Jetson Nano TensorFlow Camera Classification” : “How to Get Started with NVIDIA Jetson Nano: Install Keras + TensorFlow, Access Camera, Image Classification & Object Detection

1. “Step-by-step tutorial for installing Keras + TensorFlow on NVIDIA Jetson Nano”
2. “Image classification and object detection guide for NVIDIA Jetson Nano using Keras + TensorFlow”.

In this tutorial, we will walk you through the steps to get started with your NVIDIA Jetson Nano. We will cover the installation of Keras + TensorFlow, accessing the camera, and performing image classification and object detection. By the end of this tutorial, you will have a solid understanding of how to utilize the powerful capabilities of your Jetson Nano.

To begin, let’s start with the installation process. The Jetson Nano comes with a pre-installed version of TensorFlow, but we will update it to the latest version and install Keras. First, open a terminal on your Jetson Nano and run the following commands:

“`
pip3 install –pre –extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow
pip3 install keras
“`

This will install the latest version of TensorFlow and Keras on your Jetson Nano. Once the installation is complete, we can move on to accessing the camera.

The Jetson Nano has a CSI camera connector, which allows you to connect a compatible camera module directly to the board. To access the camera, we will use the OpenCV library. OpenCV provides a simple and efficient way to work with images and videos. To install OpenCV, run the following command:

“`
pip3 install opencv-python
“`

With OpenCV installed, we can now write a Python script to capture images from the camera. Create a new file named `camera.py` and open it in your favorite text editor. Copy and paste the following code into the file:

“`python
import cv2

cap = cv2.VideoCapture(0)

while True:
ret, frame = cap.read()

cv2.imshow(‘Camera’, frame)

if cv2.waitKey(1) & 0xFF == ord(‘q’):
break

cap.release()
cv2.destroyAllWindows()
“`

This script will continuously capture frames from the camera and display them in a window until you press the ‘q’ key to exit. Save the file and run it using the following command:

“`
python3 camera.py
“`

You should now see the live camera feed displayed in a window on your screen. Press the ‘q’ key to exit the script.

Now that we have successfully accessed the camera, let’s move on to performing image classification and object detection. For this, we will use pre-trained models available in the Keras library.

To perform image classification, we will use a pre-trained model called MobileNet. MobileNet is a lightweight model that is suitable for running on devices with limited computational resources. To install it, run the following command:

“`
pip3 install keras_applications
“`

With MobileNet installed, we can write a Python script to perform image classification on the captured frames. Create a new file named `image_classification.py` and open it in your text editor. Copy and paste the following code into the file:

“`python
import cv2
from keras.applications.mobilenet import MobileNet, preprocess_input, decode_predictions

cap = cv2.VideoCapture(0)

model = MobileNet()

while True:
ret, frame = cap.read()

resized_frame = cv2.resize(frame, (224, 224))
preprocessed_frame = preprocess_input(resized_frame)

predictions = model.predict(preprocessed_frame.reshape(1, 224, 224, 3))
decoded_predictions = decode_predictions(predictions)

cv2.putText(frame, decoded_predictions[0][0][1], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow(‘Camera’, frame)

if cv2.waitKey(1) & 0xFF == ord(‘q’):
break

cap.release()
cv2.destroyAllWindows()
“`

This script uses the MobileNet model to classify the captured frames. The top prediction is displayed as text on the frame. Save the file and run it using the following command:

“`
python3 image_classification.py
“`

You should now see the live camera feed with the top prediction displayed on each frame. Press the ‘q’ key to exit the script.

Next, let’s move on to object detection. For this, we will use a pre-trained model called YOLO (You Only Look Once). YOLO is a real-time object detection system that is capable of detecting multiple objects in an image. To install it, run the following command:

“`
pip3 install keras_retinanet
“`

With YOLO installed, we can write a Python script to perform object detection on the captured frames. Create a new file named `object_detection.py` and open it in your text editor. Copy and paste the following code into the file:

“`python
import cv2
from keras_retinanet import models
from keras_retinanet.utils.image import preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption

cap = cv2.VideoCapture(0)

model = models.load_model(‘resnet50_coco_best_v2.1.0.h5′, backbone_name=’resnet50’)

while True:
ret, frame = cap.read()

resized_frame = cv2.resize(frame, (800, 600))
preprocessed_frame = preprocess_image(resized_frame)
preprocessed_frame, scale = resize_image(preprocessed_frame)

boxes, scores, labels = model.predict_on_batch(np.expand_dims(preprocessed_frame, axis=0))

for box, score, label in zip(boxes[0], scores[0], labels[0]):
if score < 0.5: break box /= scale draw_box(frame, box.astype(int)) caption = f'{label}: {score:.2f}' draw_caption(frame, box.astype(int), caption) cv2.imshow('Camera', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` This script uses the YOLO model to detect objects in the captured frames. The bounding boxes and labels are drawn on the frame. Save the file and run it using the following command: ``` python3 object_detection.py ``` You should now see the live camera feed with bounding boxes and labels drawn around the detected objects. Press the 'q' key to exit the script. In this tutorial, you have learned how to get started with your NVIDIA Jetson Nano. We covered the installation of Keras + TensorFlow, accessing the camera, and performing image classification and object detection. With this knowledge, you can now explore and experiment with the capabilities of your Jetson Nano. Happy coding!. Source : @ryanwellsr

In this tutorial, you will learn how to get started with your NVIDIA Jetson Nano, including installing Keras + TensorFlow, accessing the camera, and performing image classification and object detection.https://t.co/WpKZMjfuS4

— Ryan Wells (@ryanwellsr) December 11, 2023

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1. “NVIDIA Jetson Nano image classification tutorial”
2. “Keras TensorFlow tutorial for NVIDIA Jetson Nano”.