def compute_batch_ap(image_ids):
APs = []
for image_id in image_ids:
# Load image
image, image_meta, gt_class_id, gt_bbox =\
data.load_image_gt(dataset, config, image_id)
# Run object detection
results = model.detect([image], verbose=0)
# Compute AP
r = results[0]
AP, precisions, recalls, overlaps = utils.compute_ap(
gt_bbox, gt_class_id, r['rois'], r['class_ids'], r['scores'])
APs.append(AP)
return APs
ax = get_ax(1)
r = results[0]
visualize.display_instances(image,
r['rois'],
r['class_ids'],
dataset.class_names,
r['scores'],
ax=ax,
title="Predictions")
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
#%% Precision-Recall
# Draw precision-recall curve
AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id,
r['rois'], r['class_ids'],
r['scores'])
visualize.plot_precision_recall(AP, precisions, recalls)
#%%
# Grid of ground truth objects and their predictions
visualize.plot_overlaps(gt_class_id, r['class_ids'], r['scores'], overlaps,
dataset.class_names)
#%% Compute mAP @ IoU=50 on Batch of Images
# Compute VOC-style Average Precision
def compute_batch_ap(image_ids):
APs = []
for image_id in image_ids:
# Load image
gt_bbox,
gt_class_id,
dataset_train.class_names,
ax=get_ax())
results = model.detect([original_image], verbose=1)
r = results[0]
visualize.display_instances(original_image,
r['rois'],
r['class_ids'],
dataset_val.class_names,
r['scores'],
ax=get_ax())
AP, precisions, recalls, overlaps =\
utils.compute_ap(gt_bbox, gt_class_id, r["rois"], r["class_ids"], r["scores"])
print(AP)
#%% Evaluation
# Compute VOC-Style mAP @ IoU=0.5
# Running on 10 images. Increase for better accuracy.
limit = 500
coco = dataset_val.load_coco(dataset_dir,
val_type,
config.IMAGE_MAX_DIM,
year=year,
return_coco=True,
auto_download=download)
dataset_val.prepare()
evaluate_coco(model, dataset_val, coco, "bbox", limit=limit)