def run(generator, args):
# display images, one at a time
for i in range(generator.size()):
# load the data
image = generator.load_image(i)
annotations, masks = generator.load_annotations(i)
# apply random transformations
if args.random_transform:
image, annotations, masks = generator.random_transform_group_entry(
image, annotations, masks)
# resize the image and annotations
if args.resize:
image, image_scale = generator.resize_image(image)
annotations[:, :4] *= image_scale
for m in range(len(masks)):
masks[m], _ = generator.resize_image(masks[m])
# draw anchors on the image
if args.anchors:
anchors = generator.generate_anchors(image.shape)
labels, _, _ = generator.compute_anchor_targets(
anchors, [image], [annotations], generator.num_classes())
draw_boxes(image,
anchors[np.max(labels[0], axis=1) == 1], (255, 255, 0),
thickness=1)
# draw annotations on the image
if args.annotations:
# draw annotations in red
draw_annotations(image,
annotations,
color=(0, 0, 255),
label_to_name=generator.label_to_name)
# draw regressed anchors in green to override most red annotations
# result is that annotations without anchors are red, with anchors are green
anchors = generator.generate_anchors(image.shape)
labels, _, boxes = generator.compute_anchor_targets(
anchors, [image], [annotations], generator.num_classes())
draw_boxes(image, boxes[0, np.max(labels[0], axis=1) == 1],
(0, 255, 0))
# Draw masks over the image with random colours
if args.masks:
for m in range(len(masks)):
# crop the mask with the related bbox size, and then draw them
box = annotations[m, :4].astype(int)
mask = masks[m][box[1]:box[3], box[0]:box[2]]
draw_mask(image, box, mask, annotations[m, 4].astype(int))
# add the label caption
caption = '{}'.format(
generator.label_to_name(annotations[m, 4]))
draw_caption(image, box, caption)
cv2.imshow('Image', image)
if cv2.waitKey() == ord('q'):
return False
return True
def run(generator, args):
""" Main loop.
Args
generator: The generator to debug.
args: parseargs args object.
"""
# display images, one at a time
# for i in np.random.choice(generator.size(), 200):
for i in range(generator.size()):
# load the data
image = generator.load_image(i)
annotations = generator.load_annotations(i)
# apply random transformations
if args.random_transform:
image, annotations = generator.random_transform_group_entry(
image, annotations)
# resize the image and annotations
if args.resize:
image, image_scale = generator.resize_image(image)
annotations[:, :4] *= image_scale
image = image[..., image.shape[-2] // 2, 0]
image = (image - image.min()) / image.max() * 255.0
image = np.repeat(image.reshape((512, 512, 1)), 3, axis=2) # to rgb
image = image.astype(np.uint8).copy()
anchors = anchors_for_shape(image.shape)
labels_batch, regression_batch, boxes_batch = generator.compute_anchor_targets(
anchors, [image], [annotations], generator.num_classes())
anchor_states = labels_batch[0, :, -1]
# draw anchors on the image
if args.anchors:
draw_boxes(image,
boxes_batch[0, anchor_states == 1, :], (0, 255, 0),
thickness=1)
# draw annotations on the image
if args.annotations:
# draw annotations in red
draw_annotations(image,
annotations,
color=(0, 0, 255),
label_to_name=generator.label_to_name)
# draw regressed anchors in green to override most red annotations
# result is that annotations without anchors are red, with anchors are green
draw_boxes(image, boxes_batch[0, anchor_states == 1, :],
(0, 255, 0))
# cv2.imshow('Image', image)
cv2.imwrite(os.path.join('debug', '{}.png'.format(i)), image)
# if cv2.waitKey() == ord('q'):
# return False
return True
def run(generator, args):
""" Main loop.
Args
generator: The generator to debug.
args: parseargs args object.
