def eval(train_file):
print('TRAIN')
gold_annotations = read_annotations('../data/TRAIN.annotations', return_sent=True)
pred_annotations = read_annotations(train_file, return_sent=True)
print(f'Entities extracted as entirety')
P, P_miss = get_precision(gold_annotations, pred_annotations, entirety=True)
R, R_miss = get_recall(gold_annotations, pred_annotations, entirety=True)
print(f'Precision: {P:.3f}')
print(f'Recall: {R:.3f}')
print(f'F1: {((2 * R * P) / (P + R)):.3f}')
print()
print(f'Entities extracted with "in" method')
P, P_miss = get_precision(gold_annotations, pred_annotations, entirety=False)
R, R_miss = get_recall(gold_annotations, pred_annotations, entirety=False)
print(f'Precision: {P:.3f}')
print(f'Recall: {R:.3f}')
print(f'F1: {((2 * R * P) / (P + R)):.3f}')
print(f'precision miss:')
for t in random.sample(P_miss, 5):
print(t)
print(get_dep_path(sent=nlp(t[3][2:-2]), per=t[1], org=t[2]))
print()
print(f'recall miss:')
for t in random.sample(R_miss, 5):
print(t)
print(get_dep_path(sent=nlp(t[3][2:-2]), per=t[1], org=t[2]))
def load_train_data(args, train_dir, valid_prop=0.10):
"""load training data and write to IO formatted training and validation files"""
vocab = set()
tfile = codecs.open(join(args.work_dir, TRAIN_FILE_NAME), 'w', 'utf-8')
vfile = codecs.open(join(args.work_dir, VALID_FILE_NAME), 'w', 'utf-8')
txt_files = [f for f in listdir(train_dir) if f.endswith(".txt")]
random.shuffle(txt_files)
num_val_files = int(len(txt_files) * valid_prop)
for findex, txt_file in enumerate(txt_files):
print("Reading", txt_file)
rfile = vfile if findex < num_val_files else tfile
doc_tokens, file_vocab = tokenize_document(join(train_dir, txt_file))
vocab = vocab.union(file_vocab)
annotations = read_annotations(join(train_dir, txt_file[:-3] + "ann"))
for token in doc_tokens:
ignore_token = False
for ann in annotations:
if token.start >= ann.start and token.end <= ann.end:
# Change this for IOB annotations
if ann.atype == LOC_ANN_TAG:
token.encoding = "I-LOC"
if ann.atype == PRO_ANN_TAG:
ignore_token = True
break
if not ignore_token:
print(token.text + "\t" + token.encoding, file=rfile)
tfile.close()
vfile.close()
return vocab
def replace_annotation_in_image(image, mask, new_path, bg_list, num_bg,
num_rotate, rotation_limit):
results = []
for i in range(num_rotate):
fg_cut, mask_cut, h_cut, w_cut, rot_path = rotate_image(
image, mask, rotation_limit, new_path)
# check if mask and img have the same size, otherwise retry
mh, mw = mask_cut.shape
if not (mh == h_cut and mw == w_cut):
print("fg_cut and mask_cut have different shapes! Skip.")
continue
for j in range(num_bg):
bg_file = bg_list[randint(0, len(bg_list) - 1)]
print("choose bg: " + bg_file)
bg_label_path = bg_file.replace("/images/", "/labels/").replace(
".jpg", ".txt")
bg = cv2.imread(bg_file, 1)
bg_box_list = utils.read_annotations(bg_label_path)
h, w, _ = bg.shape
bbox_rand = get_random_position_on_surface(w_cut, h_cut,
bg_box_list, w, h)
if not bbox_rand:
