predict using trained model.py

#Write a function to choose one image randomly from your dataset and predict using Trained model.
def show_image_with_predictions(df, threshold=0.6):
  # choose a random image
  row = df.sample()
  filepath = row['fileName'].values[0]
  print("filepath:", filepath)

  # get all rows for this image
  df2 = df[df['fileName'] == filepath]
  im = np.array(Image.open(filepath))
  print("im.shape:", im.shape)

  # if there's a PNG it will have alpha channel
  im = im[:,:,:3]

  # plot true boxes
  for idx, row in df2.iterrows():
    box = [
      row['xmin'],
      row['ymin'],
      row['xmax'],
      row['ymax'],
    ]
    print(box)
    draw_box(im, box, color=(255, 0, 0))
  
  ### plot predictions ###

  # get predictions
  imp = preprocess_image(im)
  imp, scale = resize_image(im)

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

  # standardize box coordinates
  boxes /= scale

  # loop through each prediction for the input image
  for box, score, label in zip(boxes[0], scores[0], labels[0]):
    # scores are sorted so we can quit as soon
    # as we see a score below threshold
    if score < threshold:
      break

    box = box.astype(np.int32)
    color = label_color(label)
    draw_box(im, box, color=color)

    class_name = label_map[label]
    caption = f"{class_name} {score:.3f}"
    draw_caption(im, box, caption)
    score, label=score, label
  plt.axis('off')
  plt.imshow(im)
  plt.show()
  return score, label
plt.rcParams['figure.figsize'] = [20, 10]

#Feel free to change threshold as per your business requirement 
score, label=show_image_with_predictions(data, threshold=0.6)