ZMeventnotification - cannot convert float NaN to integer

Discussion topics related to mobile applications and ZoneMinder Event Server (including machine learning)
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JonMoore
Posts: 26
Joined: Wed May 26, 2010 5:55 pm

ZMeventnotification - cannot convert float NaN to integer

Post by JonMoore » Mon Jun 14, 2021 3:27 pm

Hello!

Anyone had any issues getting this error before. I've been doing some housekeeping and updating and have broken my notifications server, worked through a lot of it with the excellent docs (and my previous posts to remind me of changes I needed to make for running in a TrueNAS jail)

Turning on debug logs and checking ZMDetect.log has turned this sequence up;

Code: Select all

06/14/21 16:15:31 zmesdetect[61260] DBG1 detect_sequence.py:627 [perf: Starting for frame:snapshot]
06/14/21 16:15:31 zmesdetect[61260] DBG1 detect_sequence.py:637 [Sequence of detection types to execute: ['object', 'face']]
06/14/21 16:15:31 zmesdetect[61260] DBG1 detect_sequence.py:642 [============ Frame: snapshot Running object detection type in sequence ==================]
06/14/21 16:15:31 zmesdetect[61260] DBG2 detect_sequence.py:163 [Loading sequence: index:0]
06/14/21 16:15:31 zmesdetect[61260] DBG2 detect_sequence.py:164 [Initializing model  type:object with options:{'object_config': '/usr/local/lib/zmeventnotification/models/yolov4/yolov4.cfg', 'object_weights': '/usr/local/lib/zmeventnotification/models/yolov4/yolov4.weights', 'object_labels': '/usr/local/lib/zmeventnotification/models/yolov4/coco.names', 'object_min_confidence': 0.3, 'object_framework': 'opencv', 'object_processor': 'cpu', 'disable_locks': 'yes'}]
06/14/21 16:15:31 zmesdetect[61260] DBG3 detect_sequence.py:662 [object has a same_model_sequence strategy of first]
06/14/21 16:15:31 zmesdetect[61260] DBG3 detect_sequence.py:674 [--------- Frame:snapshot Running variation: #1 -------------]
06/14/21 16:15:31 zmesdetect[61260] DBG1 yolo.py:83 [|--------- Loading "Yolo" model from disk -------------|]
06/14/21 16:15:31 zmesdetect[61260] DBG1 yolo.py:90 [perf: processor:cpu Yolo initialization (loading /usr/local/lib/zmeventnotification/models/yolov4/yolov4.weights model from disk) took: 5.17 ms]
06/14/21 16:15:31 zmesdetect[61260] DBG1 yolo.py:103 [Using CPU for detection]
06/14/21 16:15:31 zmesdetect[61260] DBG1 yolo.py:145 [|---------- YOLO (input image: 800w*600h, model resize dimensions: 416w*416h) ----------|]
06/14/21 16:15:32 zmesdetect[61260] DBG1 yolo.py:171 [perf: processor:cpu Yolo detection took: 1061.68 ms]
06/14/21 16:15:33 zmesdetect[61260] ERR detect_sequence.py:686 [Error running model: cannot convert float NaN to integer]
06/14/21 16:15:33 zmesdetect[61260] DBG2 detect_sequence.py:687 [Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/pyzm/ml/detect_sequence.py", line 683, in detect_stream
    _b,_l,_c,_m = m.detect(image=frame)
  File "/usr/local/lib/python3.8/site-packages/pyzm/ml/object.py", line 58, in detect
    b,l,c,_model_names = self.model.detect(image)
  File "/usr/local/lib/python3.8/site-packages/pyzm/ml/yolo.py", line 191, in detect
    center_x = int(detection[0] * Width)
ValueError: cannot convert float NaN to integer
seems like it's something to do with the image size being passed to the model, possibly some sort of variable type issue? I wondered if it was because the sizes have "w" and "h" with them?

Very grateful for any help!

Thanks,
Jon

tsp84
Posts: 82
Joined: Thu Dec 24, 2020 4:04 am

Re: ZMeventnotification - cannot convert float NaN to integer

Post by tsp84 » Mon Jun 14, 2021 7:27 pm

Try running it with disable locks = no
From what I can see it looks like it's failing when it tries to get a portalock and I see you have disable locks = yes.

Er wait nvm, it does seem to be when it's resizing .

JonMoore
Posts: 26
Joined: Wed May 26, 2010 5:55 pm

Re: ZMeventnotification - cannot convert float NaN to integer

Post by JonMoore » Tue Jun 15, 2021 1:37 pm

I thought it might be something in the config so I've posted my objectconfig.ini below... although it feels like it might be something else to me. I've disabled resize to see if that helps but didn't make a difference.