# todo: try again with smaller scaled object?
print("couldn't place annotation on new background")
else:
bg_img, bg_path = place_roi_on_bg(fg_cut, mask_cut, bg,
bbox_rand, rot_path, j)
norm_box = utils.abs_to_norm_bbox(bbox_rand)
result = (bg_img, bg_path, [norm_box])
results.append(result)
return results
def disambiguate(args):
'''Method for disambiguation'''
# load pubmed files
pub_files = [
f for f in listdir(args.dir)
if isfile(join(args.dir, f)) and f.endswith(".ann")
]
for _, pubfile in enumerate(pub_files):
entities = read_annotations(join(args.dir, pubfile))
pmid = pubfile.replace(".ann", "")
write_annotations(args.outdir, entities, pmid, True)
def fine_tune_yolo(debug=False):
video = Video("../datasets/AICity_data/train/S03/c010/frames")
detection_transform = DetectionTransform()
classes = utils.load_classes('../config/coco.names')
hyperparams = parse_model_config('../config/yolov3.cfg')[0]
learning_rate = float(hyperparams["learning_rate"])
momentum = float(hyperparams["momentum"])
decay = float(hyperparams["decay"])
burn_in = int(hyperparams["burn_in"])
model = Darknet('../config/yolov3.cfg')
print(model)
model.load_weights('../weights/yolov3.weights')
model.train()
for module_def, module in zip(model.module_defs, model.module_list):
if module_def["type"] == "yolo":
break
module.train(False)
if torch.cuda.is_available():
model = model.cuda()
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=1e-5)
gt = read_annotations(
'../datasets/AICity_data/train/S03/c010/m6-full_annotation.xml')
dataset = YoloDataset(video, gt, classes, transforms=detection_transform)
data_loader = DataLoader(dataset,
batch_size=16,
shuffle=True,
num_workers=4)
for epoch in tqdm(range(10), file=sys.stdout, desc='Fine tuning'):
for images, targets in tqdm(data_loader,
file=sys.stdout,
desc='Running epoch'):
if torch.cuda.is_available():
images = images.cuda()
targets = targets.cuda()
optimizer.zero_grad()
loss = model(images, targets)
loss.backward()
optimizer.step()
print('Training finished. Saving weights...')
model.save_weights('../weights/fine_tuned_yolo_freeze.weights')
print('Saved weights')
def get_y(file, df):
gold_annotations = read_annotations(file)
print(
f'{WORK_FOR} input annotations: {sum([len(annotations) for annotations in gold_annotations.values()])}'
)
y = np.zeros(df.shape[0])
for i, idx in enumerate(df.index):
sent_id, person, org, _, = idx
for ann in gold_annotations[sent_id]:
if (person in ann[0] or ann[0] in person) and (org in ann[2]
or ann[2] in org):
y[i] = 1
break
return y
def main():
start_frame = 1440
end_frame = 1789
gt = read_annotations('../annotations', start_frame, end_frame)
alg = 'mask_rcnn'
detections = read_detections('../datasets/AICity_data/train/S03/c010/det/det_{0}.txt'.format(alg))
kalman = KalmanTracking()
for i in range(start_frame, end_frame):
f = Frame(i)
f.detections = detections[i]
f.ground_truth = gt[i - start_frame]
kalman(f)
print(seq(f.detections).map(lambda d: d.id).to_list())
def create_siamese_dataset():
AICity_dataset_path = '../datasets/AICity_data/train/S03/c010/'
siamese_dataset_path = '../datasets/siamese_data/'
annotation_file = 'm6-full_annotation.xml'
frames = int(2140 * 0.25)
gt_detections = read_annotations(AICity_dataset_path + annotation_file,
frames)
for frame in range(frames + 1):
frame_name = "frame_{:04d}.jpg".format(frame + 1)
img = cv.imread(AICity_dataset_path + 'frames/' + frame_name)
for detection in gt_detections[frame]:
xtl, ytl = detection.top_left
w = detection.width
h = detection.height
class_path = str(detection.label) + '-' + str(detection.id) + '/'
if not os.path.exists(siamese_dataset_path + class_path):
os.mkdir(siamese_dataset_path + class_path)
cropped_img = img[ytl:ytl + h, xtl:xtl + w]
cv.imwrite(siamese_dataset_path + class_path + frame_name,
cropped_img)
def main():
demonstration_list = []
agents = read_annotations("annotations.txt")
grid = create_video_grid(VIDEO_WIDTH, VIDEO_HEIGHT, BLOCK_SIZE)