"""
# display images, one at a time
for i in range(generator.size()):
# load the data
image = generator.load_image(i)
annotations = generator.load_annotations(i)
# apply random transformations
if args.random_transform:
image, annotations = generator.random_transform_group_entry(
image, annotations)
# resize the image and annotations
if args.resize:
image, image_scale = generator.resize_image(image)
annotations[:, :4] *= image_scale
anchors = anchors_for_shape(image.shape)
labels_batch, regression_batch, boxes_batch = generator.compute_anchor_targets(
anchors, [image], [annotations], generator.num_classes())
anchor_states = labels_batch[0, :, -1]
# draw anchors on the image
if args.anchors:
draw_boxes(image,
anchors[anchor_states == 1], (255, 255, 0),
thickness=1)
# draw annotations on the image
if args.annotations:
# draw annotations in red
draw_annotations(image,
annotations,
color=(0, 0, 255),
label_to_name=generator.label_to_name)
# draw regressed anchors in green to override most red annotations
# result is that annotations without anchors are red, with anchors are green
draw_boxes(image, boxes_batch[0, anchor_states == 1, :],
(0, 255, 0))
cv2.imshow('Image', image)
if cv2.waitKey() == ord('q'):
return False
return True
def run(generator, args, anchor_params):
# display images, one at a time
for i in range(generator.size()):
# load the data
image = generator.load_image(i)
annotations = generator.load_annotations(i)
# apply random transformations
if args.random_transform:
image, annotations = generator.random_transform_group_entry(image, annotations)
# resize the image and annotations
if args.resize:
image, image_scale = generator.resize_image(image)
annotations['bboxes'] *= image_scale
for m in range(len(annotations['masks'])):
annotations['masks'][m], _ = generator.resize_image(annotations['masks'][m])
anchors = anchors_for_shape(image.shape, anchor_params=anchor_params)
positive_indices, _, max_indices = compute_gt_annotations(anchors, annotations['bboxes'])
# draw anchors on the image
if args.anchors:
anchors = generator.generate_anchors(image.shape)
positive_indices, _, max_indices = compute_gt_annotations(anchors, annotations['bboxes'])
draw_boxes(image, anchors[positive_indices], (255, 255, 0), thickness=1)
# draw annotations on the image
if args.annotations:
# draw annotations in red
draw_annotations(image, annotations, color=(0, 0, 255), label_to_name=generator.label_to_name)
# draw regressed anchors in green to override most red annotations
# result is that annotations without anchors are red, with anchors are green
draw_boxes(image, annotations['bboxes'][max_indices[positive_indices], :], (0, 255, 0))
# Draw masks over the image with random colours
if args.masks:
for m in range(len(annotations['masks'])):
# crop the mask with the related bbox size, and then draw them
box = annotations['bboxes'][m].astype(int)
mask = annotations['masks'][m][box[1]:box[3], box[0]:box[2]]
draw_mask(image, box, mask, annotations['labels'][m].astype(int))
# add the label caption
caption = '{}'.format(generator.label_to_name(annotations['labels'][m]))
draw_caption(image, box, caption)
cv2.imshow('Image', image)
if cv2.waitKey() == ord('q'):
return False
return True
def _get_detections(generator,
model,
score_threshold=0.05,
max_detections=300,
save_path=None,
experiment=None,
postprocess=False):
""" Get the detections from the model using the generator.
The result is a list of lists such that the size is:
all_detections[num_images][num_classes] = detections[num_detections, 4 + num_classes]
# Arguments
generator : The generator used to run images through the model.
model : The model to run on the images.
score_threshold : The score confidence threshold to use.
max_detections : The maximum number of detections to use per image.
save_path : The path to save the images with visualized detections to.
experiment : Comet ML experiment
# Returns
A list of lists containing the detections for each image in the generator.
"""
all_detections = [[None for i in range(generator.num_classes())]
for j in range(generator.size())]
for i in range(generator.size()):
raw_image = generator.load_image(i)
plot_image = copy.deepcopy(raw_image)
#Format name and save
image_name = generator.image_names[i]
row = generator.image_data[image_name]
lfname = os.path.splitext(row["tile"])[0] + "_" + str(
row["window"]) + "raw_image"
#Skip if missing a component data source
if raw_image is False:
print("Empty image, skipping")
continue
#Store plotting images
plot_rgb = plot_image[:, :, :3].copy()
#predict
image = generator.preprocess_image(raw_image)
image, scale = generator.resize_image(image)
if keras.backend.image_data_format() == 'channels_first':
image = image.transpose((2, 0, 1))
# run network
boxes, scores, labels = model.predict_on_batch(
np.expand_dims(image, axis=0))[:3]
# correct boxes for image scale
boxes /= scale
# select indices which have a score above the threshold
indices = np.where(scores[0, :] > score_threshold)[0]
# select those scores
scores = scores[0][indices]
# find the order with which to sort the scores
scores_sort = np.argsort(-scores)[:max_detections]
# select detections
image_boxes = boxes[0, indices[scores_sort], :]
image_scores = scores[scores_sort]
image_labels = labels[0, indices[scores_sort]]
image_detections = np.concatenate([
image_boxes,
np.expand_dims(image_scores, axis=1),
np.expand_dims(image_labels, axis=1)
],
axis=1)
#name image
image_name = generator.image_names[i]
row = generator.image_data[image_name]
fname = os.path.splitext(row["tile"])[0] + "_" + str(row["window"])
#drape boxes if they exist
if len(image_boxes) > 0:
#get lidar cloud if a new tile, or if not the same tile as previous image.
