def collisions_in_scene(file_path, temp_path="./temp.txt"):
# to be called in notebook with dataframe describe for further analysis
helpers.extract_frames(file_path, temp_path, save=True)
nb_collisions_total = []
nb_objects_total = []
ious_total = []
ious_conflict_total = []
conflicts_total = {}
with open(temp_path) as frames:
for frame in frames:
frame = json.loads(frame)
# print(frame["0"]["bbox"])
conflicts, nb_collisions, frame_id, nb_objects, ious, ious_conflict = collisions_in_frame(
frame, threshold=0.5)
nb_collisions_total.append(nb_collisions)
nb_objects_total.append(nb_objects)
for iou in ious:
ious_total.append(iou)
for iou in ious_conflict:
ious_conflict_total.append(iou)
conflicts_total[frame_id] = conflicts
os.remove(temp_path)
return conflicts_total, nb_collisions_total, nb_objects_total, ious_total, ious_conflict_total
def extract_data(self, scene):
helpers.extract_frames(self.original_file.format(scene),
self.frames_temp,
save=True)
helpers.extract_trajectories(self.original_file.format(scene),
self.trajectories_temp,
save=True)
sample_id = 0
helpers.remove_file(self.samples_dest.format(scene))
helpers.remove_file(self.labels_dest.format(scene))
with open(self.samples_dest.format(scene), "a") as data_csv:
data_writer = csv.writer(data_csv)
with open(self.labels_dest.format(scene), "a") as label_csv:
label_writer = csv.writer(label_csv)
# with open(parameters["scene_path"],"a") as scene_csv:
# scene_writer = csv.writer(scene_csv)
with open(self.trajectories_temp) as trajectories:
with open(self.frames_temp) as file_frames:
for k, trajectory in enumerate(trajectories):
trajectory = json.loads(trajectory)
scene_name = trajectory["scene"]
file_frames, a = tee(file_frames)
frames = trajectory["frames"]
current_id = int(trajectory["id"])
# if current_id == 63:
# print("63")
continuous = self.__are_frames_continuous(frames)
if continuous:
start, stop = frames[0], frames[-1] + 1
ids = self.__get_neighbors(
islice(a, start, stop))
len_traj = len(ids[current_id])
for i in range(0, len_traj, self.shift):
features, labels = self.__features_labels(
len_traj, current_id, ids, i)
if features != []:
sample_id = self.__persist_data(
ids, current_id, i, features,
labels, data_writer, label_writer,
sample_id, scene_name)
# if sample_id == 39:
# print("")
else:
print("trajectory {} discarded".format(
current_id))
os.remove(self.frames_temp)
os.remove(self.trajectories_temp)
def main():
args = sys.argv
data = json.load(open(args[1]))
prepare = json.load(open(args[2]))
scene_list = prepare["selected_scenes"]
scene_path = data["preprocessed_datasets"] + "{}.csv"
frames_temp = data["temp"] + "frames.txt"
nb_agents_scenes = []
for scene in scene_list:
helpers.extract_frames(scene_path.format(scene),
frames_temp,
save=True)
nb_agents_scene = []
with open(frames_temp) as frames:
for i, frame in enumerate(frames):
# print(frame["ids"])
frame = json.loads(frame)
nb_agents = len(frame["ids"].keys())
nb_agents_scene.append(nb_agents)
nb_agents_scenes.append(nb_agents)
# if nb_agents == 205:
# print(scene)
# print(i)
print("{}: mean {}, std {}".format(scene, np.mean(nb_agents_scene),
np.std(nb_agents_scene)))
print(" max {}, min {}, nb_frames {}, nb_agents".format(
np.max(nb_agents_scene), np.min(nb_agents_scene), i + 1,
np.sum(nb_agents_scene)))
helpers.remove_file(frames_temp)
print(" mean {}, std {}".format(np.mean(nb_agents_scenes),
np.std(nb_agents_scenes)))
