DetectObjects.py

#run detector on test image
#it takes a little longer on the first run and then runs at normal speed. 
import random

#Define utility functions for presenting the results:
def load_image_into_numpy_array(path):
  """Load an image from file into a numpy array.
  Puts image into numpy array to feed into tensorflow graph.
  Note that by convention we put it into a numpy array with shape
  (height, width, channels), where channels=3 for RGB.
  Args:
    path: the file path to the image
  Returns:
    uint8 numpy array with shape (img_height, img_width, 3)
  """
  img_data = tf.io.gfile.GFile(path, 'rb').read()
  image = Image.open(BytesIO(img_data))
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


#Place your test images here:
image_path = '/content/test/mnm.jpeg'

#Store test images in nmpy array:
image_np = load_image_into_numpy_array(image_path)

#Convert images to tensor form:
input_tensor = tf.convert_to_tensor(
    np.expand_dims(image_np, 0), dtype=tf.float32)

#Perform detection on the image in tensor format:
detections, predictions_dict, shapes = detect_fn(input_tensor)

#Visualize the detection boxes on the image:
label_id_offset = 1
image_np_with_detections = image_np.copy()

viz_utils.visualize_boxes_and_labels_on_image_array(
      image_np_with_detections,
      detections['detection_boxes'][0].numpy(),
      (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
      detections['detection_scores'][0].numpy(),
      category_index,
      use_normalized_coordinates=True,
      max_boxes_to_draw=200,
      min_score_thresh=0.5,#0.5,#0.5
      agnostic_mode=False,
)

plt.figure(figsize=(12,16))
plt.imshow(image_np_with_detections)
plt.show()