def test_detect_interesting_points(self):
image_file = data_dir + "/test/karyotype.bmp"
image = image_utils.read_image(image_file)
chromosomes = Pipeline.extract_chromosomes(image)
straightened_chromosomes = Pipeline.straighten_chromosomes(chromosomes)
_ = Pipeline.detect_interesting_points(straightened_chromosomes,
verbose=True)
def single_box_in_single_frame_tracking(frame_files, frame_id, box_id,
annotations, tracking_threshold,
forward_tracker, backward_tracker,
offset, low, high):
num_frame = len(frame_files)
box = [int(x) for x in annotations[frame_id][box_id]["bbox"]]
category_id = annotations[frame_id][box_id]["category_id"]
boxes = list()
# Forward tracking
is_first_frame = True
for tracking_frame_id in range(
frame_id,
min(num_frame + offset,
frame_id + high)): # cut off by range specified by "high"
# print(f"===> Process frame {tracking_frame_id} w.r.t file {frame_files[tracking_frame_id]}")
frame = image_utils.read_image(frame_files[tracking_frame_id - offset])
if is_first_frame:
forward_tracker.init(frame, box)
is_first_frame = False
continue # TODO: Is this necessary?
outputs = forward_tracker.track(frame)
tracking_score = outputs["best_score"]
# print("===> Tracking score: ", tracking_score)
if tracking_score < tracking_threshold:
# print("===> Break")
break
bbox = list(map(int, outputs['bbox']))
# draw_frame = frame.copy()
# draw_frame = cv2.rectangle(draw_frame, (bbox[0], bbox[1]), (bbox[0] + bbox[2], bbox[1] + bbox[3]),
# (0, 255, 0), 3)
# image_utils.show_images([draw_frame])
boxes.append({
"bbox": bbox,
"score": float(tracking_score),
"category_id": category_id,
"image_id": tracking_frame_id
})
# Backward tracking
is_first_frame = True
for tracking_frame_id in reversed(
range(max(offset, frame_id - low),
frame_id + 1)): # cut off by range specified by "low"
# print(f"==> Process frame {tracking_frame_id} w.r.t file {frame_files[tracking_frame_id]}")
frame = image_utils.read_image(frame_files[tracking_frame_id - offset])
if is_first_frame:
backward_tracker.init(frame, box)
is_first_frame = False
continue # TODO: Is this necessary?
outputs = backward_tracker.track(frame)
tracking_score = outputs["best_score"]
# print("===> Tracking score: ", tracking_score)
if tracking_score < tracking_threshold:
# print("===> Break")
break
bbox = list(map(int, outputs['bbox']))
# draw_frame = frame.copy()
# draw_frame = cv2.rectangle(draw_frame, (bbox[0], bbox[1]), (bbox[0] + bbox[2], bbox[1] + bbox[3]),
# (0, 255, 0), 3)
# image_utils.show_images([draw_frame])
boxes.append({
"bbox": bbox,
"score": float(tracking_score),
"category_id": category_id,
"image_id": tracking_frame_id
})
return boxes
def test_straighten_chromosomes(self):
image_file = data_dir + "/test/karyotype.bmp"
image = image_utils.read_image(image_file)
chromosomes = Pipeline.extract_chromosomes(image)
_ = Pipeline.straighten_chromosomes(chromosomes, debug=True)
def test_extract_chromosomes(self):
image_file = data_dir + "/test/karyotype.bmp"
image = image_utils.read_image(image_file)
chromosomes = Pipeline.extract_chromosomes(image)
for chromosome in chromosomes:
image_utils.show_image(chromosome, cmap=None)
def test_generate_chromosome_cluster(self):
image_file = data_dir + "/test/karyotype.bmp"
image = image_utils.read_image(image_file)
image_utils.show_image(image)
chromosome_cluster = Pipeline.generate_chromosome_cluster(image)
image_utils.show_image(chromosome_cluster, cmap=None)
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(description='tracking demo')
parser.add_argument('--raw_dir', type=str, help='path to frame files')
parser.add_argument('--tracking_dir', type=str, help='path to tracking_json')
parser.add_argument('--result_dir',
type=str,
help='path to save visualized results')
args = parser.parse_args()
raw_detection = general_utils.get_all_files(args.raw_dir,
keep_dir=True,
sort=True)
tracking_detection = general_utils.get_all_files(args.tracking_dir,
keep_dir=True,
sort=True)
general_utils.create_directory(args.result_dir)
num_frame = len(raw_detection)
for i in tqdm(range(num_frame)):
raw = image_utils.read_image(raw_detection[i])
tracking = image_utils.read_image(tracking_detection[i])
combined = np.concatenate([raw, tracking], axis=1)
cv2.imwrite(f"{args.result_dir}/frame_{'{:06d}'.format(i + 1)}.jpg",
combined)