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Value error in tensorflow custom image detection

I am using tensorflow to detect custom images and i also trained a custom model for it but when i am running the detection scripts it is giving the following error
and this is my code-
import numpy as np
import argparse
import os
import tensorflow as tf
from PIL import Image
from io import BytesIO
import glob
import matplotlib.pyplot as plt
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1
# Patch the location of gfile
tf.gfile = tf.io.gfile
def load_model(model_path):
model = tf.saved_model.load(model_path)
return model
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: a file path (this can be local or on colossus)
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
img_data = tf.io.gfile.GFile(path, 'rb').read()
image = Image.open(BytesIO(img_data))
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def run_inference_for_single_image(model, image):
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
input_tensor = tf.convert_to_tensor(image)
# The model expects a batch of images, so add an axis with `tf.newaxis`.
input_tensor = input_tensor[tf.newaxis, ...]
# Run inference
output_dict = model(input_tensor)
# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(output_dict.pop('num_detections'))
output_dict = {key: value[0, :num_detections].numpy()
for key, value in output_dict.items()}
output_dict['num_detections'] = num_detections
# detection_classes should be ints.
output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
# Handle models with masks:
if 'detection_masks' in output_dict:
# Reframe the the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
output_dict['detection_masks'], output_dict['detection_boxes'],
image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5, tf.uint8)
output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
return output_dict
def run_inference(model, category_index, image_path):
if os.path.isdir(image_path):
image_paths = []
for file_extension in ('*.png', '*jpg'):
image_paths.extend(glob.glob(os.path.join(image_path, file_extension)))
for i_path in image_paths:
image_np = load_image_into_numpy_array(i_path)
# Actual detection.
output_dict = run_inference_for_single_image(model, image_np)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks_reframed', None),
use_normalized_coordinates=True,
line_thickness=8)
plt.imshow(image_np)
plt.show()
else:
image_np = load_image_into_numpy_array(image_path)
# Actual detection.
output_dict = run_inference_for_single_image(model, image_np)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks_reframed', None),
use_normalized_coordinates=True,
line_thickness=8)
plt.imshow(image_np)
plt.show()
if _name_ == '_main_':
parser = argparse.ArgumentParser(description='Detect objects inside webcam videostream')
parser.add_argument('-m', '--model', type=str, required=True, help='Model Path')
parser.add_argument('-l', '--labelmap', type=str, required=True, help='Path to Labelmap')
parser.add_argument('-i', '--image_path', type=str, required=True, help='Path to image (or folder)')
args = parser.parse_args()
detection_model = load_model(args.model)
category_index = label_map_util.create_category_index_from_labelmap(args.labelmap, use_display_name=True)
run_inference(detection_model, category_index, args.image_path)
i got this code from tannergilberts github repo and have been watching a yt video to do this but i dont know why i am getting the valueerror and i am not able to fix it also someone pls help

How can I fix "OSError: invalid face handle"?

