image_id = random.choice(dataset.image_ids)
print(image_id)
image, image_meta, gt_class_id, gt_bbox, gt_mask = modellib.load_image_gt(dataset,
config, image_id)
info = dataset.image_info[image_id]
print("image ID: {}.{} ({}) {}".format(info["source"], info["id"], image_id,
dataset.image_reference(image_id)))
print("Original image shape: ", modellib.parse_image_meta(image_meta[np.newaxis,...])
["original_image_shape"][0])
# Run object detection
results = model.detect_molded(np.expand_dims(image, 0), np.expand_dims(image_meta, 0),
verbose=1)
# Display results
r = results[0]
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
log("gt_mask", gt_mask)
ax = get_ax(1)
# Compute AP over range 0.5 to 0.95 and print it
utils.compute_ap_range(gt_bbox, gt_class_id, gt_mask,
r['rois'], r['class_ids'], r['scores'], r['masks'],
verbose=1)
visualize.display_differences(
image,
gt_bbox, gt_class_id, gt_mask,
r['rois'], r['class_ids'], r['scores'], r['masks'],
dataset.class_names, ax=ax,
show_box=False, show_mask=False,
iou_threshold=0.5, score_threshold=0.5)
'''
200
image ID: nodule.279 (200) 279
Original image shape: [512 512 3]
Processing 1 images
image shape: (1024, 1024, 3) min: 1.00000 max: 251.00000 uint8
molded_images shape: (1, 1024, 1024, 3) min: 1.00000 max: 251.00000 uint8
image_metas shape: (1, 14) min: 0.00000 max: 1024.00000 float64
anchors shape: (1, 261888, 4) min: -0.08847 max: 1.02591 float32
gt_class_id shape: (1,) min: 1.00000 max: 1.00000 int32
gt_bbox shape: (1, 4) min: 576.00000 max: 730.00000 int32
gt_mask shape: (56, 56, 1) min: 0.00000 max: 1.00000 bool
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-32-cdafc2e3f205> in <module>
19 utils.compute_ap_range(gt_bbox, gt_class_id, gt_mask,
20 r['rois'], r['class_ids'], r['scores'], r['masks'],
---> 21 verbose=1)
22 # #print(gt_mask.shape,r['masks'].shape)
23 # #import cv2
c:\Users\User\AppData\Local\Programs\Python\Python37\lib\site-packages\mask_rcnn-2.1-py3.7.egg\mrcnn\utils.py in compute_ap_range(gt_box, gt_class_id, gt_mask, pred_box, pred_class_id, pred_score, pred_mask, iou_thresholds, verbose)
765 compute_ap(gt_box, gt_class_id, gt_mask,
766 pred_box, pred_class_id, pred_score, pred_mask,
--> 767 iou_threshold=iou_threshold)
768 if verbose:
769 print("AP @{:.2f}:\t {:.3f}".format(iou_threshold, ap))
c:\Users\User\AppData\Local\Programs\Python\Python37\lib\site-packages\mask_rcnn-2.1-py3.7.egg\mrcnn\utils.py in compute_ap(gt_boxes, gt_class_ids, gt_masks, pred_boxes, pred_class_ids, pred_scores, pred_masks, iou_threshold)
728 gt_boxes, gt_class_ids, gt_masks,
729 pred_boxes, pred_class_ids, pred_scores, pred_masks,
--> 730 iou_threshold)
731
732 # Compute precision and recall at each prediction box step
''' how can i solve this error c:\Users\User\AppData\Local\Programs\Python\Python37\lib\site-packages\mask_rcnn-2.1-py3.7.egg\mrcnn\utils.py in compute_matches(gt_boxes, gt_class_ids, gt_masks, pred_boxes, pred_class_ids, pred_scores, pred_masks, iou_threshold, score_threshold) 680 ... 117 overlaps = intersections / union
<array_function internals> in dot(*args, **kwargs)
ValueError: shapes (5,1048576) and (3136,1) not aligned: 1048576 (dim 1) != 3136 (dim 0)