for agent in agents:
positions = get_agent_positions_in_grid(agent, grid)
step_list = get_steps_from_position_list(positions)
demonstration_list.append(step_list)
grid_width = len(grid[0])
grid_height = len(grid)
num_states = grid_width * grid_height
placeholder_mdp = GridWorldMDP(width = grid_width, height=grid_height, init_loc=(0,0),
goal_locs = [], walls = [], name = "placeholder")
planner = Planner(placeholder_mdp, sample_rate=5)
planner._compute_matrix_from_trans_func()
feature_map = np.eye(num_states)
reward_array = deep_maxent_irl(
feature_map, planner, demonstration_list, args.learning_rate, args.num_iterations)
reward_matrix = np.reshape(reward_array, (grid_height, grid_width))
np.save("reward_prior", reward_matrix)
def load_test_data(args, test_dir):
"""load test data and write to IO formatted file"""
vocab = set()
tfile = codecs.open(join(args.work_dir, "test-io.txt"), 'w', 'utf-8')
txt_files = [f for f in listdir(test_dir) if f.endswith(".txt")]
for _, txt_file in enumerate(txt_files):
print("Reading", txt_file)
doc_tokens, file_vocab = tokenize_document(join(test_dir, txt_file))
vocab = vocab.union(file_vocab)
annotations = read_annotations(join(test_dir, txt_file[:-3] + "ann"))
for token in doc_tokens:
ignore_token = False
for ann in annotations:
if token.start >= ann.start and token.end <= ann.end:
# Change this for IOB annotations
if ann.atype == LOC_ANN_TAG:
token.encoding = "I-LOC"
if ann.atype == PRO_ANN_TAG:
ignore_token = True
break
if not ignore_token:
print(token.text + "\t" + token.encoding, file=tfile)
tfile.close()
return vocab
def off_the_shelf_ssd(tracking, debug=False, **kwargs):
if cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
gt = read_annotations(
'../datasets/AICity_data/train/S03/c010/m6-full_annotation.xml')
video = Video("../datasets/AICity_data/train/S03/c010/frames")
trans = transforms.Compose(
[transforms.Resize((300, 300)),
transforms.ToTensor()])
labels = ( # always index 0
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
model = build_ssd('test', 300, 21) # initialize SSD
model.load_weights('../weights/ssd300_mAP_77.43_v2.pth')
if torch.cuda.is_available():
model = model.cuda()
frames = []
model.eval()
with torch.no_grad():
for i, im in enumerate(video.get_frames()):
im_tensor = trans(im)
im_tensor = im_tensor.view((-1, ) + im_tensor.size())
if torch.cuda.is_available():
im_tensor = im_tensor.cuda()
output = model.forward(im_tensor)
detections = output.data
w = im.width
h = im.height
frame = Frame(i)
frame.ground_truth = gt[frame.id]
# skip j = 0, because it's the background class
for j in (2, 6, 7, 14):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.size(0) == 0:
continue
boxes = dets[:, 1:]
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:,
np.newaxis])).astype(np.float32,
copy=False)
for cls_det in cls_dets:
x1 = int(w * cls_det[0])
y1 = int(h * cls_det[1])
det = Detection(-1,
labels[j - 1], (x1, y1),
width=w * (cls_det[2] - cls_det[0]),
height=h * (cls_det[3] - cls_det[1]),
confidence=cls_det[4])
frame.detections.append(det)
# kalman(frame)
if tracking is not None:
tracking(frame, frames, debug=debug)
frames.append(frame)
if debug:
plt.figure()
for det in frame.detections:
rect = patches.Rectangle(det.top_left,
det.width,
det.height,
linewidth=2,
edgecolor='blue',
facecolor='none')
plt.gca().add_patch(rect)
plt.text(det.top_left[0],
det.top_left[1],
s='{} ~ {}'.format(det.label, det.id),
color='white',
verticalalignment='top',
bbox={
'color': 'blue',
'pad': 0
})
plt.imshow(im)
plt.axis('off')
# plt.savefig('../video/video_ssd_KalmanID/frame_{:04d}'.format(i))
plt.show()
plt.close()
#iou_over_time(frames)
mAP = mean_average_precision(frames)
print("SSD mAP:", mAP)
#
# Alternatives
# Controller_object (Controller)
# Want_suspect (Want)
# Building_subparts (Building)
# Hedging
# Be_in_agreement_on_action (agreement)
# Disgraceful_situation (situation)
# Change_event_duration (event)
# Intentional_deception (deception)
ANNOTATIONS_PATH = 'annotations.csv'
