Comments (4)
i have the same problem!!!
from alpr-unconstrained.
Hello! I am having the same issue. Did you find the solution?
from alpr-unconstrained.
+1
from alpr-unconstrained.
Hi,
This problem was solved in the closed issue #25 (comment)
Hope this helps!
I changed the code from python 2 to python 3 using
2to3
2to3 -w filename.py
Changed weights also in
vehicle-detection.py
line28
andlicense-plate-ocr.py
line28
according to ArgumentError in darknet.py after fixing syntax error #241When i run
bash run.sh -i samples/test -o /tmp/output -c /tmp/output/results.csv
I get no detection for car, I get his out
layer filters size input output 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BFLOPs 1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32 2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BFLOPs 3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64 4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BFLOPs 5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BFLOPs 6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BFLOPs 7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128 8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs 9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs 10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs 11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256 12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512 18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024 3.190 BFLOPs 24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024 3.190 BFLOPs 25 route 16 26 conv 64 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 64 0.044 BFLOPs 27 reorg / 2 26 x 26 x 64 -> 13 x 13 x 256 28 route 27 24 29 conv 1024 3 x 3 / 1 13 x 13 x1280 -> 13 x 13 x1024 3.987 BFLOPs 30 conv 125 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 125 0.043 BFLOPs 31 detection mask_scale: Using default '1.000000' Loading weights from data/vehicle-detector/yolo-voc.weights...Done! Searching for vehicles using YOLO... Scanning samples/test/03009.jpg 0 cars found Scanning samples/test/03016.jpg 0 cars found Scanning samples/test/03025.jpg 0 cars found Scanning samples/test/03033.jpg 0 cars found Scanning samples/test/03057.jpg 0 cars found Scanning samples/test/03058.jpg 0 cars found Scanning samples/test/03066.jpg 0 cars found Scanning samples/test/03071.jpg 0 cars found 2022-02-15 16:50:37.204997: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0. Searching for license plates using WPOD-NET layer filters size input output 0 conv 32 3 x 3 / 1 240 x 80 x 3 -> 240 x 80 x 32 0.033 BFLOPs 1 max 2 x 2 / 2 240 x 80 x 32 -> 120 x 40 x 32 2 conv 64 3 x 3 / 1 120 x 40 x 32 -> 120 x 40 x 64 0.177 BFLOPs 3 max 2 x 2 / 2 120 x 40 x 64 -> 60 x 20 x 64 4 conv 128 3 x 3 / 1 60 x 20 x 64 -> 60 x 20 x 128 0.177 BFLOPs 5 conv 64 1 x 1 / 1 60 x 20 x 128 -> 60 x 20 x 64 0.020 BFLOPs 6 conv 128 3 x 3 / 1 60 x 20 x 64 -> 60 x 20 x 128 0.177 BFLOPs 7 max 2 x 2 / 2 60 x 20 x 128 -> 30 x 10 x 128 8 conv 256 3 x 3 / 1 30 x 10 x 128 -> 30 x 10 x 256 0.177 BFLOPs 9 conv 128 1 x 1 / 1 30 x 10 x 256 -> 30 x 10 x 128 0.020 BFLOPs 10 conv 256 3 x 3 / 1 30 x 10 x 128 -> 30 x 10 x 256 0.177 BFLOPs 11 conv 512 3 x 3 / 1 30 x 10 x 256 -> 30 x 10 x 512 0.708 BFLOPs 12 conv 256 3 x 3 / 1 30 x 10 x 512 -> 30 x 10 x 256 0.708 BFLOPs 13 conv 512 3 x 3 / 1 30 x 10 x 256 -> 30 x 10 x 512 0.708 BFLOPs 14 conv 80 1 x 1 / 1 30 x 10 x 512 -> 30 x 10 x 80 0.025 BFLOPs 15 detection mask_scale: Using default '1.000000' Loading weights from data/ocr/ocr-net.weights...Done! Performing OCR... rm: cannot remove '/tmp/output/*_lp.png': No such file or directory --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) [<ipython-input-5-301d792980f0>](https://localhost:8080/#) in <module>() ----> 1 get_ipython().run_cell_magic('shell', '', '\nbash run.sh -i samples/test -o /tmp/output -c /tmp/output/results.csv') 2 frames [/usr/local/lib/python3.7/dist-packages/google/colab/_system_commands.py](https://localhost:8080/#) in check_returncode(self) 137 if self.returncode: 138 raise subprocess.CalledProcessError( --> 139 returncode=self.returncode, cmd=self.args, output=self.output) 140 141 def _repr_pretty_(self, p, cycle): # pylint:disable=unused-argument CalledProcessError: Command ' bash run.sh -i samples/test -o /tmp/output -c /tmp/output/results.csv' returned non-zero exit status 1.
What is going wrong here?
from alpr-unconstrained.
Related Issues (20)
- the model does not detect the LP if the picture was only the LP HOT 8
- The OCR operation only
- Does anyone ever upgraded the build environment? HOT 10
- Unable to load wpod model HOT 2
- using python2.7:vehicle-detection.py, line 28, vehicle_net = dn.load_net(vehicle_netcfg, vehicle_weights, 0) ctypes.ArgumentError: argument 1: wrong type # HOT 3
- Does someone ever tried to convert this to TensorFlow Lite to make it work on Android ? HOT 1
- about"bash get-networks.sh" HOT 1
- not detecting any cars from the sample images as well can it be because of python 3.7 ?? HOT 1
- affinex and affiney in loss.py
- training loss
- Explanation for detect_lp() two input params which are hardcoded
- 403 Forbidden for 'bash get-networks.sh' HOT 5
- Loss error HOT 1
- 'bash get-networks.sh' not working. HOT 1
- Website unreachable HOT 1
- List index out of range HOT 1
- License Plate Recognition in EVA multimedia database system
- Prediction Score
- Performance measurement
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