Code: Select all

# Configuration file for object detection

# NOTE: ALL parameters here can be overriden
# on a per monitor basis if you want. Just
# duplicate it inside the correct [monitor-<num>] section

# You can create your own custom attributes in the [custom] section

[general]

# Please don't change this. It is used by the config upgrade script
version=1.2

# You can now limit the # of detection process
# per target processor. If not specified, default is 1
# Other detection processes will wait to acquire lock

cpu_max_processes=3
tpu_max_processes=1
gpu_max_processes=1

# Time to wait in seconds per processor to be free, before
# erroring out. Default is 120 (2 mins)
cpu_max_lock_wait=100
tpu_max_lock_wait=100
gpu_max_lock_wait=100


#pyzm_overrides={'conf_path':'/etc/zm','log_level_debug':0}
pyzm_overrides={'conf_path':'/usr/local/etc'}

# This is an optional file
# If specified, you can specify tokens with secret values in that file
# and onlt refer to the tokens in your main config file
secrets = /usr/local/etc/zoneminder/secrets.ini

# portal/user/password are needed if you plan on using ZM's legacy
# auth mechanism to get images
portal=!ZM_PORTAL
user=!ZM_USER
password=!ZM_PASSWORD

# api portal is needed if you plan to use tokens to get images
# requires ZM 1.33 or above
api_portal=!ZM_API_PORTAL

allow_self_signed=yes
# if yes, last detection will be stored for monitors
# and bounding boxes that match, along with labels
# will be discarded for new detections. This may be helpful
# in getting rid of static objects that get detected
# due to some motion. 
match_past_detections=yes
# The max difference in area between the objects if match_past_detection is on
# can also be specified in px like 300px. Default is 5%. Basically, bounding boxes of the same
# object can slightly differ ever so slightly between detection. Contributor @neillbell put in this PR
# to calculate the difference in areas and based on his tests, 5% worked well. YMMV. Change it if needed.
# Note: You can specify label/object specific max_diff_areas as well. If present, they override this value
# example: 
# person_past_det_max_diff_area=5%
# car_past_det_max_diff_area=5000px
past_det_max_diff_area=5%

# this is the maximum size a detected object can have. You can specify it in px or % just like past_det_max_diff_area 
# This is pretty useful to eliminate bogus detection. In my case, depending on shadows and other lighting conditions, 
# I sometimes see "car" or "person" detected that covers most of my driveway view. That is practically impossible 
# and therefore I set mine to 70% because I know any valid detected objected cannot be larger than that area

max_detection_size=90%

# sequence of models to run for detection
detection_sequence=object,face,alpr
# if all, then we will loop through all models
# if first then the first success will break out
detection_mode=all

# If you need basic auth to access ZM 
#basic_user=user
#basic_password=password

# base data path for various files the ES+OD needs
# we support in config variable substitution as well
base_data_path=/usr/local/lib/zmeventnotification

# global settings for 
# bestmatch, alarm, snapshot OR a specific frame ID
frame_id=bestmatch

# this is the to resize the image before analysis is done
#resize=800
# set to yes, if you want to remove images after analysis
# setting to yes is recommended to avoid filling up space
# keep to no while debugging/inspecting masks
# Note this does NOT delete debug images later
delete_after_analyze=yes

# If yes, will write an image called <filename>-bbox.jpg as well
# which contains the bounding boxes. This has NO relation to 
# write_image_to_zm 
# Typically, if you enable delete_after_analyze you may
# also want to set  write_debug_image to no. 
write_debug_image=no

# if yes, will write an image with bounding boxes
# this needs to be yes to be able to write a bounding box
# image to ZoneMinder that is visible from its console
write_image_to_zm=yes


# Adds percentage to detections
# hog/face shows 100% always
show_percent=yes

# color to be used to draw the polygons you specified
poly_color=(255,255,255)
poly_thickness=2
import_zm_zones=yes
only_triggered_zm_zones=no

# This section gives you an option to get brief animations 
# of the event, delivered as part of the push notification to mobile devices
# Animations are created only if an object is detected
#
# NOTE: This will DELAY the time taken to send you push notifications
# It will try to first creat the animation, which may take upto a minute
# depending on how soon it gets access to frames. See notes below

[animation]

# If yes, object detection will attempt to create 
# a short GIF file around the object detection frame
# that can be sent via push notifications for instant playback
# Note this required additional software support. Default:no
create_animation=no

