ b/src/p1/remote/send.py
@ -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("第"+str(i)+"帧, 共"+str(totalFrames)+"帧")
+
+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)
--- a/src/p1/remote/send_img.py
+++ b/src/p1/remote/send_img.py
@ -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()
--- a/src/p1/remote/track_3.py
+++ b/src/p1/remote/track_3.py
@ -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)
--- a/src/p1/tools/calculator_options_pb2.py
+++ b/src/p1/tools/calculator_options_pb2.py
@ -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)
--- a/src/p1/tools/calculator_pb2.py
+++ b/src/p1/tools/calculator_pb2.py
@ -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