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Realtime Object Detection Documentation.md

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Real-time-object-detection --

🤓Real-time object detection with deep learning and openCV. To build our deep learning-based real-time object detector with OpenCV we will require

  • access our webcam/video stream in an efficient manner and
  • apply object detection to each frame.

✔Import Libraries --

- from imutils.video import VideoStream
- from imutils.video import FPS
- import numpy as np
- import argparse
- import imutils
- import time
- import cv2

--prototext : The path to the Caffe prototxt file.
-- model : The path to the pre-trained model.
-- confidence : The minimum probability threshold to filter weak detections. The default is 20%.

For the example code + pre-trained Convolutional Neural Network --

$ python real_time_object_detection.py \
  --prototxt MobileNetSSD_deploy.prototxt.txt \
  --model MobileNetSSD_deploy.caffemodel
[INFO] loading model...
[INFO] starting video stream...
[INFO] elapsed time: 55.07
[INFO] approx. FPS: 6.54

follow the above steps to see realtime object detection.

  • To run the program in Jupyter notebook, comment the argparse segment and you dont have to run it everytime like in terminal. This works fine with Jupyter notebook.