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cvat2slowfast.py
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cvat2slowfast.py
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import os
import sys
import json
from lxml import etree
from collections import OrderedDict
import pandas as pd
from natsort import natsorted
import cv2
if __name__ == "__main__":
if len(sys.argv) != 3:
print("python cvat2slowfast.py path_to_mini_scenes")
exit(0)
elif len(sys.argv) == 3:
path_to_mini_scenes = sys.argv[1]
path_to_new_dataset = sys.argv[2]
label2number = {"Walk": 0,
"Graze": 1,
"Browse": 2,
"Head Up": 3,
"Auto-Groom": 4,
"Trot": 5,
"Run": 6,
"Occluded": 7}
number2label = {value: key for key, value in label2number.items()}
old2new = {"Walk": "Walk",
"Walking": "Walk",
"Graze": "Graze",
"Browsing": "Browse",
"Head Up": "Head Up",
"Auto-Groom": "Auto-Groom",
"Mutual Grooming": "Mutual Grooming",
"Trotting": "Trot",
"Running": "Run",
"Drinking": "Drinking",
"Herding": "Herding",
"Lying Down": "Lying Down",
"Mounting-Mating": "Mounting-Mating",
"Sniff": "Sniff",
"Urinating": "Urinating",
"Defecating": "Defecating",
"Dusting": "Dusting",
"Fighting": "Fighting",
"Chasing": "Chasing",
"Occluded": "Occluded",
"Out of Focus": "Out of Focus",
"Out of Frame": "Out of Frame",
None: None}
if not os.path.exists(path_to_new_dataset):
os.makedirs(path_to_new_dataset)
if not os.path.exists(f"{path_to_new_dataset}/annotation"):
os.makedirs(f"{path_to_new_dataset}/annotation")
if not os.path.exists(f"{path_to_new_dataset}/dataset/image"):
os.makedirs(f"{path_to_new_dataset}/dataset/image")
with open(f"{path_to_new_dataset}/annotation/classes.json", "w") as file:
json.dump(label2number, file)
headers = {"original_vido_id": [], "video_id": pd.Series(dtype="int"), "frame_id": pd.Series(dtype="int"),
"path": [], "labels": []}
charades_df = pd.DataFrame(data=headers)
video_id = 1
folder_name = 1
flag = False
for i, folder in enumerate(natsorted(os.listdir(path_to_mini_scenes))):
if os.path.exists(f"{path_to_mini_scenes}/{folder}/actions"):
for j, file in enumerate(natsorted(os.listdir(f"{path_to_mini_scenes}/{folder}/actions"))):
if os.path.splitext(file)[1] == ".xml":
annotation_file = f"{path_to_mini_scenes}/{folder}/actions/{file}"
video_file = f"{path_to_mini_scenes}/{folder}/{os.path.splitext(file)[0]}.mp4"
if not os.path.exists(video_file):
print(f"{video_file} does not exist.")
continue
root = etree.parse(annotation_file).getroot()
try:
label = next(root.iterfind("track")).attrib["label"]
except StopIteration:
print(f"SKIPPED: {folder}/actions/{file}, EMPTY ANNOTATION")
continue
annotated = OrderedDict()
for track in root.iterfind("track"):
for entry in track.iter("points"):
frame_id = entry.attrib["frame"]
outside = entry.attrib["outside"]
if outside == "1":
continue
behavior = "".join(entry.find("attribute").itertext())
if annotated.get(frame_id) is None:
annotated[frame_id] = OrderedDict()
annotated[frame_id] = behavior
counter = 0
for value in annotated.values():
if old2new[value] in label2number.keys():
counter += 1
if counter < 90:
print(f"SKIPPED: {folder}/actions/{file}, length={counter}<90")
continue
folder_code = f"{label[0].capitalize()}{folder_name:04d}"
folder_name += 1
output_folder = f"{path_to_new_dataset}/dataset/image/{folder_code}"
progress = f"{i + 1}/{len(os.listdir(path_to_mini_scenes))}," \
f"{j + 1}/{len(os.listdir(f'{path_to_mini_scenes}/{folder}/actions'))}:" \
f"{folder}/actions/{file} -> {output_folder}"
print(progress)
sys.stdout.flush()
index = 0
adjusted_index = 1
vc = cv2.VideoCapture(video_file)
size = int(vc.get(cv2.CAP_PROP_FRAME_COUNT))
while vc.isOpened():
if flag is False:
if index < size:
returned = True
frame = None
else:
returned = False
frame = None
else:
returned, frame = vc.read()
if returned:
if not os.path.exists(output_folder):
os.makedirs(output_folder)
behavior = annotated.get(str(index))
behavior = old2new[behavior]
if behavior in label2number.keys():
if flag:
cv2.imwrite(f"{output_folder}/{adjusted_index}.jpg", frame)
# TODO: Major slow down here. Add to a list rather than dataframe,
# and create dataframe at the end.
charades_df.loc[len(charades_df.index)] = [f"{folder_code}",
video_id,
adjusted_index,
f"{folder_code}/{adjusted_index}.jpg",
str(label2number[behavior])]
adjusted_index += 1
index += 1
else:
break
vc.release()
video_id += 1
if video_id % 10 == 0:
charades_df.to_csv(f"{path_to_new_dataset}/annotation/data.csv", sep=" ", index=False)
charades_df.to_csv(f"{path_to_new_dataset}/annotation/data.csv", sep=" ", index=False)