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### Dependencies
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* Raspberry Pi:
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- Picamera
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* Computer
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* Computer:
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- Numpy
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- OpenCV
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- Pygame
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5. **Neural network testing:** Run *“mlp_predict_test.py”* to load testing data from “testing_data” folder and trained parameters from the xml file in “mlp_xml” folder
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6. **Cascade classifiers training (optional):** trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested training your own classifiers, please refer to [OpenCV documentation](http://docs.opencv.org/doc/user_guide/ug_traincascade.html) and [this great tutorial by Thorsten Ball](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html)
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6. **Cascade classifiers training (optional):** trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested in training your own classifiers, please refer to [OpenCV documentation](http://docs.opencv.org/doc/user_guide/ug_traincascade.html) and [this great tutorial by Thorsten Ball](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html)
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7. **Self-driving in action**: First run *“rc_driver.py”* to start the server on the computer and then run *“stream_client.py”* and *“ultrasonic_client.py”* on raspberry pi.
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