How to classify objects using Jetson Nano inference and OpenCV ?

### Import OpenCV for image I/O and window display.
import cv2
### Import Jetson Inference high level APIs for classification.
import jetson.inference
### Import Jetson Utils for CUDA image handling and conversions.
import jetson.utils

### This code is tested with Python 3.6 on Jetson Nano for compatibility with Jetson packages.
# Use Python 3.6

### Load an input image from disk into a NumPy array in BGR format as used by OpenCV.
img = cv2.imread('/home/feitdemo/github/Jetson-Nano-Python/dog-demo.jpg')

### Convert the BGR OpenCV image to RGBA because Jetson utilities expect RGBA when using cudaFromNumpy.
frame_rgba = cv2.cvtColor(img,cv2.COLOR_BGR2RGBA)
### Upload the RGBA NumPy array to GPU memory as a CUDA image so it can be processed by the network.
cude_frame = jetson.utils.cudaFromNumpy(frame_rgba)

### Create an image classification network using the pretrained GoogleNet model for fast inference on Jetson.
net = jetson.inference.imageNet("googlenet")

### Run classification on the CUDA image and return the predicted class index and confidence score.
class_id , confidence = net.Classify(cude_frame)

### Translate the numeric class ID into a human readable label for display or logging.
class_desc = net.GetClassDesc(class_id)

### Draw the predicted class text onto the original OpenCV image so the result is visible.
cv2.putText(img,class_desc,(30,80), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0),4 )

### Show the image in a window using OpenCV so you can verify the prediction visually.
cv2.imshow('img',img)
### Move the window to the top left corner for consistent on-screen positioning during demos.
cv2.moveWindow('img',0,0) # position of the windows and the left corner
### Block the script until a key is pressed so the window stays open to review results.
cv2.waitKey()

### Import OpenCV for image I/O and window display. import cv2 ### Import Jetson Inference high level APIs for classification. import jetson.inference ### Import Jetson Utils for CUDA image handling and conversions. import jetson.utils  ### This code is tested with Python 3.6 on Jetson Nano for compatibility with Jetson packages. # Use Python 3.6  ### Load an input image from disk into a NumPy array in BGR format as used by OpenCV. img = cv2.imread('/home/feitdemo/github/Jetson-Nano-Python/dog-demo.jpg')  ### Convert the BGR OpenCV image to RGBA because Jetson utilities expect RGBA when using cudaFromNumpy. frame_rgba = cv2.cvtColor(img,cv2.COLOR_BGR2RGBA) ### Upload the RGBA NumPy array to GPU memory as a CUDA image so it can be processed by the network. cude_frame = jetson.utils.cudaFromNumpy(frame_rgba)  ### Create an image classification network using the pretrained GoogleNet model for fast inference on Jetson. net = jetson.inference.imageNet("googlenet")  ### Run classification on the CUDA image and return the predicted class index and confidence score. class_id , confidence = net.Classify(cude_frame)  ### Translate the numeric class ID into a human readable label for display or logging. class_desc = net.GetClassDesc(class_id)  ### Draw the predicted class text onto the original OpenCV image so the result is visible. cv2.putText(img,class_desc,(30,80), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0),4 )  ### Show the image in a window using OpenCV so you can verify the prediction visually. cv2.imshow('img',img) ### Move the window to the top left corner for consistent on-screen positioning during demos. cv2.moveWindow('img',0,0) # position of the windows and the left corner ### Block the script until a key is pressed so the window stays open to review results. cv2.waitKey()