categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
inference = Inference()
sess = inference.get_model_session(PATH_TO_CKPT)
# Input tensor is the image.
image_tensor = inference.get_input_tensor()
# Output tensors are the detection boxes, scores, and classes.
detection_boxes = inference.get_output_tensor()
# Each score represents level of confidence for each of the objects.
detection_scores = inference.get_model_detection_scores()
detection_classes = inference.get_model_detection_classes()
# Number of objects detected.
num_detections = inference.get_model_detected_objects()
camera = PiCamera()
camera.resolution = (IM_WIDTH, IM_HEIGHT)
camera.framerate = 10
rawCapture = PiRGBArray(camera, size=(IM_WIDTH, IM_HEIGHT))
rawCapture.truncate(0)
# Initialize frame rate calculation
collector = Collector()
frame_rate_calc = collector.frame_rate_calc
freq = collector.freq
font = collector.font