# Format of animation burst
# valid options are "mp4", "gif", "mp4,gif"
# Note that gifs will be of a shorter duration
# as they take up much more disk space than mp4
animation_types='mp4,gif'

# default width of animation image. Be cautious when you increase this
# most mobile platforms give a very brief amount of time (in seconds) 
# to download the image.
# Given your ZM instance will be serving the image, it will anyway be slow
# Making the total animation size bigger resulted in the notification not 
# getting an image at all (timed out)
animation_width=640

# When an event is detected, ZM it writes frames a little late
# On top of that, it looks like with caching enabled, the API layer doesn't
# get access to DB records for much longer (around 30 seconds), at least on my 
# system. animation_retry_sleep refers to how long to wait before trying to grab
# frame information if it failed. animation_max_tries defines how many times it 
# will try and retrieve frames before it gives up
animation_retry_sleep=15
animation_max_tries=4

# if animation_types is gif then when can generate a fast preview gif
# every second frame is skipped and the frame rate doubled
# to give quick preview, Default (no)
fast_gif=no

[remote]
# You can now run the machine learning code on a different server
# This frees up your ZM server for other things
# To do this, you need to setup https://github.com/pliablepixels/mlapi
# on your desired server and confiure it with a user. See its instructions
# once set up, you can choose to do object/face recognition via that 
# external serer

# URL that will be used
#ml_gateway=http://192.168.1.183:5000/api/v1
#ml_gateway=http://10.6.1.13:5000/api/v1
#ml_gateway=http://192.168.1.21:5000/api/v1
#ml_gateway=http://10.9.0.2:5000/api/v1
#ml_fallback_local=yes
# API/password for remote gateway
ml_user=!ML_USER
ml_password=!ML_PASSWORD


# config for object
[object]

# If you are using legacy format (use_sequence=no) then these parameters will 
# be used during ML inferencing
object_detection_pattern=(person)
object_min_confidence=0.3
object_framework=coral_edgetpu
object_processor=tpu
object_weights={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names

# If you are using the new ml_sequence format (use_sequence=yes) then 
# you can fiddle with these parameters and look at ml_sequence later
# Note that these can be named anything. You can add custom variables, ad-infinitum

# Google Coral
# The mobiledet model came out in Nov 2020 and is supposed to be faster and more accurate but YMMV
tpu_object_weights_mobiledet={{base_data_path}}/models/coral_edgetpu/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite
tpu_object_weights_mobilenet={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
tpu_object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names
tpu_object_framework=coral_edgetpu
tpu_object_processor=tpu
tpu_min_confidence=0.6

# Yolo v4 on GPU (falls back to CPU if no GPU)
yolo4_object_weights={{base_data_path}}/models/yolov4/yolov4.weights
yolo4_object_labels={{base_data_path}}/models/yolov4/coco.names
yolo4_object_config={{base_data_path}}/models/yolov4/yolov4.cfg
yolo4_object_framework=opencv
yolo4_object_processor=gpu

# Yolo v3 on GPU (falls back to CPU if no GPU)
yolo3_object_weights={{base_data_path}}/models/yolov3/yolov3.weights
yolo3_object_labels={{base_data_path}}/models/yolov3/coco.names
yolo3_object_config={{base_data_path}}/models/yolov3/yolov3.cfg
yolo3_object_framework=opencv
yolo3_object_processor=gpu

# Tiny Yolo V4 on GPU (falls back to CPU if no GPU)
tinyyolo_object_config={{base_data_path}}/models/tinyyolov4/yolov4-tiny.cfg
tinyyolo_object_weights={{base_data_path}}/models/tinyyolov4/yolov4-tiny.weights
tinyyolo_object_labels={{base_data_path}}/models/tinyyolov4/coco.names
tinyyolo_object_framework=opencv
tinyyolo_object_processor=gpu


[face]
face_detection_pattern=.*
known_images_path={{base_data_path}}/known_faces
unknown_images_path={{base_data_path}}/unknown_faces
save_unknown_faces=yes
save_unknown_faces_leeway_pixels=100
face_detection_framework=dlib

# read https://github.com/ageitgey/face_recognition/wiki/Face-Recognition-Accuracy-Problems
# read https://github.com/ageitgey/face_recognition#automatically-find-all-the-faces-in-an-image
# and play around

