yuhang2685 / LicensePlateRecognition-YOLOv4-TesseractOCR

YOLOv4 customizations including License Plate Recognition.

License Plate Recognition with YOLOv4 and Tesseract OCR

Getting Started

• Conda (Recommended)

# Tensorflow CPU
conda env create -f conda-cpu.yml
conda activate yolov4-cpu

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov4-gpu

• Pip

# TensorFlow CPU
pip install -r requirements.txt

# TensorFlow GPU
pip install -r requirements-gpu.txt

• Nvidia Driver (For GPU, if you are not using Conda Environment and haven't set up CUDA yet)

Make sure to use CUDA Toolkit version 10.1 as it is the proper version for the TensorFlow version used in this repository. https://developer.nvidia.com/cuda-10.1-download-archive-update2

Downloading Official Pre-trained Weights

YOLOv4 comes pre-trained and able to detect 80 classes. For easy demo purposes we will use the pre-trained weights. Download pre-trained yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Copy and paste yolov4.weights from your downloads folder into the 'data' folder of this repository.

If you want to use yolov4-tiny.weights, a smaller model that is faster at running detections but less accurate, download file here: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights

Using Custom Trained YOLOv4 Weights

Learn How To Train Custom YOLOv4 Weights here: https://www.youtube.com/watch?v=mmj3nxGT2YQ

Watch me Walk-Through using Custom Model in TensorFlow :https://www.youtube.com/watch?v=nOIVxi5yurE

USE MY LICENSE PLATE TRAINED CUSTOM WEIGHTS: https://drive.google.com/file/d/1EUPtbtdF0bjRtNjGv436vDY28EN5DXDH/view?usp=sharing

Copy and paste your custom .weights file into the 'data' folder and copy and paste your custom .names into the 'data/classes/' folder.

The only change within the code you need to make in order for your custom model to work is on line 14 of 'core/config.py' file. Update the code to point at your custom .names file as seen below. (my custom .names file is called custom.names but yours might be named differently)

Note: If you are using the pre-trained yolov4 then make sure that line 14 remains coco.names.

YOLOv4 Using Tensorflow (tf, .pb model)

To implement YOLOv4 using TensorFlow, first we convert the .weights into the corresponding TensorFlow model files and then run the model.

# Convert darknet weights to tensorflow
## yolov4
python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4 

# Run yolov4 tensorflow model
python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --images ./data/images/kite.jpg

# Run yolov4 on video
python detect_video.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --video ./data/video/video.mp4 --output ./detections/results.avi

# Run yolov4 on webcam
python detect_video.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --video 0 --output ./detections/results.avi

If you want to run yolov3 or yolov3-tiny change --model yolov3 and .weights file in above commands.

Note: You can also run the detector on multiple images at once by changing the --images flag like such --images "./data/images/kite.jpg, ./data/images/dog.jpg"

Custom YOLOv4 Using TensorFlow

The following commands will allow you to run your custom yolov4 model. (video and webcam commands work as well)

# custom yolov4
python save_model.py --weights ./data/custom.weights --output ./checkpoints/custom-416 --input_size 416 --model yolov4 

# Run custom yolov4 tensorflow model
python detect.py --weights ./checkpoints/custom-416 --size 416 --model yolov4 --images ./data/images/car.jpg

License Plate Recognition Using Tesseract OCR

I have created a custom function to feed Tesseract OCR the bounding box regions of license plates found by my custom YOLOv4 model in order to read and extract the license plate numbers. Thorough preprocessing is done on the license plate in order to correctly extract the license plate number from the image. The function that is in charge of doing the preprocessing and text extraction is called recognize_plate and can be found in the file core/utils.py.

Disclaimer: In order to run tesseract OCR you must first download the binary files and set them up on your local machine. Please do so before proceeding or commands will not run as expected!

Official Tesseract OCR Github Repo: tesseract-ocr/tessdoc

Great Article for How To Install Tesseract on Mac or Linux Machines: PyImageSearch Article

For Windows I recommend: Windows Install

Once you have Tesseract properly installed you can move onwards. If you don't have a trained YOLOv4 model to detect license plates feel free to use one that I have trained. It is not perfect but it works well. Download license plate detector model and learn how to save and run it with TensorFlow here

Running License Plate Recognition on Images (video example below)

The license plate recognition works wonders on images. All you need to do is add the --plate flag on top of the command to run the custom YOLOv4 model.

Try it out on this image in the repository!

# Run License Plate Recognition
python detect.py --weights ./checkpoints/custom-416 --size 416 --model yolov4 --images ./data/images/car2.jpg --plate

Resulting Image Example

The output from the above command should print any license plate numbers found to your command terminal as well as output and save the following image to the detections folder.

You should be able to see the license plate number printed on the screen above the bounding box found by YOLOv4.

Behind the Scenes

This section will highlight the steps I took in order to implement the License Plate Recognition with YOLOv4 and potential areas to be worked on further.