STOPWORDS_PATH = 'stop_words_FULL.txt'
if __name__ == '__main__':
annotations = read_annotations(ANNOTATIONS_PATH)
stopw = load_stopwords(STOPWORDS_PATH)
res_rows = []
giuste = 0
for frame, word, target_synset in annotations:
# Se non è disponibile il mapping
if target_synset is None:
giuste += 1
continue
# Rimuovo il PoS dalla parola estratta dalle annotazioni
input_word = word.split('.')[0]
# disambiguo la parola input_word (quella centrale
# nelle annotazioni) usando il frame come contesto
def main(cwd, do_amgng, amgng_file, ma_window, ma_recalc_delay,
do_cla, cla_file, buffer_len, plot):
global mean_oa, mean_sem
values = inspect.getargvalues(inspect.currentframe())[3]
print('using parameters: {}'.format(values))
annotations_path = os.path.join(cwd, 'annotations.csv')
anndf = utils.read_annotations(annotations_path, ['Aux'])
amgng_df = None
if do_amgng:
from mgng.amgng import main as amgng_main
print('Training AMGNG model...')
out_file = os.path.join(cwd, 'out_amgng_{}'.format(amgng_file))
full_path = os.path.join(cwd, amgng_file)
start = datetime.now()
amgng_main(input_file=full_path, output_file=out_file,
buffer_len=buffer_len, index_col='timestamp',
skip_rows=[1,2], ma_window=ma_window,
ma_recalc_delay=ma_recalc_delay)
amgng_time = datetime.now() - start
amgng_df = set_annotations_and_plot(out_file, anndf,
'anomaly_density', plot)
amgng_df['AnnotationSpans'] = amgng_df.Annotation.copy()
utils.fill_annotations(amgng_df, 'Annotation', '.*OA.*',
spans_field='AnnotationSpans', method='pad',
mean=mean_oa, std=mean_sem)
amgng_df.to_csv(out_file)
print('Time taken: amgng={}'.format(amgng_time))
cla_df = None
if do_cla:
from cla.swarm import swarm
from cla.cla import create_model, open_input_file
from cla.cla import prepare_run, process_row
print('Training CLA model...')
full_path = os.path.join(cwd, cla_file)
out_file = os.path.join(cwd, 'out_cla_{}'.format(cla_file))
cla_model = {}
fields, csv_reader, input_handler = open_input_file(full_path)
start = datetime.now()
for p in fields:
swarm_desc = fill_swarm_description(full_path, buffer_len, p)
model_params = swarm(cwd=cwd, input_file=cla_file,
swarm_description=swarm_desc)
model = create_model(params=model_params, predictedField=p)
model_out_file = os.path.join(cwd, '{}_{}'.format(p, cla_file))
shifter, output_handler = prepare_run(fields=fields,
predicted_field=p,
plot=False,
output_name=model_out_file)
cla_model[p] = {'model': model, 'shifter': shifter,
'output_handler': output_handler,
'model_out_file': model_out_file}
swarm_time = datetime.now() - start
start = datetime.now()
for i, row in enumerate(csv_reader):
for p in fields:
process_row(row=row, fields=fields, predicted_field=p,
model=cla_model[p]['model'],
shifter=cla_model[p]['shifter'],
output_handler=cla_model[p]['output_handler'],
counter=i)
cla_time = datetime.now() - start
input_handler.close()
for i, p in enumerate(fields):
cla_model[p]['output_handler'].close()
df = pd.read_csv(cla_model[p]['model_out_file'], parse_dates=True,
index_col='timestamp')
if i == 0:
cla_df = df
else:
cla_df.anomaly_likelihood += df.anomaly_likelihood
cla_df.anomaly_likelihood /= len(fields)
cla_df.to_csv(out_file)
cla_df = set_annotations_and_plot(out_file, anndf,
'anomaly_likelihood', plot)
cla_df['AnnotationSpans'] = cla_df.Annotation.copy()
utils.fill_annotations(cla_df, 'Annotation', '.*OA.*',
spans_field='AnnotationSpans', method='pad',
mean=mean_oa, std=mean_sem)
cla_df.to_csv(out_file)
print('Time taken: swarm={}, cla={}'.format(swarm_time, cla_time))
return amgng_df, cla_df
def evaluate_detection(image_list,
test_dict,
graph_list,
label_map,
min_threshold=0.2,
ignore_labels=False,
do_recognition=False,
graph_r=None,
labels_r=None,
save_images=False,
logging_dir=None):
"""
Evaluate a list of detection graphs given a minimum threshold. Analyse results to find a good threshold.