# quick overview: 
# num_jitters is how many times to distort images 
# upsample_times is how many times to upsample input images (for small faces, for example)
# model can be hog or cnn. cnn may be more accurate, but I haven't found it to be 

face_num_jitters=1
face_model=cnn
face_upsample_times=1

# This is maximum distance of the face under test to the closest matched
# face cluster. The larger this distance, larger the chances of misclassification.
#
face_recog_dist_threshold=0.6
# When we are first training the face recognition model with known faces,
# by default we use hog because we assume you will supply well lit, front facing faces
# However, if you are planning to train with profile photos or hard to see faces, you
# may want to change this to cnn. Note that this increases training time, but training only
# happens once, unless you retrain again by removing the training model
face_train_model=cnn
#if a face doesn't match known names, we will detect it as 'unknown face'
# you can change that to something that suits your personality better ;-)
#unknown_face_name=invader

[alpr]
alpr_detection_pattern=.*
alpr_use_after_detection_only=yes
# Many of the ALPR providers offer both a cloud version
# and local SDK version. Sometimes local SDK format differs from
# the cloud instance. Set this to local or cloud. Default cloud
alpr_api_type=cloud

# -----| If you are using plate recognizer | ------
alpr_service=plate_recognizer
#alpr_service=open_alpr_cmdline

# If you want to host a local SDK https://app.platerecognizer.com/sdk/
#alpr_url=http://192.168.1.21:8080/alpr
# Plate recog replace with your api key
alpr_key=!PLATEREC_ALPR_KEY
# if yes, then it will log usage statistics of the ALPR service
platerec_stats=yes
# If you want to specify regions. See http://docs.platerecognizer.com/#regions-supported
#platerec_regions=['us','cn','kr']
# minimal confidence for actually detecting a plate
platerec_min_dscore=0.1
# minimal confidence for the translated text
platerec_min_score=0.2


# ----| If you are using openALPR |-----
#alpr_service=open_alpr
#alpr_key=!OPENALPR_ALPR_KEY

# For an explanation of params, see http://doc.openalpr.com/api/?api=cloudapi
#openalpr_recognize_vehicle=1
#openalpr_country=us
#openalpr_state=ca
# openalpr returns percents, but we convert to between 0 and 1
#openalpr_min_confidence=0.3

# ----| If you are using openALPR command line |-----

openalpr_cmdline_binary=alpr

# Do an alpr -help to see options, plug them in here
# like say '-j -p ca -c US' etc.
# keep the -j because its JSON

# Note that alpr_pattern is honored
# For the rest, just stuff them in the cmd line options

openalpr_cmdline_params=-j -d
openalpr_cmdline_min_confidence=0.3


## Monitor specific settings


# Examples:
# Let's assume your monitor ID is 999
#[monitor-999]
# my driveway
#match_past_detections=no
#wait=5
#object_detection_pattern=(person)

# Advanced example - here we want anything except potted plant
# exclusion in regular expressions is not
# as straightforward as you may think, so 
# follow this pattern
# object_detection_pattern = ^(?!object1|object2|objectN)
# the characters in front implement what is 
# called a negative look ahead

# object_detection_pattern=^(?!potted plant|pottedplant|bench|broccoli)
#alpr_detection_pattern=^(.*x11)
#delete_after_analyze=no
#detection_pattern=.*
#import_zm_zones=yes

# polygon areas where object detection will be done.
# You can name them anything except the keywords defined in the optional
# params below. You can put as many polygons as you want per [monitor-<mid>]
# (see examples).

#my_driveway=306,356 1003,341 1074,683 154,715

# You are now allowed to specify detection pattern per zone
# the format is <polygonname>_zone_detection_pattern=<regexp>
# So if your polygon is called my_driveway, its associated
# detection pattern will be my_driveway_zone_detection_pattern
# If none is specified, the value in object_detection_pattern 
# will be used
# This also applies to ZM zones. Let's assume you have 
# import_zm_zones=yes and let's suppose you have a zone in ZM
# called Front_Door. In that case, all you need to do is put in a 
# front_door_zone_detection_pattern=(person|car) here
#
# NOTE: ZM Zones are converted to lowercase, and spaces are replaced
# with underscores@3

#my_driveway_zone_detection_pattern=(person)
#some_other_area=0,0 200,300 700,900
# use license plate recognition for my driveway
# see alpr section later for more data needed
#resize=no
#detection_sequence=object,alpr


[ml]
# When enabled, you can specify complex ML inferencing logic in ml_sequence
# Anything specified in ml_sequence will override any other ml attributes

# Also, when enabled, stream_sequence will override any other frame related
# attributes 
use_sequence = yes

# if enabled, will not grab exclusive locks before running inferencing
# locking seems to cause issues on some unique file systems
disable_locks= yes

