Open neilyoung opened 5 months ago
I also see a possible ambiguity problem for German plates at least.
In your data set you have B EW 742
. This is recognized as
BEW742
Local Thresholding, with closing
LABEL: BEW742
mlp PREDICTION: B5W742
svm_rbf PREDICTION: BLW742
sgd PREDICTION: D5W742
kneighbors PREDICTION: BEW742
mlp_adam PREDICTION: B5W742
mlp_sgd PREDICTION: BEW742
svm_linear PREDICTION: B5W742
Plain BEW742
, without spaces, is simply ambiguous, because it could be a Berlin numberplate (B EW 742) or even a Beckum number plate (BE W 742). And there are many, many other ambiguities in German number plate recognition, if you not even detect the space between the first 1 to 3 letters and the rest.
Confused now: Tried this:
python predict.py -t /Users/decades/Documents/tmp/SimpleLPR/imgs/plates/BEW742.png
and it brought me the same "Can't segment...". But it was OK with python predict -a
Oh, that's crazy... You don't really support the provisioning of arbitrary images somewhere in the paths...
I copied my "auto5.png" as "BRD1891.png" into "./imgs/plates" and ran...
python predict.py -t BRD1891.png
And look what I've got:
Otsu Thresholding, with closing
LABEL: BRD1891
mlp PREDICTION: 5GD25V2
svm_rbf PREDICTION: BRD1B71
sgd PREDICTION: BGD2BV1
kneighbors PREDICTION: BRD1B21
mlp_adam PREDICTION: BRD2372
mlp_sgd PREDICTION: 5RD23R2
svm_linear PREDICTION: 5PD2572
What kind of higher nonsense is this? (besides the fact, that there is not even one correct prediction?)
First of all: Thanks for sharing.
I was running it under Python 3.10 and found these issues:
from sklearn.externals import joblib
failed all the time and was successfully replaced byimport joblib
.as_grey
is used. Needs to be.as_gray
Other than that: I was successfully predicting with "-a", but not with my own image, like this one or others:
I always got
Can't segment image
for all own images.EDIT: Came over the mlp training by increasing the limit to 300