Optionally use recognition graph to re-label results or ignore the labels completely (just compare bounding boxes).
:param image_list: list of images (label files exist)
:param graph_list: list of detection graph files
:param label_map: label map for detection
:param min_threshold: minimum threshold to filter detection results
:param ignore_labels: if true, only compare bounding boxes
:param do_recognition: if true, re-label detection results
:param graph_r: graph file for recognition
:param labels_r: label file for recognition
:return:
"""
detector = detect.Detector(detection_threshold=min_threshold)
if do_recognition:
recognizer = recognize.TensorflowRecognition()
recognizer.load_graph(graph_r, labels_r)
for graph in graph_list:
detector.load_graph(graph, label_map)
# evaluate this graph and determine a good threshold
rec_true = 0
rec_false = 0
detect_true = 0
detect_false = 0
detect_missing = 0
num_images = len(image_list)
sum_threshold = 0.0
for image in image_list:
print(image)
label_path = utils.get_label_path_by_image(image)
annotation_list = utils.read_annotations(label_path)
img = cv2.imread(image)
id_list, score_list, box_list = detector.detect(img)
detection_list = utils.detection_results_to_annotation_list(
id_list, score_list, box_list)
match_list = []
annotation_unmatched_list = list(range(len(annotation_list)))
for detection in detection_list:
best_match = -1
best_p = 0.0 # minimum likelihood for matching
label_match = False
for i in range(len(annotation_list)):
annotation = annotation_list[i]
p = match_bounding_boxes(detection.bbox, annotation.bbox)
if ignore_labels:
if p > best_p:
best_match = i
best_p = p
elif not do_recognition:
if p > best_p:
hyp_list = [(detection.label, detection.prob)]
likelihood, score = match_hypotheses(
1, hyp_list, annotation.label)
# todo: save threshold/score
if likelihood > 0:
label_match = True
else:
if p > best_p:
n = 3 # todo
hyp_list = [(detection.label, detection.prob)
] # todo: do recognition
likelihood, score = match_hypotheses(
n, hyp_list, annotation.label)
# todo: save threshold/score
if likelihood > 0:
label_match = True
if not best_match == -1:
sum_threshold = sum_threshold + detection.prob # get thresholds for correct(!) detections (only bbox)
match_list.append((best_match, best_p, label_match))
if save_images:
save_image(img, detection_list, annotation_list, match_list,
logging_dir)
for match in match_list:
if match[0] == -1:
detect_false = detect_false + 1
else:
detect_true = detect_true + 1
if match[0] in annotation_unmatched_list:
annotation_unmatched_list.remove(match[0])
if match[2]:
rec_true = rec_true + 1
else:
rec_false = rec_false + 1
for index in annotation_unmatched_list:
detect_missing = detect_missing + 1
detected = detect_true + detect_false
to_detect = detect_true + detect_missing
rec_total = rec_true + rec_false
detection_rate = float(detect_true) / float(to_detect)
correct_detected = float(detect_true) / float(detected)
recognition_rate = float(rec_true) / float(rec_total)
avg_threshold = float(sum_threshold) / float(detect_true)
print(
"Detected {} of all annotations.\n{} of detections were correct (annotated)."