if generator.with_lidar:
if i == 0:
generator.load_lidar_tile()
elif not generator.image_data[i][
"tile"] == generator.image_data[i - 1]["tile"]:
generator.load_lidar_tile()
#The tile could be the full tile, so let's check just the 400 pixel crop we are interested
#Not the best structure, but the on-the-fly generator always has 0 bounds
if hasattr(generator, 'hf'):
bounds = generator.hf["utm_coords"][generator.row["window"]]
else:
bounds = []
if save_path is not None:
draw_annotations(plot_rgb,
generator.load_annotations(i),
label_to_name=generator.label_to_name)
draw_detections(plot_rgb,
image_boxes,
image_scores,
image_labels,
label_to_name=generator.label_to_name,
score_threshold=score_threshold)
#name image
image_name = generator.image_names[i]
row = generator.image_data[image_name]
fname = os.path.splitext(row["tile"])[0] + "_" + str(row["window"])
#Write RGB
cv2.imwrite(os.path.join(save_path, '{}.png'.format(fname)),
plot_rgb)
if experiment:
experiment.log_image(os.path.join(save_path,
'{}.png'.format(fname)),
name=fname)
# copy detections to all_detections
for label in range(generator.num_classes()):
all_detections[i][label] = image_detections[
image_detections[:, -1] == label, :-1]
return all_detections
def _get_detections(generator,
model,
score_threshold=0.05,
max_detections=300,
save_path=None,
experiment=None,
postprocess=True):
""" Get the detections from the model using the generator.
The result is a list of lists such that the size is:
all_detections[num_images][num_classes] = detections[num_detections, 4 + num_classes]
# Arguments
generator : The generator used to run images through the model.
model : The model to run on the images.
score_threshold : The score confidence threshold to use.
max_detections : The maximum number of detections to use per image.
save_path : The path to save the images with visualized detections to.
experiment : Comet ML experiment
# Returns
A list of lists containing the detections for each image in the generator.
"""
all_detections = [[None for i in range(generator.num_classes())]
for j in range(generator.size())]
for i in range(generator.size()):
raw_image = generator.load_image(i)
plot_image = generator.retrieve_window()
plot_image = copy.deepcopy(plot_image[:, :, ::-1])
#Skip if missing a component data source
if raw_image is False:
print("Empty image, skipping")
continue
#predict
image = generator.preprocess_image(raw_image)
image, scale = generator.resize_image(image)
if keras.backend.image_data_format() == 'channels_first':
image = image.transpose((2, 0, 1))
# run network
boxes, scores, labels = model.predict_on_batch(
np.expand_dims(image, axis=0))[:3]
# correct boxes for image scale
boxes /= scale
# select indices which have a score above the threshold
indices = np.where(scores[0, :] > score_threshold)[0]
# select those scores
scores = scores[0][indices]
# find the order with which to sort the scores
scores_sort = np.argsort(-scores)[:max_detections]
# select detections
image_boxes = boxes[0, indices[scores_sort], :]
image_scores = scores[scores_sort]
image_labels = labels[0, indices[scores_sort]]
image_detections = np.concatenate([
image_boxes,
np.expand_dims(image_scores, axis=1),
np.expand_dims(image_labels, axis=1)
],
axis=1)
#name image
image_name = generator.image_names[i]
row = generator.image_data[image_name]
fname = os.path.splitext(row["tile"])[0] + "_" + str(row["window"])
if save_path is not None:
draw_annotations(plot_image,
generator.load_annotations(i),
label_to_name=generator.label_to_name)
draw_detections(plot_image,
image_boxes,
image_scores,
image_labels,
label_to_name=generator.label_to_name,
score_threshold=score_threshold)
#name image
image_name = generator.image_names[i]
row = generator.image_data[image_name]
fname = os.path.splitext(row["tile"])[0] + "_" + str(row["window"])
#Write RGB
cv2.imwrite(os.path.join(save_path, '{}.png'.format(fname)),
plot_image)
if experiment:
experiment.log_image(os.path.join(save_path,
'{}.png'.format(fname)),
name=fname)
# copy detections to all_detections
for label in range(generator.num_classes()):
all_detections[i][label] = image_detections[
image_detections[:, -1] == label, :-1]
return all_detections