# Run inference
predicted_bbox, predicted_labels, predicted_scores = run_inference(detr, np.expand_dims(model_input, axis=0), config)
frame = frame.astype(np.float32)
frame = frame / 255
frame = numpy_bbox_to_image(frame, predicted_bbox, labels=predicted_labels, scores=predicted_scores, class_name=COCO_CLASS_NAME)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
physical_devices = tf.config.list_physical_devices('GPU')
if len(physical_devices) == 1:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
config = TrainingConfig()
args = training_config_parser().parse_args()
config.update_from_args(args)
# Load the model with the new layers to finetune
detr = get_detr_model(config, include_top=True, weights="detr")
config.background_class = 91
# RUn webcam inference
run_webcam_inference(detr)
t_class = tf.squeeze(t_class, axis=-1)
# Compute map
cal_map(p_bbox, p_labels, p_scores, np.zeros((138, 138, len(p_bbox))), np.array(t_bbox), np.array(t_class), np.zeros((138, 138, len(t_bbox))), ap_data, iou_thresholds)
print(f"Computing map.....{it}", end="\r")
it += 1
#if it > 10:
# break
# Compute the mAp over all thresholds
calc_map(ap_data, iou_thresholds, class_names, print_result=True)
if __name__ == "__main__":
physical_devices = tf.config.list_physical_devices('GPU')
if len(physical_devices) == 1:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
config = TrainingConfig()
args = training_config_parser().parse_args()
config.update_from_args(args)
# Load the model with the new layers to finetune
detr = build_model(config)
valid_dt, class_names = load_coco_dataset(config, 1, augmentation=None)
# Run training
eval_model(detr, config, class_names, valid_dt)