.format(detection_rate, correct_detected))
if do_recognition:
print("\nRecognition rate: {}\n\n".format(recognition_rate))
print("average threshold of correct (!) detections: {}".format(
avg_threshold))
import sys
from utils import read_annotations
gold_annotations = read_annotations(sys.argv[1])
pred_annotations = read_annotations(sys.argv[2])
def get_precision(gold_annotations, pred_annotations, entirety=False):
TP, FP = 0, 0
for sent_id, annotations in pred_annotations.items():
for pred_ann in annotations:
per, _, org = pred_ann
T = False
for glod_ann in gold_annotations[sent_id]:
if entirety:
if per == glod_ann[0] and org == glod_ann[2]:
T = True
break
else:
if (per in glod_ann[0]
or glod_ann[0] in per) and (org in glod_ann[2]
or glod_ann[2] in org):
T = True
break
if T:
TP += 1
else:
FP += 1
return TP / (TP + FP)
def main():
video = Video("../datasets/AICity_data/train/S03/c010/vdo.avi")
gt = read_annotations('../annotations', start_frame, end_frame)
"""
DETECTIONS
"""
det_algs = ['yolo3', 'mask_rcnn', 'ssd512']
for alg in det_algs:
detections = read_detections(
'../datasets/AICity_data/train/S03/c010/det/det_{0}.txt'.format(
alg))
detections = detections[start_frame:end_frame + 1]
frames = []
# roi = cv2.imread('../datasets/AICity_data/train/S03/c010/roi.jpg')
for im, f in seq(video.get_frames(
start_frame_number=start_frame)).take(end_frame - start_frame +
1):
f.ground_truth = gt[f.id]
f.detections = detections[f.id]
frames.append(f)
if make_video:
make_video_frame(im, f, frames)
iou_over_time(frames)
mAP = mean_average_precision(frames)
print(alg, " mAP:", mAP)
"""
DETECTIONS FROM ALTERED GROUND TRUTH
"""
frames = []
for im, f in seq(video.get_frames()).take(end_frame - start_frame + 1):
f.ground_truth = gt[f.id]
f.detections = alter_detections(f.ground_truth)
frames.append(f)
if make_video:
make_video_frame(im, f, frames)
iou_over_time(frames)
mAP = mean_average_precision(frames)
print('Random alteration', " mAP:", mAP)
"""
OPTICAL FLOW
"""
of_det_1 = read_optical_flow(
'../datasets/optical_flow/detection/LKflow_000045_10.png')
of_det_2 = read_optical_flow(
'../datasets/optical_flow/detection/LKflow_000157_10.png')
of_gt_1 = read_optical_flow('../datasets/optical_flow/gt/000045_10.png')
of_gt_2 = read_optical_flow('../datasets/optical_flow/gt/000157_10.png')
img_1 = cv2.imread('../datasets/optical_flow/img/000045_10.png')
img_2 = cv2.imread('../datasets/optical_flow/img/000157_10.png')
msen_of = msen(of_det_2, of_gt_2)
pepn_of = pepn(of_det_2, of_gt_2)
print(msen_of, pepn_of)
show_optical_flow(of_gt_1)
show_optical_flow_arrows(img_1, of_gt_1)
msen_45 = msen(of_det_1, of_gt_1, plot=True)
pepn_45 = pepn(of_det_1, of_gt_1)
print("Sequence 045: MSEN", msen_45, "PEPN", pepn_45)
msen_157 = msen(of_det_2, of_gt_2, plot=True)
pepn_157 = pepn(of_det_2, of_gt_2)
print("Sequence 157: MSEN", msen_157, "PEPN", pepn_157)
show_optical_flow(of_gt_1)
def off_the_shelf_yolo(tracking, debug=False, *args, **kwargs):
video = Video("../datasets/AICity_data/train/S03/c010/frames")
detection_transform = DetectionTransform()
classes = utils.load_classes('../config/coco.names')
gt = read_annotations(
'../datasets/AICity_data/train/S03/c010/m6-full_annotation.xml')
model = Darknet('../config/yolov3.cfg')
model.load_weights('../weights/fine_tuned_yolo_freeze.weights')
if torch.cuda.is_available():
model = model.cuda()
frames = []
last_im = None
model.eval()
with torch.no_grad():
for i, im in tqdm(enumerate(video.get_frames(start=len(video) // 4)),
total=len(video),
file=sys.stdout,
desc='Yolo'):
