How to run TensorFlow Object Detection model on Jetson Nano | DLology

visualize.py

from IPython.display import Image as DisplayImage

# Boxes unit in pixels (image coordinates).
boxes_pixels = []
for i in range(num_detections):
    # scale box to image coordinates
    box = boxes[i] * np.array([image.shape[0],
                               image.shape[1], image.shape[0], image.shape[1]])
    box = np.round(box).astype(int)
    boxes_pixels.append(box)
boxes_pixels = np.array(boxes_pixels)

# Remove overlapping boxes with non-max suppression, return picked indexes.
pick = non_max_suppression(boxes_pixels, scores[:num_detections], 0.5)


for i in pick:
    box = boxes_pixels[i]
    box = np.round(box).astype(int)
    # Draw bounding box.
    image = cv2.rectangle(
        image, (box[1], box[0]), (box[3], box[2]), (0, 255, 0), 2)
    label = "{}:{:.2f}".format(int(classes[i]), scores[i])
    # Draw label (class index and probability).
    draw_label(image, (box[1], box[0]), label)

# Save and display the labeled image.
save_image(image[:, :, ::-1])
DisplayImage(filename="./data/img.png")