I was following this tutorial: https://www.youtube.com/watch?v=pDXdlXlaCco
I got to the very last step, and I got OSError: invalid face handle. I tried looking around and found nothing about this. After following the trail, I found one line in ImageFont.py that was supposedly messing everything up: size, offset = self.font.getsize(text, "L", direction, features, language).
Here is the rest of my code, any help would be greatly appreciated:
import cv2
import numpy as np
import os
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import config_util
from object_detection.builders import model_builder
import tensorflow as tf
from object_detection.utils import config_util
from object_detection.protos import pipeline_pb2
from google.protobuf import text_format
WORKSPACE_PATH = 'Tensorflow/workspace'
SCRIPTS_PATH = 'Tensorflow/scripts'
APIMODEL_PATH = 'Tensorflow/models'
ANNOTATION_PATH = WORKSPACE_PATH+'/annotations'
IMAGE_PATH = WORKSPACE_PATH+'/images'
MODEL_PATH = WORKSPACE_PATH+'/models'
PRETRAINED_MODEL_PATH = WORKSPACE_PATH+'/pre-trained-models'
CONFIG_PATH = MODEL_PATH+'/my_ssd_mobnet/pipeline.config'
CHECKPOINT_PATH = MODEL_PATH+'/my_ssd_mobnet/'
CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
CONFIG_PATH = MODEL_PATH+'/'+CUSTOM_MODEL_NAME+'/pipeline.config'
# Load pipeline config and build a detection model
configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
detection_model = model_builder.build(model_config=configs['model'], is_training=False)
# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-11')).expect_partial()
#tf.function
def detect_fn(image):
image, shapes = detection_model.preprocess(image)
prediction_dict = detection_model.predict(image, shapes)
detections = detection_model.postprocess(prediction_dict, shapes)
return detections
cap = cv2.VideoCapture(0)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
category_index = label_map_util.create_category_index_from_labelmap(ANNOTATION_PATH+'/label_map.pbtxt')
while True:
ret, frame = cap.read()
image_np = np.array(frame)
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
detections = detect_fn(input_tensor)
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections
# detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
label_id_offset = 1
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes']+label_id_offset,
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=5,
min_score_thresh=.5,
agnostic_mode=False)
cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600)))
if cv2.waitKey(1) & 0xFF == ord('q'):
cap.release()
break
Edit: Here is the stack trace:
Traceback (most recent call last):
File "c:/Users/Rohan/Desktop/facial recognition/RealTimeObjectDetection-main/RealTimeObjectDetection-main/test.py", line 73, in <module>
agnostic_mode=False)
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\visualization_utils.py", line 1259, in visualize_boxes_and_labels_on_image_array
use_normalized_coordinates=use_normalized_coordinates)
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\visualization_utils.py", line 162, in draw_bounding_box_on_image_array
use_normalized_coordinates)
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\visualization_utils.py", line 219, in draw_bounding_box_on_image
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\visualization_utils.py", line 219, in <listcomp>
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python37\lib\site-packages\PIL\ImageFont.py", line 414, in getsize
size, offset = self.font.getsize(text, "L", direction, features, language)
OSError: invalid face handle```

Thank you all for you help! It turns out that my Pillow install was outdated, for some reason I didn't realize that was the module being used here. All I did was update it, that solved the issue.

Tensorflow object detection slow inference time

I need to measure the inference time for a TF1 object detection model. I have developed the following code by looking at various tutorials and at the official Tensorflow Github repository.
import numpy as np
import os
import sys
import tensorflow as tf
import time
from collections import defaultdict
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
%matplotlib inline
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)
import pathlib
tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)
# Enable GPU dynamic memory allocation
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# Initialize tf.Graph()
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile('/home/luigi/Downloads/SSD_MobileNet_300000/frozen_inference_graph.pb', 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Loads labels
label_map = label_map_util.load_labelmap('/home/luigi/Downloads/DOTA-3/train/Objects_label_map.pbtxt')
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=15, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Run Inference and populates results in a dict.
def run_inference(graph, image):
with graph.as_default():
with tf.Session() as sess:
ops = tf.get_default_graph().get_operations()
all_tensor_names = [output.name for op in ops for output in op.outputs]
tensor_dict = {}
tensor_keys = ['num_detections', 'detection_boxes', 'detection_scores', 'detection_classes']
for key in tensor_keys:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)
# Actual inference.
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
return output_dict
path = '/home/luigi/Downloads/test/'
#[os.path.join('/home/luigi/Downloads/test/', image_path)]
for image_path in os.listdir(path):
input_path = os.path.join(path, image_path)
print('Evaluating:', input_path)
image = Image.open(input_path)
img_width, img_height = image.size
image_np = np.array(image.getdata()).reshape((img_height, img_width, 3)).astype(np.uint8)
# Run inference.
start = time.time()
output_dict = run_inference(detection_graph, image_np)
end = time.time()
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=(12, 8))
plt.imshow(image_np)
print("Prediction time for: " + image_path + " " + str(end-start))
I think that for some reason related to the code above, the inference times that I get are slow and could be improved. I have been looking online but haven't had any luck finding anything useful. Any tips?

Custome object detection on webcame

I try to run the custom face detection model locally on ubuntu 20
but when I run the script I got this
Traceback (most recent call last):
File "webcam_detection.py", line 104, in <module>
cv2.imshow('object detection', cv2.resize(image_np, (1200,900)))
cv2.error: OpenCV(4.4.0) /tmp/pip-req-build-h2062vqd/opencv/modules/highgui/src/window.cpp:651: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'
Here is my script:
######## Object Detection for Image #########
#
# Author: Khai Do
# Date: 9/3/2019
## Some parts of the code is copied from Tensorflow object detection
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
# Import libraries
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import zipfile
import cv2
import tensorflow.compat.v1 as tf
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from utils import label_map_util
from utils import visualization_utils as vis_util
# Define the video stream
cap = cv2.VideoCapture(0) # Change only if you have more than one webcams
# What model
directPath = os.getcwd()
print(directPath)
MODEL_NAME = 'fine_tune_graph'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'object-detection.pbtxt')
# Number of classes to detect
NUM_CLASSES = 1
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Helper code
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
# Detection
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
while True:
# Read frame from camera
ret, image_np = cap.read()
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Extract image tensor
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Extract detection boxes
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Extract detection scores
scores = detection_graph.get_tensor_by_name('detection_scores:0')
# Extract detection classes
classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Extract number of detectionsd
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
# Display output
cv2.imshow('object detection', cv2.resize(image_np, (1200,900)))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break