im_tensor = detection_transform(im)
im_tensor = im_tensor.view((-1, ) + im_tensor.size())
if torch.cuda.is_available():
im_tensor = im_tensor.cuda()
detections = model.forward(im_tensor)
detections = utils.non_max_suppression(detections,
80,
conf_thres=.6,
nms_thres=0.3)
frame = Frame(i + (len(video) // 4))
frame.ground_truth = gt[frame.id]
for d in detections[0]:
if int(d[6]) in VALID_LABELS:
bbox = d.cpu().numpy()
det = Detection(-1,
classes[int(d[6])], (bbox[0], bbox[1]),
width=bbox[2] - bbox[0],
height=bbox[3] - bbox[1],
confidence=d[5])
detection_transform.unshrink_detection(det)
frame.detections.append(det)
if tracking is not None:
last_frame = None if len(frames) == 0 else frames[-1]
tracking(frame=frame,
im=im,
last_frame=last_frame,
last_im=last_im,
frames=frames,
debug=False)
frames.append(frame)
last_im = im
if debug:
plt.figure()
for det in frame.detections:
rect = patches.Rectangle(det.top_left,
det.width,
det.height,
linewidth=2,
edgecolor='blue',
facecolor='none')
plt.gca().add_patch(rect)
if tracking is None:
text = '{}'.format(det.label)
else:
text = '{} ~ {}'.format(det.label, det.id)
plt.text(det.top_left[0],
det.top_left[1],
s=text,
color='white',
verticalalignment='top',
bbox={
'color': 'blue',
'pad': 0
})
plt.imshow(im)
plt.axis('off')
# plt.savefig('../video/video_yolo_fine_tune_good/frame_{:04d}'.format(i))
plt.show()
plt.close()
# iou_over_time(frames)
mAP = mean_average_precision(frames)
print("YOLO mAP:", mAP)
def main(cwd, do_amgng, amgng_file, ma_window, ma_recalc_delay,
do_cla, cla_file, buffer_len, plot):
values = inspect.getargvalues(inspect.currentframe())[3]
print('using parameters: {}'.format(values))
annotations_path = os.path.join(cwd, 'annotations.csv')
anndf = utils.read_annotations(annotations_path, ['Type'], 20000)
amgng_df = None
if do_amgng:
from mgng.amgng import main as amgng_main
print('Training AMGNG model...')
out_file = os.path.join(cwd, 'out_amgng_{}'.format(amgng_file))
full_path = os.path.join(cwd, amgng_file)
start = datetime.now()
amgng_main(input_file=full_path, output_file=out_file,
buffer_len=buffer_len, index_col='timestamp',
skip_rows=[1,2], ma_window=ma_window,
ma_recalc_delay=ma_recalc_delay)
amgng_time = datetime.now() - start
print('Reading results...')
amgng_df = pd.read_csv(out_file, parse_dates=True,
index_col='timestamp')
amgng_df['Annotation'] = anndf.Type
print('Writing annotated results...')
amgng_df.to_csv(out_file)
if plot:
utils.plot_results(amgng_df, ['ECG1'], 'anomaly_score',
'anomaly_density', '[rs]')
print('Time taken: amgng={}'.format(amgng_time))
cla_df = None
if do_cla:
from cla.swarm import swarm
from cla.cla import main as cla_main
out_file = os.path.join(cwd, 'out_cla_{}'.format(cla_file))
print('Training CLA model...')
full_path = os.path.join(cwd, cla_file)
SWARM_DESCRIPTION = {
'includedFields': [
{
'fieldName': 'timestamp',
'fieldType': 'datetime',
},
{
'fieldName': 'ECG1',
'fieldType': 'float',
},
],
'streamDef': {
'info': 'chfdbchf13 ECG1',
'version': 1,
'streams': [
{
'info': 'chfdbchf13',
'source': full_path,
'columns': ['*']
}
]
},
'inferenceType': 'TemporalAnomaly',
'inferenceArgs': {
'predictionSteps': [1],
'predictedField': 'ECG1'
},
'iterationCount': buffer_len,
'swarmSize': 'small'
}
start = datetime.now()
swarm(cwd=cwd, input_file=cla_file,
swarm_description=SWARM_DESCRIPTION)
swarm_time = datetime.now() - start
start = datetime.now()
cla_main(cwd=cwd, input_file=full_path, output_name=out_file, plot=False,
predicted_field='ECG1')
cla_time = datetime.now() - start
print('Reading results...')
