How to use opencv-python with Darknet's YOLOv4?

Another post starts with you beautiful people 😊

Thank you all for messaging me your doubts about Darknet's YOLOv4. I am very happy to see in a very short amount of time my lovely aspiring data scientists have learned a state of the art object detection and recognition technique. If you are new to my blog and to computer vision then please check my following blog posts one by one-

Since now we have learned to use YOLOv4 built on Darknet's framework. In this post, I am going to share with you how can you use your trained YOLOv4 model with another awesome computer vision and machine learning software library- OpenCV and of course with Python 🐍. Yes, the Python wrapper of OpenCV library has just released it's latest version with support of YOLOv4 which you can install in your system using below command-

pip install opencv-python --upgrade

For Google Colab and Kaggle, use the following command-

!pip install opencv-python --upgrade

For example in my Colab notebook, the above command with output is like below-

As you can see in the last line of the output, the version of opencv-python is 4.4.0.40 which is, in fact, the latest one so our installation is done successfully. Now I will show you how can we test a pre-trained YOLOv4 model trained on coco dataset with Python and OpenCV-

Let's understand the above code snippet💭-

First I have imported the OpenCV library as cv and cv2_imshow library specific to Colab for opening image in Colab notebook.

In the next line, I have created an OpenCV model from the YOLOv4 configuration and pre-trained weights file. If you don't have these files then you can download those from this link. Here OpenCV's dnn_DetectionModel() function is all that you need for this purpose 💓

After creating the model, I did some input image preprocessing for this model like resizing the image to a specific resolution, normalizing, and maintaining the RGB format.

In the next line, I have uploaded a new unseen image where I want to detect some objects.

Next, I am iterating the names of the objects. Since our pre-trained model is trained on 80 classes known as the coco dataset, we need to take the names one by one from the file.

In the next line, I am using detect() function to detect the objects on the input image. This function is equivalent to the Darknet's detection function. It gives the object name, calculates the confidence score, and gives the bounding boxes.

Next, inside the for loop, I have written logic to put object name, confidence score, and draw a bounding box around the detected object using OpenCV inbuilt functions.

In the end, to show the image with the detection I am using the cv2_imshow() function. If you are using your own system then instead of this function use the OpenCV's imshow() function.

Now we are ready to test our image. Once I run the above code, I get the following image with the detection-

See the result, it's amazing right 💥. The pre-trained YOLOv4 model with OpenCV and Python is successfully able to detect person, wine glass, dining table, bowl, bottle with a high confidence score. In the same way, you can test your custom trained YOLOv4 model. You just need to replace YOLOv4 specific configuration file, weights, and class name file with your own, and the remaining code will be the same as it is. Let me show you the example where I have used my custom YOLOv4 model which I trained for a Global Wheat Head Detection Challenge. Since at the time of the participation OpenCV support was not released for Darknet's YOLOv4 so I had to convert my YOLOv4 model to keras/TensorFlow model and made changes accordingly. But now I can use it very easily 👲-

Wow, that was so easy 💪. For this competition, I had to put so much effort to make Darknet work in the Kaggle platform. But now with opencv-python, it is very easy for me. If you are interested to see my complete solution to the challenge you can check this link ☝.

That's it guys for today! From this post, you have learned to use your Darknet's YOLOv4 model with OpenCV and Python. Now there is no solid reason to not use this state of the art object detection and recognition neural network model in your project. The links I have shared at the starting of this blog and my solution link of a real-world object detection competition are quite enough to master in this field. So don't stop yourself 💥, try the learning from this post on your own, explore the code with experiments and in my next blog post, I will again share with you something new and exciting. Till then Go chase your dreams, have an awesome day, make every second count, and see you later in my next post😇