# Decode and object detection
Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/decode-detection-display.html](https://docs.qualcomm.com/doc/80-70015-50/topic/decode-detection-display.html)
The **gst-decode-detect-display.py** uses a YOLOv8 TFLite model to identify the
object in a scene from a video stream, overlay the bounding boxes over the detected objects,
and then display the results.
## Use cases
1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html#python-sample-applications__section_gm5_s5j_bdc).
2. Run the object detection and encode
script:
python3 /usr/bin/gst-decode-detect-display.pyCopy to clipboard
The following are the default file paths in the Python
script:
INPUT_FILE = "/opt/data/1280_720_h264_30fps_1min.mp4"Copy to clipboard
MODEL_FILE = "/opt/data/YOLOv8-Detection-Quantized.tflite"Copy to clipboard
LABELS_FILE = "/opt/data/yolov8.labels"Copy to clipboard
## Expected output
Figure : Expected output for object detection
## Pipeline flow
Figure : Pipeline for decode and object detection
| Process | Description |
| --- | --- |
| **Decoding** | **Decoding** |
| filesrc | Reads the video data. |
| qtdemux | Demultiplexes the video data. |
| H264parse | Parses the H.264 video. |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70015-50/topic/v4l2h264dec.html) | Decodes the H.264 video from a file. |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvconverter.html) |
Receives the video stream on its sink pad.
Performs preprocessing:
Color conversion
Scaling down/up
Normalization on the stream data when the model expects the floating point values as an input
Converts the video stream to a tensor stream on its source pad.
The object detection model uses this tensor stream for inferencing.