I have successfully tested the example described in the github.
Being still a beginner, I would like to be able to test this example with a rtsp video stream. How can this be done? Is there any guide to be able to test this?
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This sample only support MP4 files with h264 video. deepstream_lpr_app/README.txt at master · NVIDIA-AI-IOT/deepstream_lpr_app · GitHub
You can refer to deepstream-app sample code for the rtsp source part.
I realized the the LPR can only recognize the rectangular license plate. How about the square license plate with 2 rows ? Can we re-train the model with a new dateset which contains two rows character images?
Currently, LPRnet does not support two lines of license plates.
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I am trying to follow the flow of this discussion and steps, but after splitting the initial data 80/20, This instruction fails: tlt detectnet_v2 dataset_convert -d /workspace/openalpr/SPECS_tfrecord.txt -o /workspace/openalpr/lpd_tfrecord/lpd
It seems to need docker and asks “Did you run docker login?” which may be assumed but not in the instruction flow.
Any help appreciated. Thanks.
To run LPDR (License Plate Detection and Recognition) i just need of jetson nano and camera borad V2 8MP (Raspberry cam)?
Jetson devices or the PCs with dgpu can run LPDR.
Can run inference with mp4 file.
I have a jetson nano and a camera board V2 8MP, with this i can run run Real-Time License Plate Detection and Recognition?.
For the fps, refer to https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app
Would you give us to improve the precision with using OpenALPR data?
I run the following command to start fine-tuning on the OpenALPR data in TLT container:
$ detectnet_v2 train -e /workspace/openalpr/SPECS_train.txt -r /workspace/openalpr/exp_unpruned -k nvidia_tlt
But it did not reach your reference accuracy. Here is our re-train result.
Epoch 120/120
=========================
Validation cost: 0.000082
Mean average_precision (in %): 63.5910
class name average precision (in %)
------------ --------------------------
lpd 63.591
Here is the train spce data.
random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/output/lpd/experiments/lpd_tfrecord/*"
image_directory_path: "/workspace/output/lpd/experiments/lpd/data/"
}
image_extension: "jpg"
target_class_mapping {
key: "lpd"
value: "lpd"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 640
output_image_height: 480
min_bbox_width: 1.0
min_bbox_height: 1.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "lpd"
value {
clustering_config {
coverage_threshold: 0.00499999988824
dbscan_eps: 0.20000000298
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 4
}
}
}
}
model_config {
pretrained_model_file: "/workspace/output/lpd/experiments/pretrained/usa_unpruned.tlt"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
training_precision {
backend_floatx: FLOAT32
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 1
minimum_detection_ground_truth_overlap {
key: "lpd"
value: 0.699999988079
}
evaluation_box_config {
key: "lpd"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "lpd"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 10.0
}
}
enable_autoweighting: true
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 4
num_epochs: 120
enable_qat: False
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "lpd"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 0.40000000596
cov_radius_y: 0.40000000596
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.400000154972
}
Best regards.
Kaka
How did you generate tfrecords? Since the openalpr only contains 222 images, the tfrecords generation take random images from it, it may result in different results via different tfrecords.
For improving mAP, you can try to run more epochs.
Here is the tfrecords file. I followed the blog.
kitti_config {
root_directory_path: "/workspace/output/lpd/experiments/lpd/data"
image_dir_name: "image"
label_dir_name: "label"
image_extension: ".jpg"
partition_mode: "random"
num_partitions: 2
val_split: 20
num_shards: 4
}
image_directory_path: "/workspace/output/lpd/experiments/lpd/data"
I increased the epoch sized from 120 to 240 but it seems that it is not effective.
Epoch 240/240
=========================
Validation cost: 0.000087
Mean average_precision (in %): 63.5292
class name average precision (in %)
------------ --------------------------
lpd 63.5292
Please create a new forum topic for discussing.
I see.
Hi there,
I need to use the Nvidia ALPR application with a USB camera not MP4 video file.
Has any one done this before? or knows the procedure on how to do this?
I looked into the app, it looks complicated and I don’t know how to change it to run on a USB camera.
Any help is greatly appreciated.
Best Regards,
Farough
I made. The topic name is:
Run Nvidia ALPR app on USB camera
Your assistance is appreciated.
Regards,
Farough
Did you create the topic in TAO forum? TAO Toolkit - NVIDIA Developer Forums
If not, could you share the link where you have created the topic?