cla_df = pd.read_csv(out_file, parse_dates=True, index_col='timestamp')
cla_df['Annotation'] = anndf.Type
print('Writing annotated results...')
cla_df.to_csv(out_file)
if plot:
utils.plot_results(cla_df, ['ECG1'], 'anomaly_score',
'anomaly_likelihood', '[rs]')
print('Time taken: swarm={}, cla={}'.format(swarm_time, cla_time))
return amgng_df, cla_df
def multiply_dataset(input_dir,
output_dir,
bg_dir,
num_rotate=1,
num_illuminate=1,
num_scale=1,
num_blur=1,
num_bg=1):
print("input: " + input_dir)
print("output: " + output_dir)
bg_list = []
use_bg = False
if bg_dir is not None and not bg_dir == "" and num_bg > 0:
print("try to read background data set")
for dirname, dirnames, filenames in os.walk(bg_dir):
for filename in filenames:
file = dirname + '/' + filename
if file.endswith(".jpg"):
l_file = file.replace("/images/",
"/labels/").replace(".jpg", ".txt")
if not (os.path.isfile(l_file)):
print(
"error: Surface label file does not exist! Skipping image."
)
continue
bg_list.append("{}".format(file))
print("found {} backgrounds".format(len(bg_list)))
if len(bg_list) > 0:
use_bg = True
else:
print("background change disabled")
print("multiply image (light, scale, blur, rotate): {}x{}x{}x{}x{} times".
format(num_illuminate, num_scale, num_blur, num_rotate, num_bg))
rotation_limit = 80
for dirname, dirnames, filenames in os.walk(input_dir):
for filename in filenames:
file_path = dirname + '/' + filename
if not os.path.exists(dirname.replace(
input_dir, output_dir)): # creates dir path
os.makedirs(dirname.replace(input_dir, output_dir))
roi_path = dirname.replace(input_dir, output_dir).replace(
"/images", "/rois").replace("/labels", "/rois")
if not os.path.exists(roi_path):
os.makedirs(roi_path)
# deals with all None-Image files
if imghdr.what(file_path) is None:
if ".jpg" in filename or ".png" in filename: # ignore empty images
continue
if dirname.endswith(
"/labels"
): # ignores the label files, they will be written later
continue
#if "train.txt" in filename or "test.txt" in filename: # leaves the files empty to fill it later
# train_txt = open(file_path.replace(input_dir, output_dir).replace("test.txt", "train.txt"), 'a+')
else: # copy the files without changing
old_file = open(file_path, 'r')
new_file = open(file_path.replace(input_dir, output_dir),
'w+')
new_file.write(old_file.read())
old_file.close() # close the streams
new_file.close()
continue
if "mask." in file_path: # ignore masks
continue
if "/rois" in file_path: # ignore generated rois
continue
label_path = file_path.replace("/images/", "/labels/").replace(
".jpg", ".txt").replace(".png", ".txt")
mask_path = file_path.replace(".jpg", "_mask.jpg").replace(
".png", "_mask.png")
if not os.path.isfile(label_path): # skip images with no labels
continue
has_mask = False
if os.path.isfile(mask_path): # check mask exists
has_mask = True
print(file_path)
image = cv2.imread(file_path)
mask = cv2.imread(mask_path, 0)
annotation_list = utils.read_annotations(label_path)
new_path = file_path.replace(input_dir, output_dir)
results = []
if has_mask and use_bg:
results = replace_annotation_in_image(image, mask, new_path,
bg_list, num_bg,
num_rotate,
rotation_limit)
bbox_list = []
for a in annotation_list:
bbox = copy.deepcopy(a.bbox)
bbox_list.append(bbox)
original = (image, new_path, bbox_list)
results.append(original)
for res in results:
if not len(annotation_list) == len(res[2]):
print(
"ERROR: There are {} annotations, but only {} bounding boxes were changed. "
+ "Skip.".format(len(annotation_list), len(res[2])))
continue
change_whole_image(res[0], res[1], annotation_list, res[2],
num_illuminate, num_scale, num_blur)