print(" max {}, min {}".format(np.max(nb_agents_scenes),
np.min(nb_agents_scenes)))
def change_rate(self, scene_name):
former_rate = float(self.framerates_json[scene_name])
rate_ratio = int(former_rate / self.new_rate)
# self.destination_file = self.destination_file.format(scene_name)
helpers.remove_file(self.destination_file.format(scene_name))
self.counter = 0
helpers.extract_frames(self.original_file.format(scene_name),
self.temp,
save=True)
with open(self.temp) as frames:
for frame in frames:
frame = json.loads(frame)
i = frame["frame"]
if i % rate_ratio == 0:
self.__write_frame(frame["ids"], scene_name)
self.counter += 1
helpers.remove_file(self.temp)
def extract_data(self, scene):
helpers.extract_frames(self.original_file.format(scene),
self.frames_temp,
save=True)
helpers.remove_file(self.samples_dest.format(scene))
helpers.remove_file(self.labels_dest.format(scene))
with open(self.samples_dest.format(scene), "a") as data_csv:
data_writer = csv.writer(data_csv)
with open(self.labels_dest.format(scene), "a") as label_csv:
label_writer = csv.writer(label_csv)
with open(self.frames_temp) as frames:
observations = {}
sample_id = 0
for frame in frames:
delete_ids = []
observations[sample_id] = []
sample_id += 1
for id_ in observations:
if len(observations[id_]
) < self.t_obs + self.t_pred:
observations[id_].append(frame)
else:
features, labels, ids = self.__features_labels(
observations[id_])
self.__persist_data(ids, features, labels,
data_writer, label_writer,
id_)
delete_ids.append(id_)
for id_ in delete_ids:
del observations[id_]
helpers.remove_file(self.frames_temp)
def main():
# file_path = CSV + "new_rates/gates8_30.0to2.5.csv"
# file_path = CSV + "bad.csv"
# file_path = CSV + "lankershim_inter2_10.0to1.0.csv"
# file_path = CSV + "deathCircle1.csv"
# file_path = CSV + "01_tracks.csv"
args = sys.argv
data = json.load(open(args[1]))
original = int(args[3])
file_path = ""
if original:
file_path = data["extracted_datasets"] + args[2] + ".csv"
else:
file_path = data["preprocessed_datasets"] + args[2] + ".csv"
framerate = 1
# factor_div = 3/2
plot_last_step = True
nb_last_steps = 200
new_color_index = 0
color_dict = {}
temp_path = "./data/temp/temp.txt"
target_size = 1000
margin = 10
offset = [0,0]
helpers.extract_frames(file_path,temp_path,save = True)
try:
min_,max_ = vis.find_bounding_coordinates(temp_path)
w,h = vis.get_scene_image_size(min_,max_)
print(min_,max_)
print(w,h)
factor_div = np.max([w,h]) / target_size
w,h = int(w/factor_div) + margin,int(h/factor_div)+margin
offset = np.divide(min_,-factor_div)
print(factor_div)
print(offset)
print(w,h)
# Create a black image
img = np.zeros((h,w,3), np.uint8)
last_frames = deque([])
with open(temp_path) as frames:
for frame in frames:
frame = json.loads(frame)
# print(frame)
if len(last_frames) == nb_last_steps:
last_frames.popleft()
last_frames.append(frame)
img1 = img.copy()
img1,color_dict,new_color_index = vis.plot_current_frame(frame,img1,color_dict,new_color_index,factor_div,offset, display_box= True)
if plot_last_step:
img1 = vis.plot_last_steps(img1,frame,last_frames,color_dict,factor_div=factor_div,offset = offset)
img1 = vis.plot_frame_number(w,h, img1,frame)
# if file_path == CSV + "bad.csv":
img1 = vis.plot_scene_name(w,h, img1,frame)
cv2.imshow('image1',img1)
cv2.waitKey(int(1000/framerate))
cv2.destroyAllWindows()
os.remove(temp_path)
except KeyboardInterrupt:
cv2.destroyAllWindows()
os.remove(temp_path)