def predict_and_plot_differences(dataset, img_id):
original_image, image_meta, gt_class_id, gt_box, gt_mask =\
modellib.load_image_gt(dataset, config,
img_id, use_mini_mask=False)
results = model.detect([original_image], verbose=0)
r = results[0]
visualize.display_differences(
original_image,
gt_box, gt_class_id, gt_mask,
r['rois'], r['class_ids'], r['scores'], r['masks'],
class_names = ['tumor'], title="", ax=get_ax(),
show_mask=True, show_box=True)
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=get_ax(),
show_box=False,
show_mask=False,
iou_threshold=0.5,
score_threshold=0.5)
# Display predictions only
# visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
# dataset.class_names, r['scores'], ax=get_ax(1),
# show_bbox=False, show_mask=False,
# title="Predictions")
# Display Ground Truth only
# visualize.display_instances(image, gt_bbox, gt_mask, gt_class_id,
def process( self, args ):
"""
Run inference with trained model
"""
# load validation / test dataset
test_ds = ShipDataset( (768,768) )
test_ds.load_info( os.path.join( args.data_path, 'test' ) )
test_ds.prepare()
# Load weights
print("Loading weights ", args.model_pathname)
self._model.load_weights(args.model_pathname, by_name=True)
for sample in range ( 10 ):
# pick random image
image_id = random.choice( test_ds.image_ids )
image, image_meta, gt_class_id, gt_bbox, gt_mask = modellib.load_image_gt( test_ds,
self._config,
image_id,
use_mini_mask=False )
# get image info
info = test_ds.image_info[ image_id ]
print("image ID: {}.{} ({}) {}".format( info["source"],
info["id"],
image_id,
test_ds.image_reference(image_id) ) )
# run object detection
results = self._model.detect([image], verbose=1)
r = results[0]
# 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)
# display results
visualize.display_instances( image,
r['rois'],
r['masks'],
r['class_ids'],
test_ds.class_names,
r['scores'],
title="Predictions",
show_bbox=False)
# display actual vs predicted differences
visualize.display_differences( image,
gt_bbox,
gt_class_id,
gt_mask,
r['rois'],
r['class_ids'],
r['scores'],
r['masks'],
test_ds.class_names,
title="Actual vs Predict Difference" )
return
modellib.load_image_gt(dataset, inference_config,
image_id, use_mini_mask=False)
print(dataset.image_info[image_id])
log("original_image", original_image)
log("image_meta", image_meta)
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
log("gt_mask", gt_mask)
results = model.detect([original_image], verbose=0)
r = results[0]
#visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id,
# dataset.class_names, figsize=(8, 8))
visualize.display_differences(original_image,
gt_bbox, gt_class_id, gt_mask,
r["rois"], r["class_ids"], r["scores"], r['masks'],
dataset.class_names, show_box=False)
# Compute VOC-Style mAP @ IoU=0.5
# Running on 10 images. Increase for better accuracy.
#image_ids = np.random.choice(dataset.image_ids, 1000)
#print(image_ids)
#print(dataset.image_ids)
APs = []
precision = {}
precision[1] = []
# Recreate the model in inference mode
model = modellib.MaskRCNN(mode="inference",
config=inference_config,
model_dir=MODEL_DIR)
model.load_weights(COCO_MODEL_PATH, by_name=True)
PIC_NAME = "temporary_dataset/"
dataset_root_path = os.path.join(ROOT_DIR, PIC_NAME)
img_floder = os.path.join(dataset_root_path, "pic")
mask_floder = os.path.join(dataset_root_path, "cv2_mask")
imglist = os.listdir(img_floder)
gt_num = len(imglist)
# Validation dataset
dataset_test = DrugDataset()
dataset_test.load_shapes(gt_num, img_floder, mask_floder, imglist,
dataset_root_path)
dataset_test.prepare()
for i in range(gt_num):
original_image, image_meta, gt_class_id, gt_bbox, gt_mask = modellib.load_image_gt(
dataset_test, inference_config, i, use_mini_mask=False)
results = model.detect([original_image], verbose=0)
r = results[0]
visualize.display_differences(imglist[i], original_image, gt_bbox,
np.asarray([1]), gt_mask, r['rois'],
np.asarray([1]), r['scores'], r['masks'],
class_names)
