(yolo_outputs,
              image_shape,
              max_boxes=10,
              score_threshold=.6,
              iou_threshold=.5)
return boxes, scores, classes
Evaluate YOLO model on given input batch and return filtered boxes.
Evaluate YOLO model on given input batch and return filtered boxes.
Evaluate YOLO model on given input batch and return filtered boxes .
def yolo_eval(yolo_outputs,
              image_shape,
              max_boxes=10,
              score_threshold=.6,
              iou_threshold=.5):
    """Evaluate YOLO model on given input batch and return filtered boxes."""
    box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs
    boxes = yolo_boxes_to_corners(box_xy, box_wh)
    boxes, scores, classes = yolo_filter_boxes(
        box_confidence, boxes, box_class_probs, threshold=score_threshold)
    
    # Scale boxes back to original image shape.
    height = image_shape[0]
    width = image_shape[1]
    image_dims = K.stack([height, width, height, width])
    image_dims = K.reshape(image_dims, [1, 4])
    boxes = boxes * image_dims

    # TODO: Something must be done about this ugly hack!
    max_boxes_tensor = K.variable(max_boxes, dtype='int32')
    K.get_session().run(tf.variables_initializer([max_boxes_tensor]))
    nms_index = tf.image.non_max_suppression(
        boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold)
    boxes = K.gather(boxes, nms_index)
    scores = K.gather(scores, nms_index)
    classes = K.gather(classes, nms_index)
    
    return boxes, scores, classes
def yolo_eval ( yolo_outputs , image_shape , max_boxes = 10 , score_threshold = .6 , iou_threshold = .5 ) : box_confidence , box_xy , box_wh , box_class_probs = yolo_outputs boxes = yolo_boxes_to_corners ( box_xy , box_wh ) boxes , scores , classes = yolo_filter_boxes ( box_confidence , boxes , box_class_probs , threshold = score_threshold ) # Scale boxes back to original image shape. height = image_shape [ 0 ] width = image_shape [ 1 ] image_dims = K . stack ( [ height , width , height , width ] ) image_dims = K . reshape ( image_dims , [ 1 , 4 ] ) boxes = boxes * image_dims # TODO: Something must be done about this ugly hack! max_boxes_tensor = K . variable ( max_boxes , dtype = 'int32' ) K . get_session ( ) . run ( tf . variables_initializer ( [ max_boxes_tensor ] ) ) nms_index = tf . image . non_max_suppression ( boxes , scores , max_boxes_tensor , iou_threshold = iou_threshold ) boxes = K . gather ( boxes , nms_index ) scores = K . gather ( scores , nms_index ) classes = K . gather ( classes , nms_index ) return boxes , scores , classes