def detect_and_save(video: Video, start=0, end=None, detector=None):
"""
Create a file containing object detection results of frames inside in_frame_folder
:param video: Object of Video class
:param start: Starting Frame Number (default: 1)
:param end: Ending Frame Number (default: last frame)
:param detector: Detector to use for object detection
"""
in_frame_folder = os.path.abspath(video.frames_path)
out_file_name = os.path.abspath(video.detection_path)
# print(in_frame_folder, out_file_name)
print(detector)
pickler = BATRPickle(out_file=out_file_name)
sorted_frames_filenames = sorted(os.listdir(in_frame_folder))
for frame_n, frame_filename in enumerate(sorted_frames_filenames,
start=start):
frame_filename = os.path.join(in_frame_folder, frame_filename)
detected_objects = detector.detect(frame_filename)
pickler.pickle(detected_objects, f"frame{frame_n:06d}")
# print(f"Frame # {frame_n}")
if frame_n == end:
break
del pickler
def improved_fill_missing_detection(video: Video):
in_detection_result_file = os.path.abspath(video.detection_path)
pickler = BATRPickle(in_file=in_detection_result_file)
tracking_path = os.path.join(os.path.dirname(in_detection_result_file),
"improved_detections.tar.gz")
output_pickler = BATRPickle(out_file=tracking_path)
sorted_frames_abs_filenames = sorted([
os.path.join(video.frames_path, filename)
for filename in os.listdir(video.frames_path)
])
last_serial = 0
previously_detected_objects = []
for frame_n, frame_filename in enumerate(sorted_frames_abs_filenames,
start=0):
# print(f"Frame # {frame_n}\n{'-' * 10}")
frame = cv2.imread(frame_filename)
detected_objects = pickler.unpickle(f"frame{frame_n:06d}")
# Find Out which object is Tracked and which is not
for obj in detected_objects:
set_tracker(obj, frame)
nn_index, nn_distance = nearest_neighbor(
obj, previously_detected_objects)
if nn_distance > 0.5 and not math.isinf(nn_distance):
previously_detected_objects[
nn_index].tracked_in_next_frame = True
obj.serial = previously_detected_objects[nn_index].serial
else:
# This is new object
obj.serial = last_serial
last_serial += 1
# Seperate objects that are not tracked
missed_obj = [
obj for obj in previously_detected_objects
if not hasattr(obj, "tracked_in_next_frame")
]
# Track these missed object in current frame
for obj in missed_obj:
nn_index, nn_distance = nearest_neighbor(obj, detected_objects)
nn_index = nn_index
# Double Check. May be it is already being tracked.
if not math.isinf(nn_index):
intersect = intersection(obj.bbox(),
detected_objects[nn_index].bbox())
if obj.w * obj.h == 0 or intersect / (obj.w * obj.h) > 0.7:
continue
# cv2.rectangle(frame, obj.pt_a(), obj.pt_b(), COLOR['blue'], 6)
#
# if nn_distance > 0.5 and not math.isinf(nn_distance):
# continue
ok, bbox = obj.tracker.update(frame)
bbox = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
# Found in current frame by tracker
if ok:
lost_and_found = DetectedObject(obj.type,
obj.probability,
_x=bbox[0],
_y=bbox[1],
_w=bbox[2],
_h=bbox[3])
lost_and_found.correct_bbox(frame)
lost_and_found.tracker = obj.tracker
lost_and_found.serial = obj.serial
detected_objects.append(lost_and_found)
previously_detected_objects = list(detected_objects)
# Display Rectangle around objects and checking whether their
# bbox are correct
detected_objects_without_trackers = []
for obj in detected_objects:
cv2.rectangle(frame, obj.pt_a(), obj.pt_b(), Color.GREEN.value, 4)
write_text(frame, f"{obj.serial}", (obj.cx(), obj.cy()))
detected_objects_without_trackers.append(
DetectedObject(obj.type, obj.probability, obj.x, obj.y, obj.w,
obj.h))
for obj in detected_objects_without_trackers:
object_bbox_check(frame, obj)
output_pickler.pickle(detected_objects_without_trackers,
f"frame{frame_n:06d}")
cv2.imwrite(f"output/frame{frame_n:06d}.jpg", frame)