google.protobuf.message.DecodeError: Wrong wire type in tag

I am building project on detection of floating garbage in water using raspberrypi4b. My model was trained on faster_rcnn. I am using tensorflow version 1.14. I followed https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi this tutorial.This is my code.
import os
import cv2
import numpy as np
from picamera.array import PiRGBArray
from picamera import PiCamera
import tensorflow as tf
import argparse
import sys
# Set up camera constants
IM_WIDTH = 1280
IM_HEIGHT = 720
#IM_WIDTH = 640 Use smaller resolution for
#IM_HEIGHT = 480 slightly faster framerate
# Select camera type (if user enters --usbcam when calling this script,
# a USB webcam will be used)
camera_type = 'picamera'
parser = argparse.ArgumentParser()
parser.add_argument('--usbcam', help='Use a USB webcam instead of picamera',
action='store_true')
args = parser.parse_args()
if args.usbcam:
camera_type = 'usb'
# This is needed since the working directory is the object_detection folder.
sys.path.append('..')
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'litter'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,'label_map.pbtxt')
# Number of classes the object detector can identify
NUM_CLASSES = 1
## Load the label map.
# Label maps map indices to category names, so that when the convolution
# network predicts `5`, we know that this corresponds to `airplane`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Initialize frame rate calculation
frame_rate_calc = 1
freq = cv2.getTickFrequency()
font = cv2.FONT_HERSHEY_SIMPLEX
# Initialize camera and perform object detection.
# The camera has to be set up and used differently depending on if it's a
# Picamera or USB webcam.
# I know this is ugly, but I basically copy+pasted the code for the object
# detection loop twice, and made one work for Picamera and the other work
# for USB.
### Picamera ###
if camera_type == 'picamera':
# Initialize Picamera and grab reference to the raw capture
camera = PiCamera()
camera.resolution = (IM_WIDTH,IM_HEIGHT)
camera.framerate = 10
rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))
rawCapture.truncate(0)
for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
t1 = cv2.getTickCount()
# Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
frame = np.copy(frame1.array)
frame.setflags(write=1)
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_expanded = np.expand_dims(frame_rgb, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.40)
cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
t2 = cv2.getTickCount()
time1 = (t2-t1)/freq
frame_rate_calc = 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
rawCapture.truncate(0)
camera.close()
### USB webcam ###
elif camera_type == 'usb':
# Initialize USB webcam feed
camera = cv2.VideoCapture(0)
ret = camera.set(3,IM_WIDTH)
ret = camera.set(4,IM_HEIGHT)
while(True):
t1 = cv2.getTickCount()
# Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
ret, frame = camera.read()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_expanded = np.expand_dims(frame_rgb, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.85)
cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
t2 = cv2.getTickCount()
time1 = (t2-t1)/freq
frame_rate_calc = 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
This is the error I am getting:
Traceback (most recent call last):
File "litter.py", line 85, in <module>
od_graph_def.ParseFromString(serialized_graph)
File "/home/pi/.local/lib/python3.7/site-packages/google/protobuf/message.py", line 187, in
ParseFromString
return self.MergeFromString(serialized)
File "/home/pi/.local/lib/python3.7/site-packages/google/protobuf/internal/python_message.py", line
1127, in MergeFromString
if self._InternalParse(serialized, 0, length) != length:
File "/home/pi/.local/lib/python3.7/site-packages/google/protobuf/internal/python_message.py", line
1181, in InternalParse
buffer, new_pos, wire_type) # pylint: disable=protected-access
File "/home/pi/.local/lib/python3.7/site-packages/google/protobuf/internal/decoder.py", line 973, in
_DecodeUnknownField
raise _DecodeError('Wrong wire type in tag.')
google.protobuf.message.DecodeError: Wrong wire type in tag.