This repo uses YOLO version 2 with Keras to detect objects in videos.

• Give a video as the input
• Break the video into Frames
• For simplicity, we are taking 0.5 frames per second
• Divide each frame(image) using grids
• Perform image classification and Localization on each grid cell -> Result, a vector for each cell representing the probability of an object detected, the dimensions of the bounding box and class of the detected image.
• Perform thresholding to remove multiple detected instances
• Perform Non-max suppression to refine the boxes more
• Additionally anchor boxes are used to detect several objects in one grid cell
• Combine the processed images (again with 0.5 frames per second) and make a video

• Clone this repository to your PC

• Create your virtual environment virtualenv object_det

• Activate your virtual environment source object_det/bin/activate

• Install dependencies from requirement.txt pip install --user --requirement requirements.txt

• Install OpenCV

• Place your video in the master directory

• Change the input/output paths specific to your system

• Download pre-trained YOLO cfg and weights files from https://pjreddie.com/darknet/yolo/

• Convert the dowloaded cfg and weights files into a h5 file using YAD2K library. (This is explained step by step below in the more details section)

• Copy the generated h5 file to the model_data folder and edit the name of the pretrained model in yolo.py code to the name of your h5 file.

• Assign your input image file name to input_image_name variable in yolo_video.py.

• Open terminal from the repository directory directly and run the yolo_video.py file

  python yolo_video.py

How to convert cfg and weights files to h5 using YAD2k library (Windows)

• Clone the YAD2K Library to your PC
• Open terminal from the cloned directory
• Copy and paste the downloaded weights and cfg files to the YAD2K master directory
• Run python yad2k.py yolo.cfg yolo.weights model_data/yolo.h5 on the terminal and the h5 file will be generated.
• Move the generated h5 file to model_data folder of the simpleYOLOwKeras directory
• I have used YOLOv2 608x608

Use python yolo.py command for detecting objects in a Image