# Chain of frames 
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence.detect_stream
# Very important: Make sure final ending brace is indented 
stream_sequence = {
		'frame_strategy': 'most_models',
		'frame_set': 'snapshot,alarm',
		'contig_frames_before_error': 5,
		'max_attempts': 3,
		'sleep_between_attempts': 4,
		#'resize':800

	}

# Chain of ML models to use
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence
# Very important: Make sure final ending brace is indented 
ml_sequence= {
		'general': {
			'model_sequence': 'object,face',
			'disable_locks': 'yes',
			'match_past_detections': 'yes',
			'past_det_max_diff_area': '5%',
			#'car_past_det_max_diff_area': '10%',
			#'ignore_past_detection_labels': ['dog', 'cat']

		},
		'object': {
			'general':{
				'pattern':'(person)',
				'same_model_sequence_strategy': 'first' # also 'most', 'most_unique's
			},
			'sequence': [{
                                # YoloV4 on GPU if TPU fails (because sequence strategy is 'first')
                                'object_config':'{{base_data_path}}/models/yolov4/yolov4.cfg',
                                'object_weights':'{{base_data_path}}/models/yolov4/yolov4.weights',
                                'object_labels': '{{base_data_path}}/models/yolov4/coco.names',				# YoloV4 on GPU if TPU fails (because sequence strategy is 'first')
				'object_min_confidence': 0.3,
                                'object_framework':'opencv',
                                'object_processor': 'cpu'

			}]
		},
		'face': {
			'general':{
				'pattern': '.*',				
				'same_model_sequence_strategy': 'union' # combines all outputs of this sequence
			},
			'sequence': [
			{
				'name': 'DLIB based face recognition',
				'enabled': 'yes',
				#'pre_existing_labels': ['face'], # If you use TPU detection first, we can run this ONLY if TPU detects a face first
				'save_unknown_faces':'yes',
				'unknown_images_path': '{{base_data_path}}/unknown_faces',
                                'face_detection_framework': 'dlib',
                                'known_images_path': '{{base_data_path}}/known_faces',
                                'face_model': 'cnn',
                                'face_train_model': 'cnn',
                                'face_recog_dist_threshold': 0.6,
                                'face_num_jitters': 1,
                                'face_upsample_times': 1
			}]
		}
	}



tsp84
Posts: 82
Joined: Thu Dec 24, 2020 4:04 am

Re: ZMeventnotification - cannot convert float NaN to integer

Post by tsp84 » Tue Jun 15, 2021 10:07 pm

Once I get home I'll sit down and try and help you out

User avatar
asker
Posts: 1510
Joined: Sun Mar 01, 2015 12:12 pm

Re: ZMeventnotification - cannot convert float NaN to integer

Post by asker » Wed Jun 16, 2021 10:05 am

Code: Select all

    _b,_l,_c,_m = m.detect(image=frame)
  File "/usr/local/lib/python3.8/site-packages/pyzm/ml/object.py", line 58, in detect
    b,l,c,_model_names = self.model.detect(image)
  File "/usr/local/lib/python3.8/site-packages/pyzm/ml/yolo.py", line 191, in detect
    center_x = int(detection[0] * Width)
ValueError: cannot convert float NaN to integer
Two things could be happening:
a) Width is invalid. This is unlikely, because your logs show the image was passed correctly

Code: Select all

06/14/21 16:15:31 zmesdetect[61260] DBG1 yolo.py:145 [|---------- YOLO (input image: 800w*600h, model resize dimensions: 416w*416h) ----------|]
and your code is not triggering any resize, if it did, resize would show in the logs. So it is not resize related either.


b) Your detections are getting messed up. That is, detection[0] doesn't exist. There could be several reasons:
b.1) Your config and model files did not download properly for yolov4 (possible, I've seen it some times)
b.2) You have disabed_locks=true, which can mess things up (more likely)

Enable the locks and try.
If that doesn't work, re-download the yolo models and config files
Please don't ask me questions via PM. Please post in these forums or GitHub.

Please read before posting:
How to set up logging properly
How to troubleshoot and report - ES
How to troubleshoot and report - zmNinja
ES docs
zmNinja docs

JonMoore
Posts: 26
Joined: Wed May 26, 2010 5:55 pm

Re: ZMeventnotification - cannot convert float NaN to integer

Post by JonMoore » Thu Jun 17, 2021 11:21 am

ah thanks for the help!

Tried setting the disable locks to no and it didn't make any difference.

Deleted the models folder and re-ran the install.sh to re-download them and everything just worked so must have been a corrupt model download.
Thanks so much

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