FabianGroeger96 / cnn-number-detection

Number detection implemented using TensorFlow with custom CNN architecture for fast inference and custom dataset

CNN number detection

Table of Contents

1. Project
2. Example Images
3. Installation
4. Usage
5. Additional Remarks

Project

The goal of this repository is to implement a number detection using Tensorflow with a custom Convolution Neural Net (CNN) architecture specifically for fast inference. The CNN will be trained using a custom created dataset that contains numbers from 1-9 and a category for 'not numbers (-1)'.

The CNN is fast enough to run in real-time on a Raspberry Pi.

Specs

• Image size (input to the model): 320 x 240
• Camera fps: 38
• Processing time (with isolator): 25ms - 45ms (on Raspberry Pi)

Download links

• trained models
• dataset
• example images for isolator

Includes

• DataExtractor (to extract data for the dataset)
• Isolator (to find the numbers inside an image)
• Trainer (to train the model)
• Tester (to test the model)

Example Images

Example input images

Example extracted images (dataset images)

Installation

Requirements

You need to have the following packages installed (check requirements.txt):

• Python 3.6
• Tensorflow 1.4.1+
• OpenCV 4.0
• Etc.

Install

Clone the repo and install 3rd-party libraries

$ git clone https://github.com/FabianGroeger96/cnn-number-detection
$ cd cnn-number-detection
$ pip3 install -r requirements.txt

Usage

Extract data with DataExtractor

1. Create a folder named images_to_extract in the data extractor directory (The folder can be named differently, but don't forget to change the INPUT_DATA_DIR variable in the constants.py file). This directory will be used to extract the regions of interest to train your CNN.
2. Copy the data to extract the regions of interest into the images_to_extract folder
3. Specify which categories you want to extract in the constants.py file, by changing the CATEGORIES variable
4. Run the extract_data.py file and call the method extract_data() from the Extractor
5. After the method is finished your extracted regions of interest are located in the data_extracted folder. In there you will also find folders for each of your categories. These folders are used to label the regions of interest for then training your CNN.

Label the Data (by Hand)

1. First of all you will have to extract the regions of interest with the DataExtractor (follow the step Extract data with DataExtractor)
2. Classify the images, by dragging them in the corresponding category folder

Label the Data (with existing Model)

1. First of all you will have to extract the regions of interest with the DataExtractor (follow the step Extract data with DataExtractor)
2. Specify in the constants.py file where your model will be located, by modifying the MODEL_DIR constant
3. Place your existing model in the directory that you specified before
4. Run the extract_data.py file and call the method categorize_with_trained_model(), this will categorize your regions of interest
5. Verify that the data was labeled correctly, by checking the data_extracted folder

Create dataset pickle files

1. If you are finished labeling the images, run the extract_data.py file and call the method rename_images_in_categories() from the Extractor, this will rename the images in each category
2. Run the extract_data.py file and call the method create_training_data(), this will create your pickle files (X.pickle and y.pickle) which contain the data and the labels of your data

Train the CNN

1. Check if the pickle files (X.pickle and y.pickle) were created in the root directory of the project
2. Run the train_model.py file within the trainer, this will train your model and save it to the directory specified in the constants.py (MODEL_DIR)

Test the CNN

usage: test_model.py [-h] [--model_type MODEL_TYPE] [--model_path MODEL_PATH] [--test_image TEST_IMAGE]
                     [--test_folder TEST_FOLDER] [--test_on_random]

cnn-number-detection

optional arguments:
  -h, --help                 show this help message and exit
  --model_type MODEL_TYPE    type of model to use
  --model_path MODEL_PATH    path to the saved model
  --test_image TEST_IMAGE    path to the image for testing the model
  --test_folder TEST_FOLDER  folder with images for inference
  --test_on_random           if a randomly generated image should be used for inference

1. Check if the model is in the directory specified in constants.py (MODEL_DIR)
2. Upload a testing image or a folder full of test images to the tester directory
3. Specify the correct model type (--model_type) and model path (--model_path)
4. Run inference
   1. Run the test_model.py file within the Tester by specifying the image with --test_image
   2. Run the test_model.py file within the Tester by specifying the folder with --test_folder
   3. Run the test_model.py file within the Tester on a random image with --test_on_random

Additional Remarks

Command to create a video out of a bunch of images.

 convert *.jpg recognized.mpeg