This demo will be showing the step-by-step workflow on the following original image.

First step of the process is taking the bounding box coordinates from YOLOv4 and simply taking the subimage region within the bounds of the box. Since this image is super small the majority of the time we use cv2.resize() to blow the image up 3x its original size.

Then we convert the image to grayscale and apply a small Gaussian blur to smooth it out.

Following this, the image is thresholded to white text with black background and has Otsu's method also applied. This white text on black background helps to find contours of image.

The image is then dilated using opencv in order to make contours more visible and be picked up in future step.

Next we use opencv to find all the rectangular shaped contours on the image and sort them left to right.

As you can see this causes many contours to be found other than just the contours of each character within the license plate number. In order to filter out the unwanted regions we apply a couple parameters to be met in order to accept a contour. These parameters are just height and width ratios (i.e. the height of region must be at least 1/6th of the total height of the image). A couple other parameters on area of region etc are also placed. Check out code to see exact details. This filtering leaves us with.

The individual characters of the license plate number are now the only regions of interest left. We segment each subimage and apply a bitwise_not mask to flip the image to black text on white background which Tesseract is more accurate with. The final step is applying a small median blur on the image and then it is passed to Tesseract to get the letter or number from it. Example of how letters look like when going to tesseract.

Each letter or number is then just appended together into a string and at the end you get the full license plate that is recognized! BOOM!

Running License Plate Recognition on Video

Running the license plate recognition straight on video at the same time that YOLOv4 object detections causes a few issues. Tesseract OCR is fairly expensive in terms of time complexity and slows down the processing of the video to a snail's pace. It can still be accomplished by adding the --plate command line flag to any detect_video.py commands.

However, I believe the best route to go is to run video detections without the plate flag and instead run them with --crop flag which crops the objects found on screen and saves them as new images. See how it works here Once the video is done processing at a higher FPS all the license plate images will be cropped and saved within detections/crop folder. I have added an easy script within the repository called license_plate_recognizer.py that you can run in order to recognize license plates. Plus this allows you to easily customize the script to further enhance any recognitions. I will be working on linking this functionality automatically in future commits to the repository.

Running License Plate Recognition with detect_video.py is done with the following command.

python detect_video.py --weights ./checkpoints/custom-416 --size 416 --model yolov4 --video ./data/video/license_plate.mp4 --output ./detections/recognition.avi --plate

The recommended route I think is more efficient is using this command. Customize the rate at which detections are cropped within the code itself.

python detect_video.py --weights ./checkpoints/custom-416 --size 416 --model yolov4 --video ./data/video/license_plate.mp4 --output ./detections/recognition.avi --crop

Now play around with license_plate_recognizer.py and have some fun!

Running Tesseract OCR on any Detections

I have also implemented a generic use of Tesseract OCR with YOLOv4. By enabling the flag --ocr with any detect.py image command you can search detections for text and extract what is found. Generic preprocessing is applied on the subimage that makes up the inside of the detection bounding box. However, so many lighting or color issues require advanced preprocessing so this function is by no means perfect. You will also need to install tesseract on your local machine prior to running this flag (see links and suggestions in above section)

Example command (note this image doesn't have text so will not output anything, just meant to show how command is structured):

python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --images ./data/images/dog.jpg --ocr

Command Line Args Reference

save_model.py:
  --weights: path to weights file
    (default: './data/yolov4.weights')
  --output: path to output
    (default: './checkpoints/yolov4-416')
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'False')
  --input_size: define input size of export model
    (default: 416)
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)

detect.py:
  --images: path to input images as a string with images separated by ","
    (default: './data/images/kite.jpg')
  --output: path to output folder
    (default: './detections/')
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'False')
  --weights: path to weights file
    (default: './checkpoints/yolov4-416')
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)
  --size: resize images to
    (default: 416)
  --iou: iou threshold
    (default: 0.45)
  --score: confidence threshold
    (default: 0.25)
  --count: count objects within images
    (default: False)
  --dont_show: dont show image output
    (default: False)
  --info: print info on detections
    (default: False)
  --crop: crop detections and save as new images
    (default: False)
    
detect_video.py:
  --video: path to input video (use 0 for webcam)
    (default: './data/video/video.mp4')
  --output: path to output video (remember to set right codec for given format. e.g. XVID for .avi)
    (default: None)
  --output_format: codec used in VideoWriter when saving video to file
    (default: 'XVID)
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './checkpoints/yolov4-416')
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)
  --size: resize images to
    (default: 416)
  --iou: iou threshold
    (default: 0.45)
  --score: confidence threshold
    (default: 0.25)
  --count: count objects within video
    (default: False)
  --dont_show: dont show video output
    (default: False)
  --info: print info on detections
    (default: False)
  --crop: crop detections and save as new images
    (default: False)

References

Huge shoutout goes to hunglc007 for creating the backbone of this repository:

• tensorflow-yolov4-tflite